diff --git a/.github/workflows/python-formating.yml b/.github/workflows/python-formating.yml new file mode 100644 index 0000000..e1444f2 --- /dev/null +++ b/.github/workflows/python-formating.yml @@ -0,0 +1,35 @@ +name: "Lint, Format and Auto Commit - Python" +on: + push: + branches: [main, develop] + paths: + - 'src/**/*.py' + pull_request: + branches: [main, develop] + paths: + - 'src/**/*.py' + +permissions: + contents: write + id-token: write + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v4 + + - name: Install ruff + run: pip install ruff + + - name: Lint and Format + run: | + ruff check --fix + ruff format + + - name: Auto Commit + uses: stefanzweifel/git-auto-commit-action@v4 + with: + commit_message: "Code formatted with ruff" \ No newline at end of file diff --git a/.ruff.toml b/.ruff.toml new file mode 100644 index 0000000..5b14ed6 --- /dev/null +++ b/.ruff.toml @@ -0,0 +1,77 @@ +# Exclude a variety of commonly ignored directories. +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".git-rewrite", + ".hg", + ".ipynb_checkpoints", + ".mypy_cache", + ".nox", + ".pants.d", + ".pyenv", + ".pytest_cache", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + ".vscode", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "site-packages", + "venv", +] + +# Same as Black. +line-length = 88 +indent-width = 4 + +# Assume Python 3.9 +target-version = "py39" + +[lint] +# Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`) codes by default. +# Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or +# McCabe complexity (`C901`) by default. +select = ["E4", "E7", "E9", "F"] +ignore = [] + +# Allow fix for all enabled rules (when `--fix`) is provided. +fixable = ["ALL"] +unfixable = [] + +# Allow unused variables when underscore-prefixed. +dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" + +[format] +# Like Black, use double quotes for strings. +quote-style = "double" + +# Like Black, indent with spaces, rather than tabs. +indent-style = "space" + +# Like Black, respect magic trailing commas. +skip-magic-trailing-comma = false + +# Like Black, automatically detect the appropriate line ending. +line-ending = "auto" + +# Enable auto-formatting of code examples in docstrings. Markdown, +# reStructuredText code/literal blocks and doctests are all supported. +# +# This is currently disabled by default, but it is planned for this +# to be opt-out in the future. +docstring-code-format = false + +# Set the line length limit used when formatting code snippets in +# docstrings. +# +# This only has an effect when the `docstring-code-format` setting is +# enabled. +docstring-code-line-length = "dynamic" \ No newline at end of file diff --git a/README.md b/README.md index 866dc71..4317088 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,613 @@ -# mixture-of-rags +# small-language-model-rags-is-all-you-need -A code repository for the project called mixture-of-rags which is an implementation of the concept mixture-of-agents(MoA) but for the RAG usecase +This is a repository that contains the code for the experiments conducted for the project called "Small Language Model RAGs is all you need". The project aims to showcase the capabilities of building RAG systems on top of smaller language models like Gemma2 9B, Llama 3 8B, Mistral 7B, and others. The idea of the project is how we can leverage the capabilities of smaller language models with some smart prompt engineering and some interesting inovative ideas to have results that are comparable or in some cases better than RAG systems built on top of larger language models like GPT-4o, Claude 3.5 Sonnet, and others. +For the experiments we used two different approaches or with other words types of RAG systems: +- **Simple RAG** - The classic RAG system pipeline +- **Mixture of RAG** (Mixture RAG) - RAG system pipeline inspired by the implementation of Mixture of Agents (MoA) + +## Libraries used in this project: +- [Langfuse](https://www.langfuse.com/) +- [Langchain](https://www.langchain.com/) +- [Ragas](https://docs.ragas.io/en/stable/) +- [Pydantic](https://pydantic-docs.helpmanual.io/) + +## LLM providers: +- [AWS Bedrock](https://aws.amazon.com/bedrock/) +- [Groq](https://www.groq.com/) +- [OpenAI](https://www.openai.com/) + +## Requirements: +- [Python](https://www.python.org/downloads/) +- [pip](https://pip.pypa.io/en/stable/installation/) +- [Docker & Docker Compose](https://docs.docker.com/get-docker/) +- [AWS Account](https://aws.amazon.com/) +- [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-quickstart.html) +- [AWS SSO account login](https://docs.aws.amazon.com/singlesignon/latest/userguide/getting-started.html) +- [OpenAI API key](https://platform.openai.com/docs/guides/authentication) +- [Groq API key](https://www.groq.com/) + +## Environment Variables: +Create a `.env` file in the root directory of the project and add the following environment variables: +```bash +BEDROCK_REGION_NAME = 'BEDROCK_REGION_NAME' +BEDROCK_CREDENTIALS_PROFILE_NAME = 'BEDROCK_CREDENTIALS_PROFILE_NAME' +OPENAI_API_KEY = 'OPENAI_API_KEY' +GROQ_API_KEY = 'GROQ_API_KEY' +LANGFUSE_SECRET_KEY = 'SECRET_KEY' +LANGFUSE_PUBLIC_KEY = 'PUBLIC_KEY' +LANGFUSE_HOST = 'HOST' +LANGFUSE_SCORE_URL = 'HOST_SCORE_URL' +``` +The environment variables are used to authenticate the AWS Bedrock, OpenAI, Groq, and Langfuse APIs. + +## Configuration for experiments + +We have two different types of RAG system pipelines which were used for the experiments: +- **Simple RAG** - The classic RAG system pipeline +- **Mixture of RAG (Mixture RAG)** - RAG system pipeline inspired by the implementation of Mixture of Agents (MoA) + +The example configuration for the Simple RAG (can be found in `config/simple.rag.example.yaml`) looks like: +```yaml +experiment_name: "simple_rag" + +llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: "Act like a Machine Learning Teacher" + user_message: "Ask a question or provide a prompt" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: True + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data/raw/" + +vector_store: + path: "./data/database_1500_100" +``` + +The example configuration for the Mixture RAG (can be found in `config/mixture.rag.example.yaml`) looks like: +```yaml +experiment_name: "mixture_rag" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 500 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: "Act like a Machine Learning Expert" + user_message: "Ask a question or provide a prompt" + - llm: + provider: "groq" + model_spec: + model_name: "mixtral-8x7b-32768" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: "Act like a Machine Learning Beginner" + user_message: "Ask a question or provide a prompt" + - llm: + provider: "groq" + model_spec: + model_name: "mixtral-8x7b-32768" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: "Act like a Machine Learning Teacher" + user_message: "Ask a question or provide a prompt" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "openai" + model_spec: + model: "gpt-4o" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You have been provided with a set of responses from various open-source models to the latest user query. + Your task is to synthesize these responses into a single, high-quality response. + It is crucial to critically evaluate the information provided in these responses, recognizing that some of it may be biased or incorrect. + Your response should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply to the instruction. + Ensure your response is well-structured, coherent, and adheres to the highest standards of accuracy and reliability. + user_message: "Ask a question or provide a prompt" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 2 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" +``` + +The validation of the configuration files is done with Pydantic (more info about it can be seen in the configuration models `src/configuration/configuration_model.py`) + +The configuration files of a Simple RAG pipeline must have the following structure: +``` +- experiment_name: str +- llm: dict + - provider: str + - model_spec: dict + - prompt: dict + - system_message: str + - user_message: str +- embedder: dict + - model_id: str + - model_kwargs: dict +- retriever: dict + - search_type: str + - retriever_kwargs: dict +- chunker: dict + - chunk_size: int + - chunk_overlap: int +- data: dict + path: str +- vector_store: dict + path: str (for additional validation the path name must include the chunk_size and chunk_overlap) +``` + +The configuration files of a Mixture RAG pipeline must have the following structure: +``` +- experiment_name: str +- layers: list (it has to have two layers one the rag llms and the other the aggregator llm) + - layer_type: str + - layer_spec: list + - llm: dict + - provider: str + - model_spec: dict + - prompt: dict + - system_message: str + - user_message: str +- embedder: dict + - model_id: str + - model_kwargs: dict +- retriever: dict + - search_type: str + - retriever_kwargs: dict +- chunker: dict + - chunk_size: int + - chunk_overlap: int +- data: dict + path: str +- vector_store: dict + path: str (for additional validation the path name must include the chunk_size and chunk_overlap) +``` + +The model_spec is different for each provider: +``` +- Bedrock + - model_id: str + - model_kwargs: dict +- Groq + - model_name: str + - temperature: float + - max_tokens: int +- OpenAI + - model: str + - temperature: float + - max_tokens: int +``` + + +## How to run the experiments: + +1. Clone the repository +2. Install the required Python packages with the following command: +```bash +pip install -r requirements.txt +``` +3. Create a `.env` file in the root directory of the project and add the environment variables(see the Environment Variables section) +4. Create a configuration file for the experiment you want to run(see the Configuration for experiments section) and put in the `src/configuration` folder +5. Setup the Langfuse Server: + - 4a. Use their Cloud Service hosted Langfuse + - 4b. Use the Docker Compose file to run the Langfuse Server locally +6. Modify the `execute_pipeline.py` file with the configuration file you want to run and the prompt templates you want to use. +7. Run the `execute_pipeline.py` file with the following command: +```bash +python execute_pipeline.py +``` +8. Generate the results by running the `extract_results.py` file with the following command: +```bash +python extract_results.py +``` +9. The results will be saved in the `results` folder and you can analyze them. + + +## Experiments + +In total there were 28 experiments both with Simple RAG and Mixture RAG pipelines. + +### Dataset + +The dataset used for the experiments is a collection of research papers in the field of Natural Language Processing (NLP), to be exact in the field of Large Language Models (LLMs). The dataset consists of 14 most cited papers in the field of NLP and LLMs. Questions from these papers were used as the evaluation dataset for the experiments. + +### Experimental Pipeline Setup + +All the experimental pipelines share these common components: +- **Chunker**: The dataset is chunked into smaller parts to be used for the experiments. The chunk size is 1500 and the chunk overlap is 100. +- **Embedder**: The Amazon Titan Embed Text model is used to embed the chunks of the dataset, with 512 vector dimensions. +- **Vector Store**: The embedded vectors are stored in a FAISS vector database for faster retrieval. +- **Retriever**: The retrieval of the most similar chunks is done using the FAISS vector database. The number of similar chunks retrieved is 5 and the search type is similarity. + +The experimental pipelines differ in the LLMs used and the way of the LLMs are used/combined. + +The LLM used in the pipelines are: +- gemma2-9b-it +- gemma-7b-it +- mistral-7b-instruct +- mixtral-8x7b-instruct +- llama-3-8b-instruct +- llama-3.1-8b-instruct +- llama-3-70b-instruct +- llama-3.1-70b-instruct +- llama-3.1-405b-instruct +- claude-3-haiku +- claude-3-sonnet +- claude-3-opus +- claude-3-5-sonnet +- gpt-4o +- gpt-4o-mini +- gpt-4-turbo + +Each of the LLMs have specific instruction prompt templates that are used for the experiments. Those templates can be found on: +- [Prompts Engineering Guide](https://www.promptingguide.ai/) +- [Ollama](https://ollama.ai/) +- [Anthropic Prompt Engineering](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) + +All of the prompts used in the experiments are stored in the `src/constants/prompts.py` file. + +#### Simple RAG Pipeline + +The Simple RAG pipeline uses a single LLM to generate the responses. This is how the Simple RAG looks: + +![image](https://github.com/user-attachments/assets/ee34da3d-6be8-4b92-9943-36abedfc575a) + +For all the experiments the system and the user messages are the same: + +``` +system_message: + "You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate." + +user_message: "Please answer my question based on the provided context:" +``` + +#### Mixture RAG Pipeline + +The Mixture RAG pipeline mostly is like the Simple RAG pipeline but in the Generator we basically trigger multiple LLMs(Simple RAGs with the same prompt system and user messages previsoly defined) to generate the responses and those response are the aggregated by another LLM. This is how the Mixture RAG looks: + +![image](https://github.com/user-attachments/assets/649467b8-bafa-4d85-831a-dab052314662) + + +There are three different system and user messages combinations used for the experiments, for the aggregation LLM: + +- One combination is really similar to the one used in the Mixture of Agents (MoA) implementation: +``` +system_message: + You have been provided with a set of responses from various small language models to the latest user query. + Your task is to synthesize these responses into a single, high-quality response. + It is crucial to critically evaluate the information provided in these responses, recognizing that some of it may be biased or incorrect. + Your response should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply. + Ensure your response is well-structured, coherent, and adheres to the highest standards of accuracy and reliability. + +user_message: "Please synthesize the responses from the small language models and give me only the most accurate information." +``` + +- Second combination is a bit modified version of the first one: +``` +system_message: + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to create a single, high-quality response based on the responses of the small language models. + You should perform something like a ensemble model based on majority voting. + Your response should be very accurate and informative, while keeping the faithfulness and relevance to the previous responses. + +user_message: "Please generate a single response based on the provided responses:" +``` + +- Third combination is basically making the aggregator LLM to choose the best response from the generated responses(thought): +``` +system_message: + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to choose the best response from the provided responses. + You should choose the response by analyzing all available responses and selecting the one you think is the most accurate and informative. + Keep in mind the response must be a high-quality response, while getting the most faithful and relevant information from the provided responses. + When you have made your choice, make that your final response and do not provide any additional responses, like explanations or clarifications why you chose that response. + +user_message: "Please choose a single response based on the provided responses:" +``` + +All the configurations for the experiments can be found in the `src/config` folder. + +### Results and Conclusion + +For the experiments, the results are stored in a CSV file in the `results` folder. Those results are extracted from the Langfuse Server which contain detailed traces and metrics for each experiment. The results are extracted using the `extract_results.py` script. + +#### Metrics + +The metrics used for the evaluation of the experiments are: + +- **Faithfulness**: This measures the factual consistency of the generated answer against the given context. It is calculated from answer and retrieved context. The answer is scaled to (0,1) range. Higher the better. The generated answer is regarded as faithful if all the claims made in the answer can be inferred from the given context. To calculate this, a set of claims from the generated answer is first identified. Then each of these claims is cross-checked with the given context to determine if it can be inferred from the context. + + +\[ +\text{Faithfulness} = \frac{\text{Number of claims in the generated answer that can be inferred from given context}}{\text{Total number of claims in the generated answer}} +\] + +- **Answer Relevancy**: The evaluation metric, Answer Relevancy, focuses on assessing how pertinent the generated answer is to the given prompt. A lower score is assigned to answers that are incomplete or contain redundant information and higher scores indicate better relevancy. This metric is computed using the question, the context and the answer. The Answer Relevancy is defined as the mean cosine similarity of the original question to a number of artifical questions, which where generated (reverse engineered) based on the answer: + +\[ +\text{Answer Relevancy} = \frac{1}{N} \sum_{i=1}^{N} \cos(E_{g_i}, E_o) +\] + +\[ +\text{Answer Relevancy} = \frac{1}{N} \sum_{i=1}^{N} \frac{E_{g_i} \cdot E_o}{\|E_{g_i}\| \|E_o\|} +\] + +Where: +- \(E_{g_i}\) is the embedding of the \(i^{th}\) artificial question generated from the answer. +- \(E_o\) is the embedding of the original question. +- \(N\) is the number of artificial questions generated from the answer. + +> [!NOTE] +> Eventhough in practice the score will range between 0 and 1 most of the time, this is not mathematically guranteed, due to the nature of the cosine similarity ranging from -1 to 1. + + +- **Context Utilization**: Context utilization measures the extent to which the retrieved context aligns with the annotated answer, treated as the ground truth. It is computed using question, ground truth and the retrieved context, and the values range between 0 and 1, with higher values indicating better performance. To estimate context utilization from the ground truth answer, each claim in the ground truth answer is analyzed to determine whether it can be attributed to the retrieved context or not. In an ideal scenario, all claims in the ground truth answer should be attributable to the retrieved context. If the ground truth is not provided, the judge evaluator LLM is used to generate the ground truth answer. + +\[ +\text{Context Utilization} = \frac{\text{GT claims that can be attributed to context}}{\text{Number of claims in GT}} +\] + +#### Judge LLM and Embedder +For Judge LLM Evaluator it is worked with Claude 3.5 Sonnet model and the Amazon Titan Embed Text 2 model with 512 dimensions. The configuration for the Judge LLM and Embedder can be found in the `src/constants/evaluation_config.py` file. + + +#### Analysis of the Results + + +The first initial exploration of the results was to check what questions were problematic, or in other words what questions had lower scores. The idea is to drop those questions from the experiments and to use the 10 most relevant questions and answers. + +To identify those questions, the dataset is group by the questions and it is calculated the mean of the scores for each questions for the metrics faithfulness, answer relevancy and context utilization. From those mean scores for each questions another basic average score is created which is used to rank the questions and to decide which questions to drop. + +

Questions with the lowest scores

+ + + + + + + + + + + + + + + + + + + + + + + + +
IndexQuestionScore
1How many stages are there in the development of the Llama 3 model?0.939045
2Does Claude 3 models have vision capabilities?0.925869
3Can the GPT-4 model accept both text and image inputs?0.884999
4On what architecture the Gemma model is based on?0.86232
5What is the difference between the Llama 2 and Llama 2-Chat ?0.857979
6Is Mixtral based on the idea of a mixture of experts?0.855282
7How many stages of training are in the GPT model?0.848322
8What tokenizer is used in the Gemma2 model?0.785732
9What is Mixture of Agents?0.770606
10What are the two tasks in BERT?0.768416
11How can attention be described in the Transformer?0.743815
12What is sliding window attention?0.741466
13What is optimizer is used for LLaMA?0.643345
14On what architecture the GPT-3 model is based on?0.583516
+ +From the table we can see what questions have the lowest scores. The last four questions are dropped from the analysis of the results. + +The next step is to analyze the results for each experiment and to rank them based on the average scores for each metric. The top 14 experiments for each metric are presented below and one additional ranking is done based on the average of the average scores for each metric. + +

Faithfulness

+ + + + + + + + + + + + + + + + + + + + + + + + +
IndexExperimentFaithfulness
1simple-rag-llama-3.1-70b-instruct0.961231
2simple-rag-llama-3.1-8b0.957778
3simple-rag-llama-3.1-405b-instruct0.945641
4mixture-rag-gemma2-9b-it-thought0.924542
5simple-rag-gemma-7b-it0.923677
6simple-rag-llama-3-8b0.913214
7simple-rag-llama-3-70b0.901136
8simple-rag-mixtral-8x7b-instruct0.896447
9simple-rag-gpt-4o0.895355
10mixture-rag-mixtral-8x7-instruct-thought0.892727
11mixture-rag-mixtral-8x7-instruct-modified0.882197
12simple-rag-mistral-7b-instruct0.878027
13simple-rag-claude-3-opus0.867106
14simple-rag-gpt-4o-mini0.851786
+ +We can see even at the top of the ranking by faithfulness there are some experiments with high score that are based on larger LLMs but the scores are really close to the experiments with smaller LLMs based RAGs. + +

Answer Relevancy

+ + + + + + + + + + + + + + + + + + + + + + \ + + +
IndexExperimentAnswer Relevancy
1simple-rag-gpt-4o-mini0.918347
2simple-rag-mistral-7b-instruct0.914597
3mixture-rag-gemma2-9b-it-thought0.910476
4simple-rag-claude-3.5-sonnet0.90533
5simple-rag-gemma2-9b-it0.905305
6mixture-rag-llama3.1-8b-instruct-thought0.897726
7simple-rag-claude-3-opus0.891054
8simple-rag-llama-3-70b0.885328
9simple-rag-mixtral-8x7b-instruct0.884369
10simple-rag-gpt-4o0.884128
11simple-rag-claude-3-sonnet0.874334
12mixture-rag-llama3.1-8b-instruct-modified0.871686
13mixture-rag-gemma2-9b-it-modified0.867729
14simple-rag-claude-3-haiku0.865661
+ +The experiments with smaller LLMs based RAGs overall are at the top of the ranking by answer relevancy. + +

Context Utilization

+ + + + + + + + + + + + + + + + + + + + + + + + +
IndexExperimentContext Utilization
1mixture-rag-llama3.1-8b-instruct0.916667
2mixture-rag-mixtral-8x7-instruct-modified0.916667
3mixture-rag-mixtral-8x7-instruct0.913889
4simple-rag-mixtral-8x7b-instruct0.908333
5simple-rag-mistral-7b-instruct0.908333
6simple-rag-gpt-4o-mini0.9
7mixture-rag-llama3.1-8b-instruct-modified0.897222
8simple-rag-llama-3.1-405b-instruct0.897222
9simple-rag-gpt-4o0.897222
10mixture-rag-gemma2-9b-it-modified0.880556
11mixture-rag-gemma2-9b-it-thought0.880556
12simple-rag-llama-3.1-8b0.880556
13simple-rag-gemma-7b-it0.875000
14simple-rag-llama-3-8b0.875000
+ +From the experiments for context utilization we can conclude that smaller LLMs based RAGs and RAG based on the Mixture of RAGs have better results than the experiments with larger LLMs based RAGs. + +

Average of the Average Scores

+ + + + + + + + + + + + + + + + + + + + + + + + +
IndexExperimentAverage Score
1mixture-rag-gemma2-9b-it-thought0.905191
2simple-rag-mistral-7b-instruct0.900319
3simple-rag-llama-3.1-405b-instruct0.896580
4simple-rag-mixtral-8x7b-instruct0.896383
5simple-rag-gpt-4o0.892935
6simple-rag-gpt-4o-mini0.890044
7simple-rag-llama-3.1-70b-instruct0.890022
8simple-rag-gemma-7b-it0.887449
9simple-rag-llama-3.1-8b0.887003
10mixture-rag-mixtral-8x7-instruct-modified0.886127
11simple-rag-llama-3-70b0.883451
12simple-rag-llama-3-8b0.881460
13simple-rag-gemma2-9b-it0.871802
14mixture-rag-gemma2-9b-it-modified0.857831
+ +From the results we can conclude that the experiments with smaller LLMs based RAGs have really similar results to the experiments with larger LLMs based RAGs. Also the top performing RAG pipeline is the Mixture RAG based around the aggregation of the responses using Gemma2 9b IT which is a smaller LLM, with aggregation based on thought process. + +#### Summary conclusion + +The experiments show that RAG pipelines based on smaller LLMs have really close performance and results to the RAG pipelines based on larger LLMs, in some cases they even outperform the larger LLMs based RAGs. Also the Mixture RAG proposed pipelines shows promising results and can be used in the future for production implementations. + +RAG systems based on smaller LLMs mainly work because they are utilizing already extracted context from the vector store(vector database) and their only usecase is to refine the context and generate the response or to aggregate the responses from multiple LLMs(by different ways). Most of them are able to handle big context size windows, plus they are really fast and cheap compared to the larger LLMs based RAGs. + + + +## Project Structure: +``` +. +├── config # Configuration files +├── data # Data files & Vector Database files +| ├── database_1500_100 # FAISS Vector Database files for database with 1500 chunk size and 100 chunk overlap +| └── raw # Raw data files (PDFs) used as the dataset for the experiments +├── notebooks # Jupyter notebooks for data analysis +| └── 01_exploring_results.ipynb # Jupyter notebook for exploring the results +├── results # Results files +| └── results.csv # Results CSV file +├── src # Source code +| ├── configuration # Configuration models and validation +| | ├── configuration_model.py # Configuration models +| | └── load_configuration.py # Load configuration files script +| ├── constants # Constants used in the project +| | ├── evaluation_config.py # Evaluation configuration constants +| | ├── prompts.py # Prompt templates +| | └── questions.py # Questions for the experiments - evaluation dataset +| ├── models # Models for the RAG pipelines +| | ├── base_mixture_rag # Abstract base class for Mixture RAG +| | ├── base_simple_rag # Abstract base class for Simple RAG +| | ├── mixture_rag.py # Mixture RAG model +| | └── simple_rag.py # Simple RAG model +| ├── pipelines # RAG pipelines +| | ├── mixture_rag_pipeline.py # Mixture RAG pipeline +| | └── simple_rag_pipeline.py # Simple RAG pipeline +| └── utils # Utility functions +| └── evaluation_utils.py # Evaluation utility functions +├── .env # Environment variables file (not included in the repository) +├── .gitignore # Git ignore file +├── docker-compose.yml # Docker Compose file for Langfuse Server +├── execute_pipeline.py # Script to execute the RAG pipeline +├── extract_results.py # Script to extract the results +├── README.md # README.md file (this file) +└── requirements.txt # Python requirements file +``` diff --git a/config/mixture.rag.claude-3-haiku-modified.yaml b/config/mixture.rag.claude-3-haiku-modified.yaml new file mode 100644 index 0000000..00d24d7 --- /dev/null +++ b/config/mixture.rag.claude-3-haiku-modified.yaml @@ -0,0 +1,86 @@ +experiment_name: "mixture-rag-claude-3-haiku-modified" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "groq" + model_spec: + model_name: "gemma2-9b-it" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "mistral.mixtral-8x7b-instruct-v0:1" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "meta.llama3-1-8b-instruct-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: | + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to create a single, high-quality response based on the responses of the small language models. + You should perform something like a ensemble model based on majority voting. + Your response should be very accurate and informative, while keeping the faithfulness and relevance to the previous responses. + user_message: "Please generate a single response based on the provided responses:" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" \ No newline at end of file diff --git a/config/mixture.rag.claude-3-haiku-thought.yaml b/config/mixture.rag.claude-3-haiku-thought.yaml new file mode 100644 index 0000000..0314b54 --- /dev/null +++ b/config/mixture.rag.claude-3-haiku-thought.yaml @@ -0,0 +1,87 @@ +experiment_name: "mixture-rag-claude-3-haiku-thought" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "groq" + model_spec: + model_name: "gemma2-9b-it" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "mistral.mixtral-8x7b-instruct-v0:1" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "meta.llama3-1-8b-instruct-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: | + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to choose the best response from the provided responses. + You should choose the response by analyzing all available responses and selecting the one you think is the most accurate and informative. + Keep in mind the response must be a high-quality response, while getting the most faithful and relevant information from the provided responses. + When you have made your choice, make that your final response and do not provide any additional responses, like explanations or clarifications why you chose that response. + user_message: "Please choose a single response based on the provided responses:" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" \ No newline at end of file diff --git a/config/mixture.rag.claude-3-haiku.yaml b/config/mixture.rag.claude-3-haiku.yaml index 6012be0..ae63c22 100644 --- a/config/mixture.rag.claude-3-haiku.yaml +++ b/config/mixture.rag.claude-3-haiku.yaml @@ -11,7 +11,7 @@ layers: max_tokens: 4096 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" @@ -25,7 +25,7 @@ layers: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" @@ -39,7 +39,7 @@ layers: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/mixture.rag.gemma2-9b-it-modified.yaml b/config/mixture.rag.gemma2-9b-it-modified.yaml new file mode 100644 index 0000000..25fc3b8 --- /dev/null +++ b/config/mixture.rag.gemma2-9b-it-modified.yaml @@ -0,0 +1,86 @@ +experiment_name: "mixture-rag-gemma2-9b-it-modified" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "mistral.mixtral-8x7b-instruct-v0:1" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "meta.llama3-1-8b-instruct-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "groq" + model_spec: + model_name: "gemma2-9b-it" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to create a single, high-quality response based on the responses of the small language models. + You should perform something like a ensemble model based on majority voting. + Your response should be very accurate and informative, while keeping the faithfulness and relevance to the previous responses. + user_message: "Please generate a single response based on the provided responses:" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" \ No newline at end of file diff --git a/config/mixture.rag.gemma2-9b-it-thought.yaml b/config/mixture.rag.gemma2-9b-it-thought.yaml new file mode 100644 index 0000000..5b2db8b --- /dev/null +++ b/config/mixture.rag.gemma2-9b-it-thought.yaml @@ -0,0 +1,87 @@ +experiment_name: "mixture-rag-gemma2-9b-it-thought" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "mistral.mixtral-8x7b-instruct-v0:1" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "meta.llama3-1-8b-instruct-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "groq" + model_spec: + model_name: "gemma2-9b-it" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to choose the best response from the provided responses. + You should choose the response by analyzing all available responses and selecting the one you think is the most accurate and informative. + Keep in mind the response must be a high-quality response, while getting the most faithful and relevant information from the provided responses. + When you have made your choice, make that your final response and do not provide any additional responses, like explanations or clarifications why you chose that response. + user_message: "Please choose a single response based on the provided responses:" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" \ No newline at end of file diff --git a/config/mixture.rag.gemma2-9b-it.yaml b/config/mixture.rag.gemma2-9b-it.yaml index 4183fc0..aebc21f 100644 --- a/config/mixture.rag.gemma2-9b-it.yaml +++ b/config/mixture.rag.gemma2-9b-it.yaml @@ -15,7 +15,7 @@ layers: stop_sequences: ["\n\nHuman"] prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" @@ -29,7 +29,7 @@ layers: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" @@ -43,7 +43,7 @@ layers: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/mixture.rag.llama-3-1-8b-instruct-modified.yaml b/config/mixture.rag.llama-3-1-8b-instruct-modified.yaml new file mode 100644 index 0000000..394a414 --- /dev/null +++ b/config/mixture.rag.llama-3-1-8b-instruct-modified.yaml @@ -0,0 +1,86 @@ +experiment_name: "mixture-rag-llama3.1-8b-instruct-modified" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "groq" + model_spec: + model_name: "gemma2-9b-it" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "mistral.mixtral-8x7b-instruct-v0:1" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "meta.llama3-1-8b-instruct-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to create a single, high-quality response based on the responses of the small language models. + You should perform something like a ensemble model based on majority voting. + Your response should be very accurate and informative, while keeping the faithfulness and relevance to the previous responses. + user_message: "Please generate a single response based on the provided responses:" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" \ No newline at end of file diff --git a/config/mixture.rag.llama-3-1-8b-instruct-thought.yaml b/config/mixture.rag.llama-3-1-8b-instruct-thought.yaml new file mode 100644 index 0000000..aba2bb6 --- /dev/null +++ b/config/mixture.rag.llama-3-1-8b-instruct-thought.yaml @@ -0,0 +1,87 @@ +experiment_name: "mixture-rag-llama3.1-8b-instruct-thought" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "groq" + model_spec: + model_name: "gemma2-9b-it" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "mistral.mixtral-8x7b-instruct-v0:1" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "meta.llama3-1-8b-instruct-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to choose the best response from the provided responses. + You should choose the response by analyzing all available responses and selecting the one you think is the most accurate and informative. + Keep in mind the response must be a high-quality response, while getting the most faithful and relevant information from the provided responses. + When you have made your choice, make that your final response and do not provide any additional responses, like explanations or clarifications why you chose that response. + user_message: "Please choose a single response based on the provided responses:" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" \ No newline at end of file diff --git a/config/mixture.rag.llama-3-1-8b-instruct.yaml b/config/mixture.rag.llama-3-1-8b-instruct.yaml index 835afc7..9937eaa 100644 --- a/config/mixture.rag.llama-3-1-8b-instruct.yaml +++ b/config/mixture.rag.llama-3-1-8b-instruct.yaml @@ -11,7 +11,7 @@ layers: max_tokens: 4096 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" @@ -25,7 +25,7 @@ layers: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" @@ -41,7 +41,7 @@ layers: stop_sequences: ["\n\nHuman"] prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/mixture.rag.mixtral-8-7b-instruct-modified.yaml b/config/mixture.rag.mixtral-8-7b-instruct-modified.yaml new file mode 100644 index 0000000..f45a269 --- /dev/null +++ b/config/mixture.rag.mixtral-8-7b-instruct-modified.yaml @@ -0,0 +1,86 @@ +experiment_name: "mixture-rag-mixtral-8x7-instruct-modified" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "groq" + model_spec: + model_name: "gemma2-9b-it" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "meta.llama3-1-8b-instruct-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "mistral.mixtral-8x7b-instruct-v0:1" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to create a single, high-quality response based on the responses of the small language models. + You should perform something like a ensemble model based on majority voting. + Your response should be very accurate and informative, while keeping the faithfulness and relevance to the previous responses. + user_message: "Please generate a single response based on the provided responses:" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" \ No newline at end of file diff --git a/config/mixture.rag.mixtral-8-7b-instruct-thought.yaml b/config/mixture.rag.mixtral-8-7b-instruct-thought.yaml new file mode 100644 index 0000000..3e3df8d --- /dev/null +++ b/config/mixture.rag.mixtral-8-7b-instruct-thought.yaml @@ -0,0 +1,87 @@ +experiment_name: "mixture-rag-mixtral-8x7-instruct-thought" + +layers: + - layer_type: "rag" + layer_spec: + - llm: + provider: "groq" + model_spec: + model_name: "gemma2-9b-it" + temperature: 0.1 + max_tokens: 4096 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "anthropic.claude-3-haiku-20240307-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_k: 250 + top_p: 1 + stop_sequences: ["\n\nHuman"] + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - llm: + provider: "bedrock" + model_spec: + model_id: "meta.llama3-1-8b-instruct-v1:0" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You are an assistant that has a lot of knowledge about Large Language Models. + Answer the user's question in a way that is easy to understand and informative. + Use the provided context to generate a response that is relevant and accurate. + user_message: "Please answer my question based on the provided context:" + - layer_type: "aggregator" + layer_spec: + - llm: + provider: "bedrock" + model_spec: + model_id: "mistral.mixtral-8x7b-instruct-v0:1" + model_kwargs: + max_tokens: 4096 + temperature: 0.1 + top_p: 1 + prompt: + system_message: | + You have been provided with a set of responses from various small language models to the latest user query. + The responses of the small language models are based on the context provided in the user query. + Your task is to choose the best response from the provided responses. + You should choose the response by analyzing all available responses and selecting the one you think is the most accurate and informative. + Keep in mind the response must be a high-quality response, while getting the most faithful and relevant information from the provided responses. + When you have made your choice, make that your final response and do not provide any additional responses, like explanations or clarifications why you chose that response. + user_message: "Please choose a single response based on the provided responses:" + +embedder: + model_id: "amazon.titan-embed-text-v2:0" + model_kwargs: + dimensions: 512 + normalize: true + +retriever: + search_type: "similarity" + retriever_kwargs: + k: 5 + +chunker: + chunk_size: 1500 + chunk_overlap: 100 + +data: + path: "./data" + +vector_store: + path: "./data/database_1500_100" \ No newline at end of file diff --git a/config/mixture.rag.mixtral-8-7b-instruct.yaml b/config/mixture.rag.mixtral-8-7b-instruct.yaml index ab5596f..2c1a7fe 100644 --- a/config/mixture.rag.mixtral-8-7b-instruct.yaml +++ b/config/mixture.rag.mixtral-8-7b-instruct.yaml @@ -11,7 +11,7 @@ layers: max_tokens: 4096 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" @@ -27,7 +27,7 @@ layers: stop_sequences: ["\n\nHuman"] prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" @@ -41,7 +41,7 @@ layers: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.claude-3-5-sonnet.yaml b/config/simple.rag.claude-3-5-sonnet.yaml index 10c81e5..10b8bff 100644 --- a/config/simple.rag.claude-3-5-sonnet.yaml +++ b/config/simple.rag.claude-3-5-sonnet.yaml @@ -12,7 +12,7 @@ llm: stop_sequences: ["\n\nHuman"] prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.claude-3-haiku.yaml b/config/simple.rag.claude-3-haiku.yaml index a5d1fcf..2256ed3 100644 --- a/config/simple.rag.claude-3-haiku.yaml +++ b/config/simple.rag.claude-3-haiku.yaml @@ -12,7 +12,7 @@ llm: stop_sequences: ["\n\nHuman"] prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.claude-3-opus.yaml b/config/simple.rag.claude-3-opus.yaml index 97cf0aa..73c5b9d 100644 --- a/config/simple.rag.claude-3-opus.yaml +++ b/config/simple.rag.claude-3-opus.yaml @@ -12,7 +12,7 @@ llm: stop_sequences: ["\n\nHuman"] prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.claude-3-sonnet.yaml b/config/simple.rag.claude-3-sonnet.yaml index fcb0803..1c53ef1 100644 --- a/config/simple.rag.claude-3-sonnet.yaml +++ b/config/simple.rag.claude-3-sonnet.yaml @@ -12,7 +12,7 @@ llm: stop_sequences: ["\n\nHuman"] prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.gemma-7b-it.yaml b/config/simple.rag.gemma-7b-it.yaml index 008dfea..705d090 100644 --- a/config/simple.rag.gemma-7b-it.yaml +++ b/config/simple.rag.gemma-7b-it.yaml @@ -8,7 +8,7 @@ llm: max_tokens: 4096 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.gemma2-9b-it.yaml b/config/simple.rag.gemma2-9b-it.yaml index 012f06e..527f5fc 100644 --- a/config/simple.rag.gemma2-9b-it.yaml +++ b/config/simple.rag.gemma2-9b-it.yaml @@ -8,7 +8,7 @@ llm: max_tokens: 4096 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.gpt-4-turbo.yaml b/config/simple.rag.gpt-4-turbo.yaml index c0e61db..83402ce 100644 --- a/config/simple.rag.gpt-4-turbo.yaml +++ b/config/simple.rag.gpt-4-turbo.yaml @@ -8,7 +8,7 @@ llm: max_tokens: 4096 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.gpt-4o-mini.yaml b/config/simple.rag.gpt-4o-mini.yaml index 8bc0681..eaa7a29 100644 --- a/config/simple.rag.gpt-4o-mini.yaml +++ b/config/simple.rag.gpt-4o-mini.yaml @@ -8,7 +8,7 @@ llm: max_tokens: 4096 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.gpt-4o.yaml b/config/simple.rag.gpt-4o.yaml index 51e2163..7c75c4f 100644 --- a/config/simple.rag.gpt-4o.yaml +++ b/config/simple.rag.gpt-4o.yaml @@ -8,7 +8,7 @@ llm: max_tokens: 4096 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.llama-3-1-405b-instruct.yaml b/config/simple.rag.llama-3-1-405b-instruct.yaml index 47cb910..7d44a20 100644 --- a/config/simple.rag.llama-3-1-405b-instruct.yaml +++ b/config/simple.rag.llama-3-1-405b-instruct.yaml @@ -10,7 +10,7 @@ llm: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.llama-3-1-70b-instruct.yaml b/config/simple.rag.llama-3-1-70b-instruct.yaml index d766972..95785a0 100644 --- a/config/simple.rag.llama-3-1-70b-instruct.yaml +++ b/config/simple.rag.llama-3-1-70b-instruct.yaml @@ -10,7 +10,7 @@ llm: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.llama-3-1-8b-instruct.yaml b/config/simple.rag.llama-3-1-8b-instruct.yaml index 5aa3bb1..490b92c 100644 --- a/config/simple.rag.llama-3-1-8b-instruct.yaml +++ b/config/simple.rag.llama-3-1-8b-instruct.yaml @@ -10,7 +10,7 @@ llm: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.llama-3-70b-instruct.yaml b/config/simple.rag.llama-3-70b-instruct.yaml index 8ce0d91..2b028e1 100644 --- a/config/simple.rag.llama-3-70b-instruct.yaml +++ b/config/simple.rag.llama-3-70b-instruct.yaml @@ -10,7 +10,7 @@ llm: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.llama-3-8b-instruct.yaml b/config/simple.rag.llama-3-8b-instruct.yaml index 71eb86c..480d239 100644 --- a/config/simple.rag.llama-3-8b-instruct.yaml +++ b/config/simple.rag.llama-3-8b-instruct.yaml @@ -10,7 +10,7 @@ llm: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.mistral-7b-instruct.yaml b/config/simple.rag.mistral-7b-instruct.yaml index d54971d..98ee73b 100644 --- a/config/simple.rag.mistral-7b-instruct.yaml +++ b/config/simple.rag.mistral-7b-instruct.yaml @@ -10,7 +10,7 @@ llm: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/config/simple.rag.mixtral-8-7b-instruct.yaml b/config/simple.rag.mixtral-8-7b-instruct.yaml index 17c63e2..53ab525 100644 --- a/config/simple.rag.mixtral-8-7b-instruct.yaml +++ b/config/simple.rag.mixtral-8-7b-instruct.yaml @@ -10,7 +10,7 @@ llm: top_p: 1 prompt: system_message: | - You are an assistant that has a lot of knowledge about Large Language Models. A + You are an assistant that has a lot of knowledge about Large Language Models. Answer the user's question in a way that is easy to understand and informative. Use the provided context to generate a response that is relevant and accurate. user_message: "Please answer my question based on the provided context:" diff --git a/execute_pipeline.py b/execute_pipeline.py index 44e923b..2ecfc73 100644 --- a/execute_pipeline.py +++ b/execute_pipeline.py @@ -1,13 +1,13 @@ """ This script executes the appropriate pipeline based on the loaded configuration. """ + import logging import warnings from dotenv import load_dotenv -from src.configuration.configuration_model import (MixtureRAGConfig, - SimpleRAGConfig) +from src.configuration.configuration_model import MixtureRAGConfig, SimpleRAGConfig from src.configuration.load_configuration import load_config from src.constants import prompts, questions from src.pipelines.mixture_rag_pipeline import mixture_rag_pipeline_execution @@ -20,11 +20,11 @@ warnings.filterwarnings("ignore") -CONFIG_PATH = "/home/bojan/Work/mixture-of-rags/config/mixture.rag.example.yaml" +CONFIG_PATH = "/home/bojan/Work/mixture-of-rags/config/simple.rag.example.yaml" PROMPT_CONSTANTS = [ - prompts.CLAUDE_3_PROMPT_RAG_SIMPLE, - prompts.CLAUDE_3_PROMPT_RAG_SIMPLE, - prompts.CLAUDE_3_PROMPT_RAG_SIMPLE, + prompts.GEMMA_PROMPT_RAG_SIMPLE, + prompts.MISTRAL_7B_PROMPT_RAG_SIMPLE, + prompts.LLAMA_3_PROMPT_RAG_SIMPLE, ] PROMPT_AGGREGATOR_CONSTANT = prompts.CLAUDE_3_MIXTURE_RAG QUESTIONS_CONSTANT = questions.QUESTIONS @@ -33,12 +33,12 @@ logger.info("Loading configuration") config = load_config(CONFIG_PATH) if isinstance(config, MixtureRAGConfig): - logger.info("Executing MixtureRAG pipeline") + logger.info("Executing Mixture RAG pipeline") mixture_rag_pipeline_execution( config, PROMPT_CONSTANTS, PROMPT_AGGREGATOR_CONSTANT, QUESTIONS_CONSTANT ) elif isinstance(config, SimpleRAGConfig): - logger.info("Executing SimpleRAG pipeline") + logger.info("Executing Simple RAG pipeline") simple_rag_pipeline_execution(config, PROMPT_CONSTANTS, QUESTIONS_CONSTANT) else: logger.error("Invalid configuration type") diff --git a/extract_results.py b/extract_results.py index 9e71d43..5008171 100644 --- a/extract_results.py +++ b/extract_results.py @@ -1,5 +1,6 @@ """Script to extract results from Langfuse API and save them to a CSV file.""" +import logging import os import pandas as pd @@ -7,19 +8,18 @@ from langfuse import Langfuse from requests.auth import HTTPBasicAuth -URL = "http://cloud.langfuse.com/api/public/scores/" +from src.constants import evaluation_config + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +URL = os.environ.get("LANGFUSE_SCORE_URL") USERNAME = os.environ.get("LANGFUSE_PUBLIC_KEY") PASSWORD = os.environ.get("LANGFUSE_SECRET_KEY") PUBLIC_KEY = os.environ.get("LANGFUSE_PUBLIC_KEY") SECRET_KEY = os.environ.get("LANGFUSE_SECRET_KEY") HOST = os.environ.get("LANGFUSE_HOST") -COLUMNS = [ - "experiment_name", - "trace_id", - "faithfulness", - "answer_relevancy", - "context_utilization", -] +COLUMNS = evaluation_config.RESULT_COLUMNS def get_score(score_id: str) -> tuple[str, float]: @@ -33,7 +33,7 @@ def get_score(score_id: str) -> tuple[str, float]: Tuple[str, float]: The name and value of the score """ response = requests.get( - URL + score_id, auth=HTTPBasicAuth(USERNAME, PASSWORD) + URL + score_id, auth=HTTPBasicAuth(USERNAME, PASSWORD), timeout=60 ).json() score_name = response["name"] score_value = response["value"] @@ -59,61 +59,77 @@ def fetch_session_ids(client: Langfuse) -> list[str]: ] -def fetch_traces(client: Langfuse, session_ids: list[str]) -> list: +def fetch_traces(client: Langfuse, local_session_ids: list[str]) -> list: """ Fetch the traces from the Langfuse API. Args: client (Langfuse): The Langfuse client. - session_ids (List[str]): The session IDs. + local_session_ids (List[str]): The session IDs. Returns: List: The traces. """ - traces = [] - for session_id in session_ids: - traces += client.fetch_traces(session_id=session_id).data - return traces + local_traces = [] + for session_id in local_session_ids: + local_traces += client.fetch_traces(session_id=session_id).data + return local_traces -def process_traces(traces: list) -> pd.DataFrame: +def process_traces(local_traces: list) -> pd.DataFrame: """ Process the traces and return a DataFrame. Args: - traces (List): The traces. + local_traces (List): The traces. Returns: pd.DataFrame: The DataFrame. """ data = [] - for trace in traces: + for trace in local_traces: experiment_name = trace.session_id trace_id = trace.id - trace_scores_ids = trace.scores - scores = {} - for score_id in trace_scores_ids: - score_name, score_value = get_score(score_id) - scores[score_name] = score_value - faithfulness = scores["faithfulness"] - answer_relevancy = scores["answer_relevancy"] - context_utilization = scores["context_utilization"] + if "mixture" in experiment_name: + question = trace.input["question"] + answer = trace.output["content"] + else: + question = trace.input["query"] + answer = trace.output["result"] + + scores = { + score_name: score_value + for score_id in trace.scores + for score_name, score_value in [get_score(score_id)] + } + data.append( [ experiment_name, trace_id, - faithfulness, - answer_relevancy, - context_utilization, + question, + answer, + scores["faithfulness"], + scores["answer_relevancy"], + scores["context_utilization"], ] ) return pd.DataFrame(data, columns=COLUMNS) if __name__ == "__main__": + logger.info("Creating Langfuse client") langfuse_client = Langfuse(public_key=PUBLIC_KEY, secret_key=SECRET_KEY, host=HOST) + logger.info("Fetching session IDs - experiment names") session_ids = fetch_session_ids(langfuse_client) + logger.info("Session IDs fetched") + logger.info("Fetching traces") traces = fetch_traces(langfuse_client, session_ids) + logger.info("Traces fetched") + logger.info("Processing traces - extracting scores from traces") dataframe = process_traces(traces) + logger.info("Traces processed") os.makedirs("results", exist_ok=True) + logger.info("Saving results to CSV") dataframe.to_csv("./results/results.csv", index=False) + logger.info("Results saved to CSV") diff --git a/notebooks/01_exploring_results.ipynb b/notebooks/01_exploring_results.ipynb new file mode 100644 index 0000000..9d66ef6 --- /dev/null +++ b/notebooks/01_exploring_results.ipynb @@ -0,0 +1,3360 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exploring results data\n", + "\n", + "The idea of this notebook is to explore the results data and try to find the best RAG approach by analizing the scores of metrics.\n", + "\n", + "For metrics, we are using:\n", + "- faithfulness\n", + "- answer_relevancy\n", + "- context_utilization\n", + "\n", + "Each question for each experiment has scores for each metrics. We will try to do some different analysis of the scores to find the best approach:\n", + "\n", + "- Average of the scores for each metric on experiment level and then analyzing the results, after that creating a score which is an sum of the average scores and then analyzing the results.\n", + "- Average scores for each metrics on question level and then analyzing the results, after that we drop the questions with the lowest scores and then analyzing the results based on the first approach.\n", + "\n", + "**First steps:**\n", + "\n", + "The first steps that need to be done are:\n", + "- importing the libraries needed for EDA\n", + "- loading the data\n", + "- checking the data" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing needed libraries\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "# Reading the results.csv file\n", + "dataframe = pd.read_csv(\"/home/bojan/Work/mixture-of-rags/results/results.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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experiment_nametrace_idquestionanswerfaithfulnessanswer_relevancycontext_utilization
0mixture-rag-claude-3-haiku-thought5d7ae2d3-f2b8-4840-b877-69165f991599How can attention be described in the Transfor...The response from the second model provides th...0.7272730.7230331.000000
1mixture-rag-claude-3-haiku-thoughtaa2067f5-33f7-4d70-b4c9-f1752084c8aeWhat is Mixture of Agents?The response from the third model provides the...0.5555560.4661290.805556
2mixture-rag-claude-3-haiku-thoughtcefa79c4-cba0-4961-bc87-005e2c2b8837Is Mixtral based on the idea of a mixture of e...Based on the provided responses, the best resp...0.7500000.6362651.000000
3mixture-rag-claude-3-haiku-thought8f2ee9a4-72d8-4956-8131-fa0ed9bce4a0What is sliding window attention?The response from the first model provides the...0.5714290.6911741.000000
4mixture-rag-claude-3-haiku-thought584e89e1-cc11-4101-8c96-f10cb725fa15How many stages are there in the development o...The response from the second model provides th...1.0000000.9385621.000000
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" + ], + "text/plain": [ + " experiment_name trace_id \\\n", + "0 mixture-rag-claude-3-haiku-thought 5d7ae2d3-f2b8-4840-b877-69165f991599 \n", + "1 mixture-rag-claude-3-haiku-thought aa2067f5-33f7-4d70-b4c9-f1752084c8ae \n", + "2 mixture-rag-claude-3-haiku-thought cefa79c4-cba0-4961-bc87-005e2c2b8837 \n", + "3 mixture-rag-claude-3-haiku-thought 8f2ee9a4-72d8-4956-8131-fa0ed9bce4a0 \n", + "4 mixture-rag-claude-3-haiku-thought 584e89e1-cc11-4101-8c96-f10cb725fa15 \n", + "\n", + " question \\\n", + "0 How can attention be described in the Transfor... \n", + "1 What is Mixture of Agents? \n", + "2 Is Mixtral based on the idea of a mixture of e... \n", + "3 What is sliding window attention? \n", + "4 How many stages are there in the development o... \n", + "\n", + " answer faithfulness \\\n", + "0 The response from the second model provides th... 0.727273 \n", + "1 The response from the third model provides the... 0.555556 \n", + "2 Based on the provided responses, the best resp... 0.750000 \n", + "3 The response from the first model provides the... 0.571429 \n", + "4 The response from the second model provides th... 1.000000 \n", + "\n", + " answer_relevancy context_utilization \n", + "0 0.723033 1.000000 \n", + "1 0.466129 0.805556 \n", + "2 0.636265 1.000000 \n", + "3 0.691174 1.000000 \n", + "4 0.938562 1.000000 " + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the dataframe\n", + "dataframe.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis based on the first approach\n", + "\n", + "The steps for the first approach are:\n", + "- Create a copy of the data\n", + "- Calculate the average score for each metrics per question(row)\n", + "- Check if the scores are created correctly\n", + "- Create a dataframe with all the metrics + the new score and sort the values by all the metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a copy of the dataframe\n", + "dataframe_1 = dataframe.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a score for each row by calculating the mean of the scores for each row (faithfulness, answer_relevancy, context_utilization)\n", + "dataframe_1[\"score\"] = dataframe_1[\n", + " [\"faithfulness\", \"answer_relevancy\", \"context_utilization\"]\n", + "].mean(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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experiment_nametrace_idquestionanswerfaithfulnessanswer_relevancycontext_utilizationscore
0mixture-rag-claude-3-haiku-thought5d7ae2d3-f2b8-4840-b877-69165f991599How can attention be described in the Transfor...The response from the second model provides th...0.7272730.7230331.0000000.816768
1mixture-rag-claude-3-haiku-thoughtaa2067f5-33f7-4d70-b4c9-f1752084c8aeWhat is Mixture of Agents?The response from the third model provides the...0.5555560.4661290.8055560.609080
2mixture-rag-claude-3-haiku-thoughtcefa79c4-cba0-4961-bc87-005e2c2b8837Is Mixtral based on the idea of a mixture of e...Based on the provided responses, the best resp...0.7500000.6362651.0000000.795422
3mixture-rag-claude-3-haiku-thought8f2ee9a4-72d8-4956-8131-fa0ed9bce4a0What is sliding window attention?The response from the first model provides the...0.5714290.6911741.0000000.754201
4mixture-rag-claude-3-haiku-thought584e89e1-cc11-4101-8c96-f10cb725fa15How many stages are there in the development o...The response from the second model provides th...1.0000000.9385621.0000000.979521
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" + ], + "text/plain": [ + " experiment_name trace_id \\\n", + "0 mixture-rag-claude-3-haiku-thought 5d7ae2d3-f2b8-4840-b877-69165f991599 \n", + "1 mixture-rag-claude-3-haiku-thought aa2067f5-33f7-4d70-b4c9-f1752084c8ae \n", + "2 mixture-rag-claude-3-haiku-thought cefa79c4-cba0-4961-bc87-005e2c2b8837 \n", + "3 mixture-rag-claude-3-haiku-thought 8f2ee9a4-72d8-4956-8131-fa0ed9bce4a0 \n", + "4 mixture-rag-claude-3-haiku-thought 584e89e1-cc11-4101-8c96-f10cb725fa15 \n", + "\n", + " question \\\n", + "0 How can attention be described in the Transfor... \n", + "1 What is Mixture of Agents? \n", + "2 Is Mixtral based on the idea of a mixture of e... \n", + "3 What is sliding window attention? \n", + "4 How many stages are there in the development o... \n", + "\n", + " answer faithfulness \\\n", + "0 The response from the second model provides th... 0.727273 \n", + "1 The response from the third model provides the... 0.555556 \n", + "2 Based on the provided responses, the best resp... 0.750000 \n", + "3 The response from the first model provides the... 0.571429 \n", + "4 The response from the second model provides th... 1.000000 \n", + "\n", + " answer_relevancy context_utilization score \n", + "0 0.723033 1.000000 0.816768 \n", + "1 0.466129 0.805556 0.609080 \n", + "2 0.636265 1.000000 0.795422 \n", + "3 0.691174 1.000000 0.754201 \n", + "4 0.938562 1.000000 0.979521 " + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the new dataframe\n", + "dataframe_1.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "# Grouping the dataframe by experiment_name and calculating the mean of the scores for each experiment\n", + "dataframe_1_mean = (\n", + " dataframe_1.drop(columns=[\"trace_id\", \"question\", \"answer\"])\n", + " .groupby(\"experiment_name\")\n", + " .mean()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
experiment_name
simple-rag-llama-3.1-8b0.9222220.7924260.7817460.832132
simple-rag-llama-3.1-405b-instruct0.9057620.8410260.8075400.851443
simple-rag-llama-3.1-70b-instruct0.9031280.8397520.8055560.849478
simple-rag-llama-3-8b0.8871170.8089320.8095240.835191
simple-rag-gemma-7b-it0.8734600.8347100.7916670.833279
simple-rag-gpt-4o0.8733520.8521220.8253970.850290
mixture-rag-gemma2-9b-it-thought0.8640670.8572160.7996030.840295
simple-rag-claude-3-opus0.8369470.8609460.7182540.805382
simple-rag-mixtral-8x7b-instruct0.8353740.7811650.8373020.817947
simple-rag-llama-3-70b0.8177430.8651890.7837300.822221
mixture-rag-llama3.1-8b-instruct-thought0.8161860.8347540.7658730.805604
simple-rag-gemma2-9b-it0.8148560.8887740.7837300.829120
mixture-rag-mixtral-8x7-instruct-modified0.8005640.8314770.7896830.807241
mixture-rag-mixtral-8x7-instruct-thought0.7990120.8015600.7619050.787492
simple-rag-mistral-7b-instruct0.7989870.8476590.8194440.822030
simple-rag-claude-3.5-sonnet0.7981580.8706350.7738100.814201
simple-rag-gpt-4o-mini0.7804920.8851330.8134920.826372
simple-rag-claude-3-sonnet0.7778530.8272060.7658730.790311
mixture-rag-gemma2-9b-it-modified0.7683180.8194830.8134920.800431
simple-rag-claude-3-haiku0.7481640.8218640.7599210.776649
simple-rag-gpt-4-turbo0.7449520.8472590.7718250.788012
mixture-rag-llama3.1-8b-instruct0.7107960.7760890.8035710.763485
mixture-rag-mixtral-8x7-instruct0.7024590.7954910.8234130.773787
mixture-rag-gemma2-9b-it0.6667290.8210130.7936510.760464
mixture-rag-llama3.1-8b-instruct-modified0.6615410.8438790.8115080.772309
mixture-rag-claude-3-haiku-modified0.6294210.8340740.7440480.735848
mixture-rag-claude-3-haiku0.6101960.8206880.7936510.741512
mixture-rag-claude-3-haiku-thought0.6054630.7310660.7301590.688896
\n", + "
" + ], + "text/plain": [ + " faithfulness answer_relevancy \\\n", + "experiment_name \n", + "simple-rag-llama-3.1-8b 0.922222 0.792426 \n", + "simple-rag-llama-3.1-405b-instruct 0.905762 0.841026 \n", + "simple-rag-llama-3.1-70b-instruct 0.903128 0.839752 \n", + "simple-rag-llama-3-8b 0.887117 0.808932 \n", + "simple-rag-gemma-7b-it 0.873460 0.834710 \n", + "simple-rag-gpt-4o 0.873352 0.852122 \n", + "mixture-rag-gemma2-9b-it-thought 0.864067 0.857216 \n", + "simple-rag-claude-3-opus 0.836947 0.860946 \n", + "simple-rag-mixtral-8x7b-instruct 0.835374 0.781165 \n", + "simple-rag-llama-3-70b 0.817743 0.865189 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.816186 0.834754 \n", + "simple-rag-gemma2-9b-it 0.814856 0.888774 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.800564 0.831477 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.799012 0.801560 \n", + "simple-rag-mistral-7b-instruct 0.798987 0.847659 \n", + "simple-rag-claude-3.5-sonnet 0.798158 0.870635 \n", + "simple-rag-gpt-4o-mini 0.780492 0.885133 \n", + "simple-rag-claude-3-sonnet 0.777853 0.827206 \n", + "mixture-rag-gemma2-9b-it-modified 0.768318 0.819483 \n", + "simple-rag-claude-3-haiku 0.748164 0.821864 \n", + "simple-rag-gpt-4-turbo 0.744952 0.847259 \n", + "mixture-rag-llama3.1-8b-instruct 0.710796 0.776089 \n", + "mixture-rag-mixtral-8x7-instruct 0.702459 0.795491 \n", + "mixture-rag-gemma2-9b-it 0.666729 0.821013 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.661541 0.843879 \n", + "mixture-rag-claude-3-haiku-modified 0.629421 0.834074 \n", + "mixture-rag-claude-3-haiku 0.610196 0.820688 \n", + "mixture-rag-claude-3-haiku-thought 0.605463 0.731066 \n", + "\n", + " context_utilization score \n", + "experiment_name \n", + "simple-rag-llama-3.1-8b 0.781746 0.832132 \n", + "simple-rag-llama-3.1-405b-instruct 0.807540 0.851443 \n", + "simple-rag-llama-3.1-70b-instruct 0.805556 0.849478 \n", + "simple-rag-llama-3-8b 0.809524 0.835191 \n", + "simple-rag-gemma-7b-it 0.791667 0.833279 \n", + "simple-rag-gpt-4o 0.825397 0.850290 \n", + "mixture-rag-gemma2-9b-it-thought 0.799603 0.840295 \n", + "simple-rag-claude-3-opus 0.718254 0.805382 \n", + "simple-rag-mixtral-8x7b-instruct 0.837302 0.817947 \n", + "simple-rag-llama-3-70b 0.783730 0.822221 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.765873 0.805604 \n", + "simple-rag-gemma2-9b-it 0.783730 0.829120 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.789683 0.807241 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.761905 0.787492 \n", + "simple-rag-mistral-7b-instruct 0.819444 0.822030 \n", + "simple-rag-claude-3.5-sonnet 0.773810 0.814201 \n", + "simple-rag-gpt-4o-mini 0.813492 0.826372 \n", + "simple-rag-claude-3-sonnet 0.765873 0.790311 \n", + "mixture-rag-gemma2-9b-it-modified 0.813492 0.800431 \n", + "simple-rag-claude-3-haiku 0.759921 0.776649 \n", + "simple-rag-gpt-4-turbo 0.771825 0.788012 \n", + "mixture-rag-llama3.1-8b-instruct 0.803571 0.763485 \n", + "mixture-rag-mixtral-8x7-instruct 0.823413 0.773787 \n", + "mixture-rag-gemma2-9b-it 0.793651 0.760464 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.811508 0.772309 \n", + "mixture-rag-claude-3-haiku-modified 0.744048 0.735848 \n", + "mixture-rag-claude-3-haiku 0.793651 0.741512 \n", + "mixture-rag-claude-3-haiku-thought 0.730159 0.688896 " + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by faithfulness by descending order\n", + "dataframe_1_mean.sort_values(by=\"faithfulness\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
experiment_name
simple-rag-gemma2-9b-it0.8148560.8887740.7837300.829120
simple-rag-gpt-4o-mini0.7804920.8851330.8134920.826372
simple-rag-claude-3.5-sonnet0.7981580.8706350.7738100.814201
simple-rag-llama-3-70b0.8177430.8651890.7837300.822221
simple-rag-claude-3-opus0.8369470.8609460.7182540.805382
mixture-rag-gemma2-9b-it-thought0.8640670.8572160.7996030.840295
simple-rag-gpt-4o0.8733520.8521220.8253970.850290
simple-rag-mistral-7b-instruct0.7989870.8476590.8194440.822030
simple-rag-gpt-4-turbo0.7449520.8472590.7718250.788012
mixture-rag-llama3.1-8b-instruct-modified0.6615410.8438790.8115080.772309
simple-rag-llama-3.1-405b-instruct0.9057620.8410260.8075400.851443
simple-rag-llama-3.1-70b-instruct0.9031280.8397520.8055560.849478
mixture-rag-llama3.1-8b-instruct-thought0.8161860.8347540.7658730.805604
simple-rag-gemma-7b-it0.8734600.8347100.7916670.833279
mixture-rag-claude-3-haiku-modified0.6294210.8340740.7440480.735848
mixture-rag-mixtral-8x7-instruct-modified0.8005640.8314770.7896830.807241
simple-rag-claude-3-sonnet0.7778530.8272060.7658730.790311
simple-rag-claude-3-haiku0.7481640.8218640.7599210.776649
mixture-rag-gemma2-9b-it0.6667290.8210130.7936510.760464
mixture-rag-claude-3-haiku0.6101960.8206880.7936510.741512
mixture-rag-gemma2-9b-it-modified0.7683180.8194830.8134920.800431
simple-rag-llama-3-8b0.8871170.8089320.8095240.835191
mixture-rag-mixtral-8x7-instruct-thought0.7990120.8015600.7619050.787492
mixture-rag-mixtral-8x7-instruct0.7024590.7954910.8234130.773787
simple-rag-llama-3.1-8b0.9222220.7924260.7817460.832132
simple-rag-mixtral-8x7b-instruct0.8353740.7811650.8373020.817947
mixture-rag-llama3.1-8b-instruct0.7107960.7760890.8035710.763485
mixture-rag-claude-3-haiku-thought0.6054630.7310660.7301590.688896
\n", + "
" + ], + "text/plain": [ + " faithfulness answer_relevancy \\\n", + "experiment_name \n", + "simple-rag-gemma2-9b-it 0.814856 0.888774 \n", + "simple-rag-gpt-4o-mini 0.780492 0.885133 \n", + "simple-rag-claude-3.5-sonnet 0.798158 0.870635 \n", + "simple-rag-llama-3-70b 0.817743 0.865189 \n", + "simple-rag-claude-3-opus 0.836947 0.860946 \n", + "mixture-rag-gemma2-9b-it-thought 0.864067 0.857216 \n", + "simple-rag-gpt-4o 0.873352 0.852122 \n", + "simple-rag-mistral-7b-instruct 0.798987 0.847659 \n", + "simple-rag-gpt-4-turbo 0.744952 0.847259 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.661541 0.843879 \n", + "simple-rag-llama-3.1-405b-instruct 0.905762 0.841026 \n", + "simple-rag-llama-3.1-70b-instruct 0.903128 0.839752 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.816186 0.834754 \n", + "simple-rag-gemma-7b-it 0.873460 0.834710 \n", + "mixture-rag-claude-3-haiku-modified 0.629421 0.834074 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.800564 0.831477 \n", + "simple-rag-claude-3-sonnet 0.777853 0.827206 \n", + "simple-rag-claude-3-haiku 0.748164 0.821864 \n", + "mixture-rag-gemma2-9b-it 0.666729 0.821013 \n", + "mixture-rag-claude-3-haiku 0.610196 0.820688 \n", + "mixture-rag-gemma2-9b-it-modified 0.768318 0.819483 \n", + "simple-rag-llama-3-8b 0.887117 0.808932 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.799012 0.801560 \n", + "mixture-rag-mixtral-8x7-instruct 0.702459 0.795491 \n", + "simple-rag-llama-3.1-8b 0.922222 0.792426 \n", + "simple-rag-mixtral-8x7b-instruct 0.835374 0.781165 \n", + "mixture-rag-llama3.1-8b-instruct 0.710796 0.776089 \n", + "mixture-rag-claude-3-haiku-thought 0.605463 0.731066 \n", + "\n", + " context_utilization score \n", + "experiment_name \n", + "simple-rag-gemma2-9b-it 0.783730 0.829120 \n", + "simple-rag-gpt-4o-mini 0.813492 0.826372 \n", + "simple-rag-claude-3.5-sonnet 0.773810 0.814201 \n", + "simple-rag-llama-3-70b 0.783730 0.822221 \n", + "simple-rag-claude-3-opus 0.718254 0.805382 \n", + "mixture-rag-gemma2-9b-it-thought 0.799603 0.840295 \n", + "simple-rag-gpt-4o 0.825397 0.850290 \n", + "simple-rag-mistral-7b-instruct 0.819444 0.822030 \n", + "simple-rag-gpt-4-turbo 0.771825 0.788012 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.811508 0.772309 \n", + "simple-rag-llama-3.1-405b-instruct 0.807540 0.851443 \n", + "simple-rag-llama-3.1-70b-instruct 0.805556 0.849478 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.765873 0.805604 \n", + "simple-rag-gemma-7b-it 0.791667 0.833279 \n", + "mixture-rag-claude-3-haiku-modified 0.744048 0.735848 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.789683 0.807241 \n", + "simple-rag-claude-3-sonnet 0.765873 0.790311 \n", + "simple-rag-claude-3-haiku 0.759921 0.776649 \n", + "mixture-rag-gemma2-9b-it 0.793651 0.760464 \n", + "mixture-rag-claude-3-haiku 0.793651 0.741512 \n", + "mixture-rag-gemma2-9b-it-modified 0.813492 0.800431 \n", + "simple-rag-llama-3-8b 0.809524 0.835191 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.761905 0.787492 \n", + "mixture-rag-mixtral-8x7-instruct 0.823413 0.773787 \n", + "simple-rag-llama-3.1-8b 0.781746 0.832132 \n", + "simple-rag-mixtral-8x7b-instruct 0.837302 0.817947 \n", + "mixture-rag-llama3.1-8b-instruct 0.803571 0.763485 \n", + "mixture-rag-claude-3-haiku-thought 0.730159 0.688896 " + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by answer_relevancy by descending order\n", + "dataframe_1_mean.sort_values(by=\"answer_relevancy\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
experiment_name
simple-rag-mixtral-8x7b-instruct0.8353740.7811650.8373020.817947
simple-rag-gpt-4o0.8733520.8521220.8253970.850290
mixture-rag-mixtral-8x7-instruct0.7024590.7954910.8234130.773787
simple-rag-mistral-7b-instruct0.7989870.8476590.8194440.822030
simple-rag-gpt-4o-mini0.7804920.8851330.8134920.826372
mixture-rag-gemma2-9b-it-modified0.7683180.8194830.8134920.800431
mixture-rag-llama3.1-8b-instruct-modified0.6615410.8438790.8115080.772309
simple-rag-llama-3-8b0.8871170.8089320.8095240.835191
simple-rag-llama-3.1-405b-instruct0.9057620.8410260.8075400.851443
simple-rag-llama-3.1-70b-instruct0.9031280.8397520.8055560.849478
mixture-rag-llama3.1-8b-instruct0.7107960.7760890.8035710.763485
mixture-rag-gemma2-9b-it-thought0.8640670.8572160.7996030.840295
mixture-rag-claude-3-haiku0.6101960.8206880.7936510.741512
mixture-rag-gemma2-9b-it0.6667290.8210130.7936510.760464
simple-rag-gemma-7b-it0.8734600.8347100.7916670.833279
mixture-rag-mixtral-8x7-instruct-modified0.8005640.8314770.7896830.807241
simple-rag-llama-3-70b0.8177430.8651890.7837300.822221
simple-rag-gemma2-9b-it0.8148560.8887740.7837300.829120
simple-rag-llama-3.1-8b0.9222220.7924260.7817460.832132
simple-rag-claude-3.5-sonnet0.7981580.8706350.7738100.814201
simple-rag-gpt-4-turbo0.7449520.8472590.7718250.788012
mixture-rag-llama3.1-8b-instruct-thought0.8161860.8347540.7658730.805604
simple-rag-claude-3-sonnet0.7778530.8272060.7658730.790311
mixture-rag-mixtral-8x7-instruct-thought0.7990120.8015600.7619050.787492
simple-rag-claude-3-haiku0.7481640.8218640.7599210.776649
mixture-rag-claude-3-haiku-modified0.6294210.8340740.7440480.735848
mixture-rag-claude-3-haiku-thought0.6054630.7310660.7301590.688896
simple-rag-claude-3-opus0.8369470.8609460.7182540.805382
\n", + "
" + ], + "text/plain": [ + " faithfulness answer_relevancy \\\n", + "experiment_name \n", + "simple-rag-mixtral-8x7b-instruct 0.835374 0.781165 \n", + "simple-rag-gpt-4o 0.873352 0.852122 \n", + "mixture-rag-mixtral-8x7-instruct 0.702459 0.795491 \n", + "simple-rag-mistral-7b-instruct 0.798987 0.847659 \n", + "simple-rag-gpt-4o-mini 0.780492 0.885133 \n", + "mixture-rag-gemma2-9b-it-modified 0.768318 0.819483 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.661541 0.843879 \n", + "simple-rag-llama-3-8b 0.887117 0.808932 \n", + "simple-rag-llama-3.1-405b-instruct 0.905762 0.841026 \n", + "simple-rag-llama-3.1-70b-instruct 0.903128 0.839752 \n", + "mixture-rag-llama3.1-8b-instruct 0.710796 0.776089 \n", + "mixture-rag-gemma2-9b-it-thought 0.864067 0.857216 \n", + "mixture-rag-claude-3-haiku 0.610196 0.820688 \n", + "mixture-rag-gemma2-9b-it 0.666729 0.821013 \n", + "simple-rag-gemma-7b-it 0.873460 0.834710 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.800564 0.831477 \n", + "simple-rag-llama-3-70b 0.817743 0.865189 \n", + "simple-rag-gemma2-9b-it 0.814856 0.888774 \n", + "simple-rag-llama-3.1-8b 0.922222 0.792426 \n", + "simple-rag-claude-3.5-sonnet 0.798158 0.870635 \n", + "simple-rag-gpt-4-turbo 0.744952 0.847259 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.816186 0.834754 \n", + "simple-rag-claude-3-sonnet 0.777853 0.827206 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.799012 0.801560 \n", + "simple-rag-claude-3-haiku 0.748164 0.821864 \n", + "mixture-rag-claude-3-haiku-modified 0.629421 0.834074 \n", + "mixture-rag-claude-3-haiku-thought 0.605463 0.731066 \n", + "simple-rag-claude-3-opus 0.836947 0.860946 \n", + "\n", + " context_utilization score \n", + "experiment_name \n", + "simple-rag-mixtral-8x7b-instruct 0.837302 0.817947 \n", + "simple-rag-gpt-4o 0.825397 0.850290 \n", + "mixture-rag-mixtral-8x7-instruct 0.823413 0.773787 \n", + "simple-rag-mistral-7b-instruct 0.819444 0.822030 \n", + "simple-rag-gpt-4o-mini 0.813492 0.826372 \n", + "mixture-rag-gemma2-9b-it-modified 0.813492 0.800431 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.811508 0.772309 \n", + "simple-rag-llama-3-8b 0.809524 0.835191 \n", + "simple-rag-llama-3.1-405b-instruct 0.807540 0.851443 \n", + "simple-rag-llama-3.1-70b-instruct 0.805556 0.849478 \n", + "mixture-rag-llama3.1-8b-instruct 0.803571 0.763485 \n", + "mixture-rag-gemma2-9b-it-thought 0.799603 0.840295 \n", + "mixture-rag-claude-3-haiku 0.793651 0.741512 \n", + "mixture-rag-gemma2-9b-it 0.793651 0.760464 \n", + "simple-rag-gemma-7b-it 0.791667 0.833279 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.789683 0.807241 \n", + "simple-rag-llama-3-70b 0.783730 0.822221 \n", + "simple-rag-gemma2-9b-it 0.783730 0.829120 \n", + "simple-rag-llama-3.1-8b 0.781746 0.832132 \n", + "simple-rag-claude-3.5-sonnet 0.773810 0.814201 \n", + "simple-rag-gpt-4-turbo 0.771825 0.788012 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.765873 0.805604 \n", + "simple-rag-claude-3-sonnet 0.765873 0.790311 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.761905 0.787492 \n", + "simple-rag-claude-3-haiku 0.759921 0.776649 \n", + "mixture-rag-claude-3-haiku-modified 0.744048 0.735848 \n", + "mixture-rag-claude-3-haiku-thought 0.730159 0.688896 \n", + "simple-rag-claude-3-opus 0.718254 0.805382 " + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by context_utilization by descending order\n", + "dataframe_1_mean.sort_values(by=\"context_utilization\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
experiment_name
simple-rag-llama-3.1-405b-instruct0.9057620.8410260.8075400.851443
simple-rag-gpt-4o0.8733520.8521220.8253970.850290
simple-rag-llama-3.1-70b-instruct0.9031280.8397520.8055560.849478
mixture-rag-gemma2-9b-it-thought0.8640670.8572160.7996030.840295
simple-rag-llama-3-8b0.8871170.8089320.8095240.835191
simple-rag-gemma-7b-it0.8734600.8347100.7916670.833279
simple-rag-llama-3.1-8b0.9222220.7924260.7817460.832132
simple-rag-gemma2-9b-it0.8148560.8887740.7837300.829120
simple-rag-gpt-4o-mini0.7804920.8851330.8134920.826372
simple-rag-llama-3-70b0.8177430.8651890.7837300.822221
simple-rag-mistral-7b-instruct0.7989870.8476590.8194440.822030
simple-rag-mixtral-8x7b-instruct0.8353740.7811650.8373020.817947
simple-rag-claude-3.5-sonnet0.7981580.8706350.7738100.814201
mixture-rag-mixtral-8x7-instruct-modified0.8005640.8314770.7896830.807241
mixture-rag-llama3.1-8b-instruct-thought0.8161860.8347540.7658730.805604
simple-rag-claude-3-opus0.8369470.8609460.7182540.805382
mixture-rag-gemma2-9b-it-modified0.7683180.8194830.8134920.800431
simple-rag-claude-3-sonnet0.7778530.8272060.7658730.790311
simple-rag-gpt-4-turbo0.7449520.8472590.7718250.788012
mixture-rag-mixtral-8x7-instruct-thought0.7990120.8015600.7619050.787492
simple-rag-claude-3-haiku0.7481640.8218640.7599210.776649
mixture-rag-mixtral-8x7-instruct0.7024590.7954910.8234130.773787
mixture-rag-llama3.1-8b-instruct-modified0.6615410.8438790.8115080.772309
mixture-rag-llama3.1-8b-instruct0.7107960.7760890.8035710.763485
mixture-rag-gemma2-9b-it0.6667290.8210130.7936510.760464
mixture-rag-claude-3-haiku0.6101960.8206880.7936510.741512
mixture-rag-claude-3-haiku-modified0.6294210.8340740.7440480.735848
mixture-rag-claude-3-haiku-thought0.6054630.7310660.7301590.688896
\n", + "
" + ], + "text/plain": [ + " faithfulness answer_relevancy \\\n", + "experiment_name \n", + "simple-rag-llama-3.1-405b-instruct 0.905762 0.841026 \n", + "simple-rag-gpt-4o 0.873352 0.852122 \n", + "simple-rag-llama-3.1-70b-instruct 0.903128 0.839752 \n", + "mixture-rag-gemma2-9b-it-thought 0.864067 0.857216 \n", + "simple-rag-llama-3-8b 0.887117 0.808932 \n", + "simple-rag-gemma-7b-it 0.873460 0.834710 \n", + "simple-rag-llama-3.1-8b 0.922222 0.792426 \n", + "simple-rag-gemma2-9b-it 0.814856 0.888774 \n", + "simple-rag-gpt-4o-mini 0.780492 0.885133 \n", + "simple-rag-llama-3-70b 0.817743 0.865189 \n", + "simple-rag-mistral-7b-instruct 0.798987 0.847659 \n", + "simple-rag-mixtral-8x7b-instruct 0.835374 0.781165 \n", + "simple-rag-claude-3.5-sonnet 0.798158 0.870635 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.800564 0.831477 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.816186 0.834754 \n", + "simple-rag-claude-3-opus 0.836947 0.860946 \n", + "mixture-rag-gemma2-9b-it-modified 0.768318 0.819483 \n", + "simple-rag-claude-3-sonnet 0.777853 0.827206 \n", + "simple-rag-gpt-4-turbo 0.744952 0.847259 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.799012 0.801560 \n", + "simple-rag-claude-3-haiku 0.748164 0.821864 \n", + "mixture-rag-mixtral-8x7-instruct 0.702459 0.795491 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.661541 0.843879 \n", + "mixture-rag-llama3.1-8b-instruct 0.710796 0.776089 \n", + "mixture-rag-gemma2-9b-it 0.666729 0.821013 \n", + "mixture-rag-claude-3-haiku 0.610196 0.820688 \n", + "mixture-rag-claude-3-haiku-modified 0.629421 0.834074 \n", + "mixture-rag-claude-3-haiku-thought 0.605463 0.731066 \n", + "\n", + " context_utilization score \n", + "experiment_name \n", + "simple-rag-llama-3.1-405b-instruct 0.807540 0.851443 \n", + "simple-rag-gpt-4o 0.825397 0.850290 \n", + "simple-rag-llama-3.1-70b-instruct 0.805556 0.849478 \n", + "mixture-rag-gemma2-9b-it-thought 0.799603 0.840295 \n", + "simple-rag-llama-3-8b 0.809524 0.835191 \n", + "simple-rag-gemma-7b-it 0.791667 0.833279 \n", + "simple-rag-llama-3.1-8b 0.781746 0.832132 \n", + "simple-rag-gemma2-9b-it 0.783730 0.829120 \n", + "simple-rag-gpt-4o-mini 0.813492 0.826372 \n", + "simple-rag-llama-3-70b 0.783730 0.822221 \n", + "simple-rag-mistral-7b-instruct 0.819444 0.822030 \n", + "simple-rag-mixtral-8x7b-instruct 0.837302 0.817947 \n", + "simple-rag-claude-3.5-sonnet 0.773810 0.814201 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.789683 0.807241 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.765873 0.805604 \n", + "simple-rag-claude-3-opus 0.718254 0.805382 \n", + "mixture-rag-gemma2-9b-it-modified 0.813492 0.800431 \n", + "simple-rag-claude-3-sonnet 0.765873 0.790311 \n", + "simple-rag-gpt-4-turbo 0.771825 0.788012 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.761905 0.787492 \n", + "simple-rag-claude-3-haiku 0.759921 0.776649 \n", + "mixture-rag-mixtral-8x7-instruct 0.823413 0.773787 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.811508 0.772309 \n", + "mixture-rag-llama3.1-8b-instruct 0.803571 0.763485 \n", + "mixture-rag-gemma2-9b-it 0.793651 0.760464 \n", + "mixture-rag-claude-3-haiku 0.793651 0.741512 \n", + "mixture-rag-claude-3-haiku-modified 0.744048 0.735848 \n", + "mixture-rag-claude-3-haiku-thought 0.730159 0.688896 " + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by score(mean of all the metric scores on experiment level) by descending order\n", + "dataframe_1_mean.sort_values(by=\"score\", ascending=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis based on the second approach\n", + "\n", + "The steps for the second approach are:\n", + "- Create a copy of the data\n", + "- Calculate the average score for each metrics per question(row)\n", + "- Check if the scores are created correctly\n", + "- Group the scores by question and calculate the average score for each question\n", + "- Drop the 4 questions with the lowest scores\n", + "- Do the same steps as in the first approach with the new data" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a copy of the dataframe\n", + "dataframe_2 = dataframe.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a score for each row by calculating the mean of the scores for each row (faithfulness, answer_relevancy, context_utilization)\n", + "dataframe_2[\"score\"] = dataframe_2[\n", + " [\"faithfulness\", \"answer_relevancy\", \"context_utilization\"]\n", + "].mean(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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experiment_nametrace_idquestionanswerfaithfulnessanswer_relevancycontext_utilizationscore
0mixture-rag-claude-3-haiku-thought5d7ae2d3-f2b8-4840-b877-69165f991599How can attention be described in the Transfor...The response from the second model provides th...0.7272730.7230331.0000000.816768
1mixture-rag-claude-3-haiku-thoughtaa2067f5-33f7-4d70-b4c9-f1752084c8aeWhat is Mixture of Agents?The response from the third model provides the...0.5555560.4661290.8055560.609080
2mixture-rag-claude-3-haiku-thoughtcefa79c4-cba0-4961-bc87-005e2c2b8837Is Mixtral based on the idea of a mixture of e...Based on the provided responses, the best resp...0.7500000.6362651.0000000.795422
3mixture-rag-claude-3-haiku-thought8f2ee9a4-72d8-4956-8131-fa0ed9bce4a0What is sliding window attention?The response from the first model provides the...0.5714290.6911741.0000000.754201
4mixture-rag-claude-3-haiku-thought584e89e1-cc11-4101-8c96-f10cb725fa15How many stages are there in the development o...The response from the second model provides th...1.0000000.9385621.0000000.979521
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" + ], + "text/plain": [ + " experiment_name trace_id \\\n", + "0 mixture-rag-claude-3-haiku-thought 5d7ae2d3-f2b8-4840-b877-69165f991599 \n", + "1 mixture-rag-claude-3-haiku-thought aa2067f5-33f7-4d70-b4c9-f1752084c8ae \n", + "2 mixture-rag-claude-3-haiku-thought cefa79c4-cba0-4961-bc87-005e2c2b8837 \n", + "3 mixture-rag-claude-3-haiku-thought 8f2ee9a4-72d8-4956-8131-fa0ed9bce4a0 \n", + "4 mixture-rag-claude-3-haiku-thought 584e89e1-cc11-4101-8c96-f10cb725fa15 \n", + "\n", + " question \\\n", + "0 How can attention be described in the Transfor... \n", + "1 What is Mixture of Agents? \n", + "2 Is Mixtral based on the idea of a mixture of e... \n", + "3 What is sliding window attention? \n", + "4 How many stages are there in the development o... \n", + "\n", + " answer faithfulness \\\n", + "0 The response from the second model provides th... 0.727273 \n", + "1 The response from the third model provides the... 0.555556 \n", + "2 Based on the provided responses, the best resp... 0.750000 \n", + "3 The response from the first model provides the... 0.571429 \n", + "4 The response from the second model provides th... 1.000000 \n", + "\n", + " answer_relevancy context_utilization score \n", + "0 0.723033 1.000000 0.816768 \n", + "1 0.466129 0.805556 0.609080 \n", + "2 0.636265 1.000000 0.795422 \n", + "3 0.691174 1.000000 0.754201 \n", + "4 0.938562 1.000000 0.979521 " + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Checking the new dataframe\n", + "dataframe_2.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a new dataframe by grouping the dataframe by question and calculating the mean of the scores for each question\n", + "dataframe_2_mean = (\n", + " dataframe_2.drop(columns=[\"trace_id\", \"answer\", \"experiment_name\"])\n", + " .groupby(\"question\")\n", + " .mean()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
question
How many stages are there in the development of the Llama 3 model?0.9228080.8943261.0000000.939045
Does Claude 3 models have vision capabilities?0.9376030.9739320.8660710.925869
Can the GPT-4 model accept both text and image inputs?0.8624460.9175520.8750000.884999
On what architecture the Gemma model is based on?0.6023250.9846351.0000000.862320
What is the difference between the Llama 2 and Llama 2-Chat ?0.8133900.9460630.8144840.857979
Is Mixtral based on the idea of a mixture of experts?0.8772410.6886041.0000000.855282
How many stages of training are in the GPT model?0.8050570.7399091.0000000.848322
What tokenizer is used in the Gemma2 model?0.8863170.9708790.5000000.785732
What is Mixture of Agents?0.8588510.5878870.8650790.770606
What are the two tasks in BERT?0.6566630.9362830.7123020.768416
How can attention be described in the Transformer?0.6850800.7229520.8234130.743815
What is sliding window attention?0.6363890.6415800.9464290.741466
What is optimizer is used for LLaMA?0.7659810.8307200.3333330.643345
On what architecture the GPT-3 model is based on?0.6665070.7804710.3035710.583516
\n", + "
" + ], + "text/plain": [ + " faithfulness \\\n", + "question \n", + "How many stages are there in the development of... 0.922808 \n", + "Does Claude 3 models have vision capabilities? 0.937603 \n", + "Can the GPT-4 model accept both text and image ... 0.862446 \n", + "On what architecture the Gemma model is based on? 0.602325 \n", + "What is the difference between the Llama 2 and ... 0.813390 \n", + "Is Mixtral based on the idea of a mixture of ex... 0.877241 \n", + "How many stages of training are in the GPT model? 0.805057 \n", + "What tokenizer is used in the Gemma2 model? 0.886317 \n", + "What is Mixture of Agents? 0.858851 \n", + "What are the two tasks in BERT? 0.656663 \n", + "How can attention be described in the Transformer? 0.685080 \n", + "What is sliding window attention? 0.636389 \n", + "What is optimizer is used for LLaMA? 0.765981 \n", + "On what architecture the GPT-3 model is based on? 0.666507 \n", + "\n", + " answer_relevancy \\\n", + "question \n", + "How many stages are there in the development of... 0.894326 \n", + "Does Claude 3 models have vision capabilities? 0.973932 \n", + "Can the GPT-4 model accept both text and image ... 0.917552 \n", + "On what architecture the Gemma model is based on? 0.984635 \n", + "What is the difference between the Llama 2 and ... 0.946063 \n", + "Is Mixtral based on the idea of a mixture of ex... 0.688604 \n", + "How many stages of training are in the GPT model? 0.739909 \n", + "What tokenizer is used in the Gemma2 model? 0.970879 \n", + "What is Mixture of Agents? 0.587887 \n", + "What are the two tasks in BERT? 0.936283 \n", + "How can attention be described in the Transformer? 0.722952 \n", + "What is sliding window attention? 0.641580 \n", + "What is optimizer is used for LLaMA? 0.830720 \n", + "On what architecture the GPT-3 model is based on? 0.780471 \n", + "\n", + " context_utilization \\\n", + "question \n", + "How many stages are there in the development of... 1.000000 \n", + "Does Claude 3 models have vision capabilities? 0.866071 \n", + "Can the GPT-4 model accept both text and image ... 0.875000 \n", + "On what architecture the Gemma model is based on? 1.000000 \n", + "What is the difference between the Llama 2 and ... 0.814484 \n", + "Is Mixtral based on the idea of a mixture of ex... 1.000000 \n", + "How many stages of training are in the GPT model? 1.000000 \n", + "What tokenizer is used in the Gemma2 model? 0.500000 \n", + "What is Mixture of Agents? 0.865079 \n", + "What are the two tasks in BERT? 0.712302 \n", + "How can attention be described in the Transformer? 0.823413 \n", + "What is sliding window attention? 0.946429 \n", + "What is optimizer is used for LLaMA? 0.333333 \n", + "On what architecture the GPT-3 model is based on? 0.303571 \n", + "\n", + " score \n", + "question \n", + "How many stages are there in the development of... 0.939045 \n", + "Does Claude 3 models have vision capabilities? 0.925869 \n", + "Can the GPT-4 model accept both text and image ... 0.884999 \n", + "On what architecture the Gemma model is based on? 0.862320 \n", + "What is the difference between the Llama 2 and ... 0.857979 \n", + "Is Mixtral based on the idea of a mixture of ex... 0.855282 \n", + "How many stages of training are in the GPT model? 0.848322 \n", + "What tokenizer is used in the Gemma2 model? 0.785732 \n", + "What is Mixture of Agents? 0.770606 \n", + "What are the two tasks in BERT? 0.768416 \n", + "How can attention be described in the Transformer? 0.743815 \n", + "What is sliding window attention? 0.741466 \n", + "What is optimizer is used for LLaMA? 0.643345 \n", + "On what architecture the GPT-3 model is based on? 0.583516 " + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by score by descending order\n", + "dataframe_2_mean.sort_values(by=\"score\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a copy of the dataframe\n", + "dataframe_3 = dataframe.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "# From the dataframe, excluding the questions that are not relevant for the analysis\n", + "questions_to_exclude = [\n", + " \"What is optimizer is used for LLaMA?\",\n", + " \"On what architecture the GPT-3 model is based on?\",\n", + " \"What is sliding window attention?\",\n", + " \"How can attention be described in the Transformer?\",\n", + "]\n", + "\n", + "dataframe_3_filtered = dataframe_3[~dataframe_3[\"question\"].isin(questions_to_exclude)]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a dataframe with mean values for the scores for each experiment\n", + "dataframe_3_mean = (\n", + " dataframe_3_filtered.drop(columns=[\"trace_id\", \"question\", \"answer\"])\n", + " .groupby(\"experiment_name\")\n", + " .mean()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a score for each row by calculating the mean of the scores for each row (faithfulness, answer_relevancy, context_utilization)\n", + "dataframe_3_mean[\"score\"] = dataframe_3_mean[\n", + " [\"faithfulness\", \"answer_relevancy\", \"context_utilization\"]\n", + "].mean(axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
experiment_name
simple-rag-llama-3.1-70b-instruct0.9612310.8449460.8638890.890022
simple-rag-llama-3.1-8b0.9577780.8226760.8805560.887003
simple-rag-llama-3.1-405b-instruct0.9456410.8468770.8972220.896580
mixture-rag-gemma2-9b-it-thought0.9245420.9104760.8805560.905191
simple-rag-gemma-7b-it0.9236770.8636690.8750000.887449
simple-rag-llama-3-8b0.9132140.8561650.8750000.881460
simple-rag-llama-3-70b0.9011360.8853280.8638890.883451
simple-rag-mixtral-8x7b-instruct0.8964470.8843690.9083330.896383
simple-rag-gpt-4o0.8953550.8841280.8972220.892235
mixture-rag-mixtral-8x7-instruct-thought0.8927270.7940750.8333330.840045
mixture-rag-mixtral-8x7-instruct-modified0.8821970.8595170.9166670.886127
simple-rag-mistral-7b-instruct0.8780270.9145970.9083330.900319
simple-rag-claude-3-opus0.8671060.8910540.7722220.843461
simple-rag-gpt-4o-mini0.8517860.9183470.9000000.890044
simple-rag-gemma2-9b-it0.8462120.9053050.8638890.871802
mixture-rag-gemma2-9b-it-modified0.8252080.8677290.8805560.857831
simple-rag-claude-3.5-sonnet0.8218400.9053300.8250000.850723
mixture-rag-llama3.1-8b-instruct-thought0.8200000.8977260.8388890.852205
simple-rag-gpt-4-turbo0.8187580.8605090.8222220.833829
simple-rag-claude-3-sonnet0.8179310.8743340.8138890.835385
simple-rag-claude-3-haiku0.7773410.8656610.8305560.824519
mixture-rag-mixtral-8x7-instruct0.7457500.8457670.9138890.835136
mixture-rag-llama3.1-8b-instruct0.7353520.8066520.9166670.819557
mixture-rag-llama3.1-8b-instruct-modified0.7098210.8716860.8972220.826243
mixture-rag-gemma2-9b-it0.6431560.8618260.8527780.785920
mixture-rag-claude-3-haiku0.5949210.8440300.8722220.770391
mixture-rag-claude-3-haiku-thought0.5927780.7470400.7638890.701235
mixture-rag-claude-3-haiku-modified0.5836370.8623780.8083330.751449
\n", + "
" + ], + "text/plain": [ + " faithfulness answer_relevancy \\\n", + "experiment_name \n", + "simple-rag-llama-3.1-70b-instruct 0.961231 0.844946 \n", + "simple-rag-llama-3.1-8b 0.957778 0.822676 \n", + "simple-rag-llama-3.1-405b-instruct 0.945641 0.846877 \n", + "mixture-rag-gemma2-9b-it-thought 0.924542 0.910476 \n", + "simple-rag-gemma-7b-it 0.923677 0.863669 \n", + "simple-rag-llama-3-8b 0.913214 0.856165 \n", + "simple-rag-llama-3-70b 0.901136 0.885328 \n", + "simple-rag-mixtral-8x7b-instruct 0.896447 0.884369 \n", + "simple-rag-gpt-4o 0.895355 0.884128 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.892727 0.794075 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.882197 0.859517 \n", + "simple-rag-mistral-7b-instruct 0.878027 0.914597 \n", + "simple-rag-claude-3-opus 0.867106 0.891054 \n", + "simple-rag-gpt-4o-mini 0.851786 0.918347 \n", + "simple-rag-gemma2-9b-it 0.846212 0.905305 \n", + "mixture-rag-gemma2-9b-it-modified 0.825208 0.867729 \n", + "simple-rag-claude-3.5-sonnet 0.821840 0.905330 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.820000 0.897726 \n", + "simple-rag-gpt-4-turbo 0.818758 0.860509 \n", + "simple-rag-claude-3-sonnet 0.817931 0.874334 \n", + "simple-rag-claude-3-haiku 0.777341 0.865661 \n", + "mixture-rag-mixtral-8x7-instruct 0.745750 0.845767 \n", + "mixture-rag-llama3.1-8b-instruct 0.735352 0.806652 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.709821 0.871686 \n", + "mixture-rag-gemma2-9b-it 0.643156 0.861826 \n", + "mixture-rag-claude-3-haiku 0.594921 0.844030 \n", + "mixture-rag-claude-3-haiku-thought 0.592778 0.747040 \n", + "mixture-rag-claude-3-haiku-modified 0.583637 0.862378 \n", + "\n", + " context_utilization score \n", + "experiment_name \n", + "simple-rag-llama-3.1-70b-instruct 0.863889 0.890022 \n", + "simple-rag-llama-3.1-8b 0.880556 0.887003 \n", + "simple-rag-llama-3.1-405b-instruct 0.897222 0.896580 \n", + "mixture-rag-gemma2-9b-it-thought 0.880556 0.905191 \n", + "simple-rag-gemma-7b-it 0.875000 0.887449 \n", + "simple-rag-llama-3-8b 0.875000 0.881460 \n", + "simple-rag-llama-3-70b 0.863889 0.883451 \n", + "simple-rag-mixtral-8x7b-instruct 0.908333 0.896383 \n", + "simple-rag-gpt-4o 0.897222 0.892235 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.833333 0.840045 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.916667 0.886127 \n", + "simple-rag-mistral-7b-instruct 0.908333 0.900319 \n", + "simple-rag-claude-3-opus 0.772222 0.843461 \n", + "simple-rag-gpt-4o-mini 0.900000 0.890044 \n", + "simple-rag-gemma2-9b-it 0.863889 0.871802 \n", + "mixture-rag-gemma2-9b-it-modified 0.880556 0.857831 \n", + "simple-rag-claude-3.5-sonnet 0.825000 0.850723 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.838889 0.852205 \n", + "simple-rag-gpt-4-turbo 0.822222 0.833829 \n", + "simple-rag-claude-3-sonnet 0.813889 0.835385 \n", + "simple-rag-claude-3-haiku 0.830556 0.824519 \n", + "mixture-rag-mixtral-8x7-instruct 0.913889 0.835136 \n", + "mixture-rag-llama3.1-8b-instruct 0.916667 0.819557 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.897222 0.826243 \n", + "mixture-rag-gemma2-9b-it 0.852778 0.785920 \n", + "mixture-rag-claude-3-haiku 0.872222 0.770391 \n", + "mixture-rag-claude-3-haiku-thought 0.763889 0.701235 \n", + "mixture-rag-claude-3-haiku-modified 0.808333 0.751449 " + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by faithfulness by descending order\n", + "dataframe_3_mean.sort_values(by=\"faithfulness\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
experiment_name
simple-rag-gpt-4o-mini0.8517860.9183470.9000000.890044
simple-rag-mistral-7b-instruct0.8780270.9145970.9083330.900319
mixture-rag-gemma2-9b-it-thought0.9245420.9104760.8805560.905191
simple-rag-claude-3.5-sonnet0.8218400.9053300.8250000.850723
simple-rag-gemma2-9b-it0.8462120.9053050.8638890.871802
mixture-rag-llama3.1-8b-instruct-thought0.8200000.8977260.8388890.852205
simple-rag-claude-3-opus0.8671060.8910540.7722220.843461
simple-rag-llama-3-70b0.9011360.8853280.8638890.883451
simple-rag-mixtral-8x7b-instruct0.8964470.8843690.9083330.896383
simple-rag-gpt-4o0.8953550.8841280.8972220.892235
simple-rag-claude-3-sonnet0.8179310.8743340.8138890.835385
mixture-rag-llama3.1-8b-instruct-modified0.7098210.8716860.8972220.826243
mixture-rag-gemma2-9b-it-modified0.8252080.8677290.8805560.857831
simple-rag-claude-3-haiku0.7773410.8656610.8305560.824519
simple-rag-gemma-7b-it0.9236770.8636690.8750000.887449
mixture-rag-claude-3-haiku-modified0.5836370.8623780.8083330.751449
mixture-rag-gemma2-9b-it0.6431560.8618260.8527780.785920
simple-rag-gpt-4-turbo0.8187580.8605090.8222220.833829
mixture-rag-mixtral-8x7-instruct-modified0.8821970.8595170.9166670.886127
simple-rag-llama-3-8b0.9132140.8561650.8750000.881460
simple-rag-llama-3.1-405b-instruct0.9456410.8468770.8972220.896580
mixture-rag-mixtral-8x7-instruct0.7457500.8457670.9138890.835136
simple-rag-llama-3.1-70b-instruct0.9612310.8449460.8638890.890022
mixture-rag-claude-3-haiku0.5949210.8440300.8722220.770391
simple-rag-llama-3.1-8b0.9577780.8226760.8805560.887003
mixture-rag-llama3.1-8b-instruct0.7353520.8066520.9166670.819557
mixture-rag-mixtral-8x7-instruct-thought0.8927270.7940750.8333330.840045
mixture-rag-claude-3-haiku-thought0.5927780.7470400.7638890.701235
\n", + "
" + ], + "text/plain": [ + " faithfulness answer_relevancy \\\n", + "experiment_name \n", + "simple-rag-gpt-4o-mini 0.851786 0.918347 \n", + "simple-rag-mistral-7b-instruct 0.878027 0.914597 \n", + "mixture-rag-gemma2-9b-it-thought 0.924542 0.910476 \n", + "simple-rag-claude-3.5-sonnet 0.821840 0.905330 \n", + "simple-rag-gemma2-9b-it 0.846212 0.905305 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.820000 0.897726 \n", + "simple-rag-claude-3-opus 0.867106 0.891054 \n", + "simple-rag-llama-3-70b 0.901136 0.885328 \n", + "simple-rag-mixtral-8x7b-instruct 0.896447 0.884369 \n", + "simple-rag-gpt-4o 0.895355 0.884128 \n", + "simple-rag-claude-3-sonnet 0.817931 0.874334 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.709821 0.871686 \n", + "mixture-rag-gemma2-9b-it-modified 0.825208 0.867729 \n", + "simple-rag-claude-3-haiku 0.777341 0.865661 \n", + "simple-rag-gemma-7b-it 0.923677 0.863669 \n", + "mixture-rag-claude-3-haiku-modified 0.583637 0.862378 \n", + "mixture-rag-gemma2-9b-it 0.643156 0.861826 \n", + "simple-rag-gpt-4-turbo 0.818758 0.860509 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.882197 0.859517 \n", + "simple-rag-llama-3-8b 0.913214 0.856165 \n", + "simple-rag-llama-3.1-405b-instruct 0.945641 0.846877 \n", + "mixture-rag-mixtral-8x7-instruct 0.745750 0.845767 \n", + "simple-rag-llama-3.1-70b-instruct 0.961231 0.844946 \n", + "mixture-rag-claude-3-haiku 0.594921 0.844030 \n", + "simple-rag-llama-3.1-8b 0.957778 0.822676 \n", + "mixture-rag-llama3.1-8b-instruct 0.735352 0.806652 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.892727 0.794075 \n", + "mixture-rag-claude-3-haiku-thought 0.592778 0.747040 \n", + "\n", + " context_utilization score \n", + "experiment_name \n", + "simple-rag-gpt-4o-mini 0.900000 0.890044 \n", + "simple-rag-mistral-7b-instruct 0.908333 0.900319 \n", + "mixture-rag-gemma2-9b-it-thought 0.880556 0.905191 \n", + "simple-rag-claude-3.5-sonnet 0.825000 0.850723 \n", + "simple-rag-gemma2-9b-it 0.863889 0.871802 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.838889 0.852205 \n", + "simple-rag-claude-3-opus 0.772222 0.843461 \n", + "simple-rag-llama-3-70b 0.863889 0.883451 \n", + "simple-rag-mixtral-8x7b-instruct 0.908333 0.896383 \n", + "simple-rag-gpt-4o 0.897222 0.892235 \n", + "simple-rag-claude-3-sonnet 0.813889 0.835385 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.897222 0.826243 \n", + "mixture-rag-gemma2-9b-it-modified 0.880556 0.857831 \n", + "simple-rag-claude-3-haiku 0.830556 0.824519 \n", + "simple-rag-gemma-7b-it 0.875000 0.887449 \n", + "mixture-rag-claude-3-haiku-modified 0.808333 0.751449 \n", + "mixture-rag-gemma2-9b-it 0.852778 0.785920 \n", + "simple-rag-gpt-4-turbo 0.822222 0.833829 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.916667 0.886127 \n", + "simple-rag-llama-3-8b 0.875000 0.881460 \n", + "simple-rag-llama-3.1-405b-instruct 0.897222 0.896580 \n", + "mixture-rag-mixtral-8x7-instruct 0.913889 0.835136 \n", + "simple-rag-llama-3.1-70b-instruct 0.863889 0.890022 \n", + "mixture-rag-claude-3-haiku 0.872222 0.770391 \n", + "simple-rag-llama-3.1-8b 0.880556 0.887003 \n", + "mixture-rag-llama3.1-8b-instruct 0.916667 0.819557 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.833333 0.840045 \n", + "mixture-rag-claude-3-haiku-thought 0.763889 0.701235 " + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by answer_relevancy by descending order\n", + "dataframe_3_mean.sort_values(by=\"answer_relevancy\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
experiment_name
mixture-rag-llama3.1-8b-instruct0.7353520.8066520.9166670.819557
mixture-rag-mixtral-8x7-instruct-modified0.8821970.8595170.9166670.886127
mixture-rag-mixtral-8x7-instruct0.7457500.8457670.9138890.835136
simple-rag-mixtral-8x7b-instruct0.8964470.8843690.9083330.896383
simple-rag-mistral-7b-instruct0.8780270.9145970.9083330.900319
simple-rag-gpt-4o-mini0.8517860.9183470.9000000.890044
mixture-rag-llama3.1-8b-instruct-modified0.7098210.8716860.8972220.826243
simple-rag-llama-3.1-405b-instruct0.9456410.8468770.8972220.896580
simple-rag-gpt-4o0.8953550.8841280.8972220.892235
mixture-rag-gemma2-9b-it-modified0.8252080.8677290.8805560.857831
mixture-rag-gemma2-9b-it-thought0.9245420.9104760.8805560.905191
simple-rag-llama-3.1-8b0.9577780.8226760.8805560.887003
simple-rag-gemma-7b-it0.9236770.8636690.8750000.887449
simple-rag-llama-3-8b0.9132140.8561650.8750000.881460
mixture-rag-claude-3-haiku0.5949210.8440300.8722220.770391
simple-rag-llama-3-70b0.9011360.8853280.8638890.883451
simple-rag-gemma2-9b-it0.8462120.9053050.8638890.871802
simple-rag-llama-3.1-70b-instruct0.9612310.8449460.8638890.890022
mixture-rag-gemma2-9b-it0.6431560.8618260.8527780.785920
mixture-rag-llama3.1-8b-instruct-thought0.8200000.8977260.8388890.852205
mixture-rag-mixtral-8x7-instruct-thought0.8927270.7940750.8333330.840045
simple-rag-claude-3-haiku0.7773410.8656610.8305560.824519
simple-rag-claude-3.5-sonnet0.8218400.9053300.8250000.850723
simple-rag-gpt-4-turbo0.8187580.8605090.8222220.833829
simple-rag-claude-3-sonnet0.8179310.8743340.8138890.835385
mixture-rag-claude-3-haiku-modified0.5836370.8623780.8083330.751449
simple-rag-claude-3-opus0.8671060.8910540.7722220.843461
mixture-rag-claude-3-haiku-thought0.5927780.7470400.7638890.701235
\n", + "
" + ], + "text/plain": [ + " faithfulness answer_relevancy \\\n", + "experiment_name \n", + "mixture-rag-llama3.1-8b-instruct 0.735352 0.806652 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.882197 0.859517 \n", + "mixture-rag-mixtral-8x7-instruct 0.745750 0.845767 \n", + "simple-rag-mixtral-8x7b-instruct 0.896447 0.884369 \n", + "simple-rag-mistral-7b-instruct 0.878027 0.914597 \n", + "simple-rag-gpt-4o-mini 0.851786 0.918347 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.709821 0.871686 \n", + "simple-rag-llama-3.1-405b-instruct 0.945641 0.846877 \n", + "simple-rag-gpt-4o 0.895355 0.884128 \n", + "mixture-rag-gemma2-9b-it-modified 0.825208 0.867729 \n", + "mixture-rag-gemma2-9b-it-thought 0.924542 0.910476 \n", + "simple-rag-llama-3.1-8b 0.957778 0.822676 \n", + "simple-rag-gemma-7b-it 0.923677 0.863669 \n", + "simple-rag-llama-3-8b 0.913214 0.856165 \n", + "mixture-rag-claude-3-haiku 0.594921 0.844030 \n", + "simple-rag-llama-3-70b 0.901136 0.885328 \n", + "simple-rag-gemma2-9b-it 0.846212 0.905305 \n", + "simple-rag-llama-3.1-70b-instruct 0.961231 0.844946 \n", + "mixture-rag-gemma2-9b-it 0.643156 0.861826 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.820000 0.897726 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.892727 0.794075 \n", + "simple-rag-claude-3-haiku 0.777341 0.865661 \n", + "simple-rag-claude-3.5-sonnet 0.821840 0.905330 \n", + "simple-rag-gpt-4-turbo 0.818758 0.860509 \n", + "simple-rag-claude-3-sonnet 0.817931 0.874334 \n", + "mixture-rag-claude-3-haiku-modified 0.583637 0.862378 \n", + "simple-rag-claude-3-opus 0.867106 0.891054 \n", + "mixture-rag-claude-3-haiku-thought 0.592778 0.747040 \n", + "\n", + " context_utilization score \n", + "experiment_name \n", + "mixture-rag-llama3.1-8b-instruct 0.916667 0.819557 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.916667 0.886127 \n", + "mixture-rag-mixtral-8x7-instruct 0.913889 0.835136 \n", + "simple-rag-mixtral-8x7b-instruct 0.908333 0.896383 \n", + "simple-rag-mistral-7b-instruct 0.908333 0.900319 \n", + "simple-rag-gpt-4o-mini 0.900000 0.890044 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.897222 0.826243 \n", + "simple-rag-llama-3.1-405b-instruct 0.897222 0.896580 \n", + "simple-rag-gpt-4o 0.897222 0.892235 \n", + "mixture-rag-gemma2-9b-it-modified 0.880556 0.857831 \n", + "mixture-rag-gemma2-9b-it-thought 0.880556 0.905191 \n", + "simple-rag-llama-3.1-8b 0.880556 0.887003 \n", + "simple-rag-gemma-7b-it 0.875000 0.887449 \n", + "simple-rag-llama-3-8b 0.875000 0.881460 \n", + "mixture-rag-claude-3-haiku 0.872222 0.770391 \n", + "simple-rag-llama-3-70b 0.863889 0.883451 \n", + "simple-rag-gemma2-9b-it 0.863889 0.871802 \n", + "simple-rag-llama-3.1-70b-instruct 0.863889 0.890022 \n", + "mixture-rag-gemma2-9b-it 0.852778 0.785920 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.838889 0.852205 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.833333 0.840045 \n", + "simple-rag-claude-3-haiku 0.830556 0.824519 \n", + "simple-rag-claude-3.5-sonnet 0.825000 0.850723 \n", + "simple-rag-gpt-4-turbo 0.822222 0.833829 \n", + "simple-rag-claude-3-sonnet 0.813889 0.835385 \n", + "mixture-rag-claude-3-haiku-modified 0.808333 0.751449 \n", + "simple-rag-claude-3-opus 0.772222 0.843461 \n", + "mixture-rag-claude-3-haiku-thought 0.763889 0.701235 " + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by context_utilization by descending order\n", + "dataframe_3_mean.sort_values(by=\"context_utilization\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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faithfulnessanswer_relevancycontext_utilizationscore
experiment_name
mixture-rag-gemma2-9b-it-thought0.9245420.9104760.8805560.905191
simple-rag-mistral-7b-instruct0.8780270.9145970.9083330.900319
simple-rag-llama-3.1-405b-instruct0.9456410.8468770.8972220.896580
simple-rag-mixtral-8x7b-instruct0.8964470.8843690.9083330.896383
simple-rag-gpt-4o0.8953550.8841280.8972220.892235
simple-rag-gpt-4o-mini0.8517860.9183470.9000000.890044
simple-rag-llama-3.1-70b-instruct0.9612310.8449460.8638890.890022
simple-rag-gemma-7b-it0.9236770.8636690.8750000.887449
simple-rag-llama-3.1-8b0.9577780.8226760.8805560.887003
mixture-rag-mixtral-8x7-instruct-modified0.8821970.8595170.9166670.886127
simple-rag-llama-3-70b0.9011360.8853280.8638890.883451
simple-rag-llama-3-8b0.9132140.8561650.8750000.881460
simple-rag-gemma2-9b-it0.8462120.9053050.8638890.871802
mixture-rag-gemma2-9b-it-modified0.8252080.8677290.8805560.857831
mixture-rag-llama3.1-8b-instruct-thought0.8200000.8977260.8388890.852205
simple-rag-claude-3.5-sonnet0.8218400.9053300.8250000.850723
simple-rag-claude-3-opus0.8671060.8910540.7722220.843461
mixture-rag-mixtral-8x7-instruct-thought0.8927270.7940750.8333330.840045
simple-rag-claude-3-sonnet0.8179310.8743340.8138890.835385
mixture-rag-mixtral-8x7-instruct0.7457500.8457670.9138890.835136
simple-rag-gpt-4-turbo0.8187580.8605090.8222220.833829
mixture-rag-llama3.1-8b-instruct-modified0.7098210.8716860.8972220.826243
simple-rag-claude-3-haiku0.7773410.8656610.8305560.824519
mixture-rag-llama3.1-8b-instruct0.7353520.8066520.9166670.819557
mixture-rag-gemma2-9b-it0.6431560.8618260.8527780.785920
mixture-rag-claude-3-haiku0.5949210.8440300.8722220.770391
mixture-rag-claude-3-haiku-modified0.5836370.8623780.8083330.751449
mixture-rag-claude-3-haiku-thought0.5927780.7470400.7638890.701235
\n", + "
" + ], + "text/plain": [ + " faithfulness answer_relevancy \\\n", + "experiment_name \n", + "mixture-rag-gemma2-9b-it-thought 0.924542 0.910476 \n", + "simple-rag-mistral-7b-instruct 0.878027 0.914597 \n", + "simple-rag-llama-3.1-405b-instruct 0.945641 0.846877 \n", + "simple-rag-mixtral-8x7b-instruct 0.896447 0.884369 \n", + "simple-rag-gpt-4o 0.895355 0.884128 \n", + "simple-rag-gpt-4o-mini 0.851786 0.918347 \n", + "simple-rag-llama-3.1-70b-instruct 0.961231 0.844946 \n", + "simple-rag-gemma-7b-it 0.923677 0.863669 \n", + "simple-rag-llama-3.1-8b 0.957778 0.822676 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.882197 0.859517 \n", + "simple-rag-llama-3-70b 0.901136 0.885328 \n", + "simple-rag-llama-3-8b 0.913214 0.856165 \n", + "simple-rag-gemma2-9b-it 0.846212 0.905305 \n", + "mixture-rag-gemma2-9b-it-modified 0.825208 0.867729 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.820000 0.897726 \n", + "simple-rag-claude-3.5-sonnet 0.821840 0.905330 \n", + "simple-rag-claude-3-opus 0.867106 0.891054 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.892727 0.794075 \n", + "simple-rag-claude-3-sonnet 0.817931 0.874334 \n", + "mixture-rag-mixtral-8x7-instruct 0.745750 0.845767 \n", + "simple-rag-gpt-4-turbo 0.818758 0.860509 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.709821 0.871686 \n", + "simple-rag-claude-3-haiku 0.777341 0.865661 \n", + "mixture-rag-llama3.1-8b-instruct 0.735352 0.806652 \n", + "mixture-rag-gemma2-9b-it 0.643156 0.861826 \n", + "mixture-rag-claude-3-haiku 0.594921 0.844030 \n", + "mixture-rag-claude-3-haiku-modified 0.583637 0.862378 \n", + "mixture-rag-claude-3-haiku-thought 0.592778 0.747040 \n", + "\n", + " context_utilization score \n", + "experiment_name \n", + "mixture-rag-gemma2-9b-it-thought 0.880556 0.905191 \n", + "simple-rag-mistral-7b-instruct 0.908333 0.900319 \n", + "simple-rag-llama-3.1-405b-instruct 0.897222 0.896580 \n", + "simple-rag-mixtral-8x7b-instruct 0.908333 0.896383 \n", + "simple-rag-gpt-4o 0.897222 0.892235 \n", + "simple-rag-gpt-4o-mini 0.900000 0.890044 \n", + "simple-rag-llama-3.1-70b-instruct 0.863889 0.890022 \n", + "simple-rag-gemma-7b-it 0.875000 0.887449 \n", + "simple-rag-llama-3.1-8b 0.880556 0.887003 \n", + "mixture-rag-mixtral-8x7-instruct-modified 0.916667 0.886127 \n", + "simple-rag-llama-3-70b 0.863889 0.883451 \n", + "simple-rag-llama-3-8b 0.875000 0.881460 \n", + "simple-rag-gemma2-9b-it 0.863889 0.871802 \n", + "mixture-rag-gemma2-9b-it-modified 0.880556 0.857831 \n", + "mixture-rag-llama3.1-8b-instruct-thought 0.838889 0.852205 \n", + "simple-rag-claude-3.5-sonnet 0.825000 0.850723 \n", + "simple-rag-claude-3-opus 0.772222 0.843461 \n", + "mixture-rag-mixtral-8x7-instruct-thought 0.833333 0.840045 \n", + "simple-rag-claude-3-sonnet 0.813889 0.835385 \n", + "mixture-rag-mixtral-8x7-instruct 0.913889 0.835136 \n", + "simple-rag-gpt-4-turbo 0.822222 0.833829 \n", + "mixture-rag-llama3.1-8b-instruct-modified 0.897222 0.826243 \n", + "simple-rag-claude-3-haiku 0.830556 0.824519 \n", + "mixture-rag-llama3.1-8b-instruct 0.916667 0.819557 \n", + "mixture-rag-gemma2-9b-it 0.852778 0.785920 \n", + "mixture-rag-claude-3-haiku 0.872222 0.770391 \n", + "mixture-rag-claude-3-haiku-modified 0.808333 0.751449 \n", + "mixture-rag-claude-3-haiku-thought 0.763889 0.701235 " + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Displaying the dataframe sorted by score(mean of all the metric scores on experiment level) by descending order\n", + "dataframe_3_mean.sort_values(by=\"score\", ascending=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "MLOps", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/01_exporing_results.ipynb b/notebooks/01_exporing_results.ipynb deleted file mode 100644 index d776639..0000000 --- a/notebooks/01_exporing_results.ipynb +++ /dev/null @@ -1,981 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exploring results data\n", - "\n", - "The idea of this notebook is to explore the results data and try to find the best RAG approach by analizing the scores of metrics.\n", - "\n", - "For metrics, we are using:\n", - "- faithfulness\n", - "- answer_relevancy\n", - "- context_utilization" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Importing needed libraries\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Reading the results.csv file\n", - "dataframe = pd.read_csv(\"/home/bojan/Work/mixture-of-rags/results/results.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating a dataframe with mean values for the scores for each experiment\n", - "dataframe_mean = dataframe.drop(columns=['trace_id']).groupby(\"experiment_name\").mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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faithfulnessanswer_relevancycontext_utilization
experiment_name
simple-rag-llama-3.1-8b0.9222220.7924260.781746
simple-rag-llama-3.1-405b-instruct0.9057620.8410260.807540
simple-rag-llama-3.1-70b-instruct0.9031280.8397520.805556
simple-rag-llama-3-8b0.8871170.8089320.809524
simple-rag-gemma-7b-it0.8734600.8347100.791667
simple-rag-gpt-4o0.8733520.8521220.825397
simple-rag-claude-3-opus0.8369470.8609460.718254
simple-rag-mixtral-8x7b-instruct0.8353740.7811650.837302
simple-rag-llama-3-70b0.8177430.8651890.783730
simple-rag-gemma2-9b-it0.8148560.8887740.783730
simple-rag-mistral-7b-instruct0.7989870.8476590.819444
simple-rag-claude-3.5-sonnet0.7981580.8706350.773810
simple-rag-gpt-4o-mini0.7804920.8851330.813492
simple-rag-claude-3-sonnet0.7778530.8272060.765873
simple-rag-claude-3-haiku0.7481640.8218640.759921
simple-rag-gpt-4-turbo0.7449520.8472590.771825
mixture-rag-llama3.1-8b-instruct0.7107960.7760890.803571
mixture-rag-mixtral-8x7-instruct0.7024590.7954910.823413
mixture-rag-gemma2-9b-it0.6667290.8210130.793651
mixture-rag-claude-3-haiku0.6101960.8206880.793651
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" - ], - "text/plain": [ - " faithfulness answer_relevancy \\\n", - "experiment_name \n", - "simple-rag-llama-3.1-8b 0.922222 0.792426 \n", - "simple-rag-llama-3.1-405b-instruct 0.905762 0.841026 \n", - "simple-rag-llama-3.1-70b-instruct 0.903128 0.839752 \n", - "simple-rag-llama-3-8b 0.887117 0.808932 \n", - "simple-rag-gemma-7b-it 0.873460 0.834710 \n", - "simple-rag-gpt-4o 0.873352 0.852122 \n", - "simple-rag-claude-3-opus 0.836947 0.860946 \n", - "simple-rag-mixtral-8x7b-instruct 0.835374 0.781165 \n", - "simple-rag-llama-3-70b 0.817743 0.865189 \n", - "simple-rag-gemma2-9b-it 0.814856 0.888774 \n", - "simple-rag-mistral-7b-instruct 0.798987 0.847659 \n", - "simple-rag-claude-3.5-sonnet 0.798158 0.870635 \n", - "simple-rag-gpt-4o-mini 0.780492 0.885133 \n", - "simple-rag-claude-3-sonnet 0.777853 0.827206 \n", - "simple-rag-claude-3-haiku 0.748164 0.821864 \n", - "simple-rag-gpt-4-turbo 0.744952 0.847259 \n", - "mixture-rag-llama3.1-8b-instruct 0.710796 0.776089 \n", - "mixture-rag-mixtral-8x7-instruct 0.702459 0.795491 \n", - "mixture-rag-gemma2-9b-it 0.666729 0.821013 \n", - "mixture-rag-claude-3-haiku 0.610196 0.820688 \n", - "\n", - " context_utilization \n", - "experiment_name \n", - "simple-rag-llama-3.1-8b 0.781746 \n", - "simple-rag-llama-3.1-405b-instruct 0.807540 \n", - "simple-rag-llama-3.1-70b-instruct 0.805556 \n", - "simple-rag-llama-3-8b 0.809524 \n", - "simple-rag-gemma-7b-it 0.791667 \n", - "simple-rag-gpt-4o 0.825397 \n", - "simple-rag-claude-3-opus 0.718254 \n", - "simple-rag-mixtral-8x7b-instruct 0.837302 \n", - "simple-rag-llama-3-70b 0.783730 \n", - "simple-rag-gemma2-9b-it 0.783730 \n", - "simple-rag-mistral-7b-instruct 0.819444 \n", - "simple-rag-claude-3.5-sonnet 0.773810 \n", - "simple-rag-gpt-4o-mini 0.813492 \n", - "simple-rag-claude-3-sonnet 0.765873 \n", - "simple-rag-claude-3-haiku 0.759921 \n", - "simple-rag-gpt-4-turbo 0.771825 \n", - "mixture-rag-llama3.1-8b-instruct 0.803571 \n", - "mixture-rag-mixtral-8x7-instruct 0.823413 \n", - "mixture-rag-gemma2-9b-it 0.793651 \n", - "mixture-rag-claude-3-haiku 0.793651 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Displaying the dataframe sorted by faithfulness by descending order\n", - "dataframe_mean.sort_values(by=\"faithfulness\", ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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faithfulnessanswer_relevancycontext_utilization
experiment_name
simple-rag-gemma2-9b-it0.8148560.8887740.783730
simple-rag-gpt-4o-mini0.7804920.8851330.813492
simple-rag-claude-3.5-sonnet0.7981580.8706350.773810
simple-rag-llama-3-70b0.8177430.8651890.783730
simple-rag-claude-3-opus0.8369470.8609460.718254
simple-rag-gpt-4o0.8733520.8521220.825397
simple-rag-mistral-7b-instruct0.7989870.8476590.819444
simple-rag-gpt-4-turbo0.7449520.8472590.771825
simple-rag-llama-3.1-405b-instruct0.9057620.8410260.807540
simple-rag-llama-3.1-70b-instruct0.9031280.8397520.805556
simple-rag-gemma-7b-it0.8734600.8347100.791667
simple-rag-claude-3-sonnet0.7778530.8272060.765873
simple-rag-claude-3-haiku0.7481640.8218640.759921
mixture-rag-gemma2-9b-it0.6667290.8210130.793651
mixture-rag-claude-3-haiku0.6101960.8206880.793651
simple-rag-llama-3-8b0.8871170.8089320.809524
mixture-rag-mixtral-8x7-instruct0.7024590.7954910.823413
simple-rag-llama-3.1-8b0.9222220.7924260.781746
simple-rag-mixtral-8x7b-instruct0.8353740.7811650.837302
mixture-rag-llama3.1-8b-instruct0.7107960.7760890.803571
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" - ], - "text/plain": [ - " faithfulness answer_relevancy \\\n", - "experiment_name \n", - "simple-rag-gemma2-9b-it 0.814856 0.888774 \n", - "simple-rag-gpt-4o-mini 0.780492 0.885133 \n", - "simple-rag-claude-3.5-sonnet 0.798158 0.870635 \n", - "simple-rag-llama-3-70b 0.817743 0.865189 \n", - "simple-rag-claude-3-opus 0.836947 0.860946 \n", - "simple-rag-gpt-4o 0.873352 0.852122 \n", - "simple-rag-mistral-7b-instruct 0.798987 0.847659 \n", - "simple-rag-gpt-4-turbo 0.744952 0.847259 \n", - "simple-rag-llama-3.1-405b-instruct 0.905762 0.841026 \n", - "simple-rag-llama-3.1-70b-instruct 0.903128 0.839752 \n", - "simple-rag-gemma-7b-it 0.873460 0.834710 \n", - "simple-rag-claude-3-sonnet 0.777853 0.827206 \n", - "simple-rag-claude-3-haiku 0.748164 0.821864 \n", - "mixture-rag-gemma2-9b-it 0.666729 0.821013 \n", - "mixture-rag-claude-3-haiku 0.610196 0.820688 \n", - "simple-rag-llama-3-8b 0.887117 0.808932 \n", - "mixture-rag-mixtral-8x7-instruct 0.702459 0.795491 \n", - "simple-rag-llama-3.1-8b 0.922222 0.792426 \n", - "simple-rag-mixtral-8x7b-instruct 0.835374 0.781165 \n", - "mixture-rag-llama3.1-8b-instruct 0.710796 0.776089 \n", - "\n", - " context_utilization \n", - "experiment_name \n", - "simple-rag-gemma2-9b-it 0.783730 \n", - "simple-rag-gpt-4o-mini 0.813492 \n", - "simple-rag-claude-3.5-sonnet 0.773810 \n", - "simple-rag-llama-3-70b 0.783730 \n", - "simple-rag-claude-3-opus 0.718254 \n", - "simple-rag-gpt-4o 0.825397 \n", - "simple-rag-mistral-7b-instruct 0.819444 \n", - "simple-rag-gpt-4-turbo 0.771825 \n", - "simple-rag-llama-3.1-405b-instruct 0.807540 \n", - "simple-rag-llama-3.1-70b-instruct 0.805556 \n", - "simple-rag-gemma-7b-it 0.791667 \n", - "simple-rag-claude-3-sonnet 0.765873 \n", - "simple-rag-claude-3-haiku 0.759921 \n", - "mixture-rag-gemma2-9b-it 0.793651 \n", - "mixture-rag-claude-3-haiku 0.793651 \n", - "simple-rag-llama-3-8b 0.809524 \n", - "mixture-rag-mixtral-8x7-instruct 0.823413 \n", - "simple-rag-llama-3.1-8b 0.781746 \n", - "simple-rag-mixtral-8x7b-instruct 0.837302 \n", - "mixture-rag-llama3.1-8b-instruct 0.803571 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Displaying the dataframe sorted by answer_relevancy by descending order\n", - "dataframe_mean.sort_values(by=\"answer_relevancy\", ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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faithfulnessanswer_relevancycontext_utilization
experiment_name
simple-rag-mixtral-8x7b-instruct0.8353740.7811650.837302
simple-rag-gpt-4o0.8733520.8521220.825397
mixture-rag-mixtral-8x7-instruct0.7024590.7954910.823413
simple-rag-mistral-7b-instruct0.7989870.8476590.819444
simple-rag-gpt-4o-mini0.7804920.8851330.813492
simple-rag-llama-3-8b0.8871170.8089320.809524
simple-rag-llama-3.1-405b-instruct0.9057620.8410260.807540
simple-rag-llama-3.1-70b-instruct0.9031280.8397520.805556
mixture-rag-llama3.1-8b-instruct0.7107960.7760890.803571
mixture-rag-claude-3-haiku0.6101960.8206880.793651
mixture-rag-gemma2-9b-it0.6667290.8210130.793651
simple-rag-gemma-7b-it0.8734600.8347100.791667
simple-rag-llama-3-70b0.8177430.8651890.783730
simple-rag-gemma2-9b-it0.8148560.8887740.783730
simple-rag-llama-3.1-8b0.9222220.7924260.781746
simple-rag-claude-3.5-sonnet0.7981580.8706350.773810
simple-rag-gpt-4-turbo0.7449520.8472590.771825
simple-rag-claude-3-sonnet0.7778530.8272060.765873
simple-rag-claude-3-haiku0.7481640.8218640.759921
simple-rag-claude-3-opus0.8369470.8609460.718254
\n", - "
" - ], - "text/plain": [ - " faithfulness answer_relevancy \\\n", - "experiment_name \n", - "simple-rag-mixtral-8x7b-instruct 0.835374 0.781165 \n", - "simple-rag-gpt-4o 0.873352 0.852122 \n", - "mixture-rag-mixtral-8x7-instruct 0.702459 0.795491 \n", - "simple-rag-mistral-7b-instruct 0.798987 0.847659 \n", - "simple-rag-gpt-4o-mini 0.780492 0.885133 \n", - "simple-rag-llama-3-8b 0.887117 0.808932 \n", - "simple-rag-llama-3.1-405b-instruct 0.905762 0.841026 \n", - "simple-rag-llama-3.1-70b-instruct 0.903128 0.839752 \n", - "mixture-rag-llama3.1-8b-instruct 0.710796 0.776089 \n", - "mixture-rag-claude-3-haiku 0.610196 0.820688 \n", - "mixture-rag-gemma2-9b-it 0.666729 0.821013 \n", - "simple-rag-gemma-7b-it 0.873460 0.834710 \n", - "simple-rag-llama-3-70b 0.817743 0.865189 \n", - "simple-rag-gemma2-9b-it 0.814856 0.888774 \n", - "simple-rag-llama-3.1-8b 0.922222 0.792426 \n", - "simple-rag-claude-3.5-sonnet 0.798158 0.870635 \n", - "simple-rag-gpt-4-turbo 0.744952 0.847259 \n", - "simple-rag-claude-3-sonnet 0.777853 0.827206 \n", - "simple-rag-claude-3-haiku 0.748164 0.821864 \n", - "simple-rag-claude-3-opus 0.836947 0.860946 \n", - "\n", - " context_utilization \n", - "experiment_name \n", - "simple-rag-mixtral-8x7b-instruct 0.837302 \n", - "simple-rag-gpt-4o 0.825397 \n", - "mixture-rag-mixtral-8x7-instruct 0.823413 \n", - "simple-rag-mistral-7b-instruct 0.819444 \n", - "simple-rag-gpt-4o-mini 0.813492 \n", - "simple-rag-llama-3-8b 0.809524 \n", - "simple-rag-llama-3.1-405b-instruct 0.807540 \n", - "simple-rag-llama-3.1-70b-instruct 0.805556 \n", - "mixture-rag-llama3.1-8b-instruct 0.803571 \n", - "mixture-rag-claude-3-haiku 0.793651 \n", - "mixture-rag-gemma2-9b-it 0.793651 \n", - "simple-rag-gemma-7b-it 0.791667 \n", - "simple-rag-llama-3-70b 0.783730 \n", - "simple-rag-gemma2-9b-it 0.783730 \n", - "simple-rag-llama-3.1-8b 0.781746 \n", - "simple-rag-claude-3.5-sonnet 0.773810 \n", - "simple-rag-gpt-4-turbo 0.771825 \n", - "simple-rag-claude-3-sonnet 0.765873 \n", - "simple-rag-claude-3-haiku 0.759921 \n", - "simple-rag-claude-3-opus 0.718254 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Displaying the dataframe sorted by context_utilization by descending order\n", - "dataframe_mean.sort_values(by=\"context_utilization\", ascending=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Adding mean score for each experiment(mean of all the scores)\n", - "dataframe_mean['mean'] = dataframe_mean.mean(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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faithfulnessanswer_relevancycontext_utilizationmean
experiment_name
simple-rag-llama-3.1-405b-instruct0.9057620.8410260.8075400.851443
simple-rag-gpt-4o0.8733520.8521220.8253970.850290
simple-rag-llama-3.1-70b-instruct0.9031280.8397520.8055560.849478
simple-rag-llama-3-8b0.8871170.8089320.8095240.835191
simple-rag-gemma-7b-it0.8734600.8347100.7916670.833279
simple-rag-llama-3.1-8b0.9222220.7924260.7817460.832132
simple-rag-gemma2-9b-it0.8148560.8887740.7837300.829120
simple-rag-gpt-4o-mini0.7804920.8851330.8134920.826372
simple-rag-llama-3-70b0.8177430.8651890.7837300.822221
simple-rag-mistral-7b-instruct0.7989870.8476590.8194440.822030
simple-rag-mixtral-8x7b-instruct0.8353740.7811650.8373020.817947
simple-rag-claude-3.5-sonnet0.7981580.8706350.7738100.814201
simple-rag-claude-3-opus0.8369470.8609460.7182540.805382
simple-rag-claude-3-sonnet0.7778530.8272060.7658730.790311
simple-rag-gpt-4-turbo0.7449520.8472590.7718250.788012
simple-rag-claude-3-haiku0.7481640.8218640.7599210.776649
mixture-rag-mixtral-8x7-instruct0.7024590.7954910.8234130.773787
mixture-rag-llama3.1-8b-instruct0.7107960.7760890.8035710.763485
mixture-rag-gemma2-9b-it0.6667290.8210130.7936510.760464
mixture-rag-claude-3-haiku0.6101960.8206880.7936510.741512
\n", - "
" - ], - "text/plain": [ - " faithfulness answer_relevancy \\\n", - "experiment_name \n", - "simple-rag-llama-3.1-405b-instruct 0.905762 0.841026 \n", - "simple-rag-gpt-4o 0.873352 0.852122 \n", - "simple-rag-llama-3.1-70b-instruct 0.903128 0.839752 \n", - "simple-rag-llama-3-8b 0.887117 0.808932 \n", - "simple-rag-gemma-7b-it 0.873460 0.834710 \n", - "simple-rag-llama-3.1-8b 0.922222 0.792426 \n", - "simple-rag-gemma2-9b-it 0.814856 0.888774 \n", - "simple-rag-gpt-4o-mini 0.780492 0.885133 \n", - "simple-rag-llama-3-70b 0.817743 0.865189 \n", - "simple-rag-mistral-7b-instruct 0.798987 0.847659 \n", - "simple-rag-mixtral-8x7b-instruct 0.835374 0.781165 \n", - "simple-rag-claude-3.5-sonnet 0.798158 0.870635 \n", - "simple-rag-claude-3-opus 0.836947 0.860946 \n", - "simple-rag-claude-3-sonnet 0.777853 0.827206 \n", - "simple-rag-gpt-4-turbo 0.744952 0.847259 \n", - "simple-rag-claude-3-haiku 0.748164 0.821864 \n", - "mixture-rag-mixtral-8x7-instruct 0.702459 0.795491 \n", - "mixture-rag-llama3.1-8b-instruct 0.710796 0.776089 \n", - "mixture-rag-gemma2-9b-it 0.666729 0.821013 \n", - "mixture-rag-claude-3-haiku 0.610196 0.820688 \n", - "\n", - " context_utilization mean \n", - "experiment_name \n", - "simple-rag-llama-3.1-405b-instruct 0.807540 0.851443 \n", - "simple-rag-gpt-4o 0.825397 0.850290 \n", - "simple-rag-llama-3.1-70b-instruct 0.805556 0.849478 \n", - "simple-rag-llama-3-8b 0.809524 0.835191 \n", - "simple-rag-gemma-7b-it 0.791667 0.833279 \n", - "simple-rag-llama-3.1-8b 0.781746 0.832132 \n", - "simple-rag-gemma2-9b-it 0.783730 0.829120 \n", - "simple-rag-gpt-4o-mini 0.813492 0.826372 \n", - "simple-rag-llama-3-70b 0.783730 0.822221 \n", - "simple-rag-mistral-7b-instruct 0.819444 0.822030 \n", - "simple-rag-mixtral-8x7b-instruct 0.837302 0.817947 \n", - "simple-rag-claude-3.5-sonnet 0.773810 0.814201 \n", - "simple-rag-claude-3-opus 0.718254 0.805382 \n", - "simple-rag-claude-3-sonnet 0.765873 0.790311 \n", - "simple-rag-gpt-4-turbo 0.771825 0.788012 \n", - "simple-rag-claude-3-haiku 0.759921 0.776649 \n", - "mixture-rag-mixtral-8x7-instruct 0.823413 0.773787 \n", - "mixture-rag-llama3.1-8b-instruct 0.803571 0.763485 \n", - "mixture-rag-gemma2-9b-it 0.793651 0.760464 \n", - "mixture-rag-claude-3-haiku 0.793651 0.741512 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Displaying the dataframe sorted by mean by descending order\n", - "dataframe_mean.sort_values(by=\"mean\", ascending=False)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "MLOps", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/requirements.txt b/requirements.txt index 6f4e915..f5904d9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,6 @@ langchain==0.2.12 langchain_community==0.2.11 langchain_core==0.2.29 langchain_groq==0.1.9 -pydantic==2.8.2 \ No newline at end of file +langchain_aws==0.1.16 +pydantic==2.8.2 +pandas==2.2.2 \ No newline at end of file diff --git a/results/results.csv b/results/results.csv index 271c55e..79dfaa4 100644 --- a/results/results.csv +++ b/results/results.csv @@ -1,281 +1,2799 @@ -experiment_name,trace_id,faithfulness,answer_relevancy,context_utilization -mixture-rag-mixtral-8x7-instruct,d97df213-83f6-42b2-8a55-d7f3a8086e4c,0.6818181818181818,0.6579449711329778,0.8055555555287036 -mixture-rag-mixtral-8x7-instruct,7b76dc10-0b46-4b80-9591-fea4f426b8d7,0.8620689655172413,0.7036913399983197,0.999999999975 -mixture-rag-mixtral-8x7-instruct,c367a6e3-b54f-4332-9643-fcb5641df2f8,0.8235294117647058,0.4946264511836652,0.9999999999666667 -mixture-rag-mixtral-8x7-instruct,645a072c-1632-46a6-a19a-a4112386cdc0,0.75,0.605109979287087,0.9999999999 -mixture-rag-mixtral-8x7-instruct,f5dc38db-2a3a-4d9c-a288-7bda627cbfe8,0.9565217391304348,0.8247055771772133,0.9999999999 -mixture-rag-mixtral-8x7-instruct,e17c6bad-7b15-4ff6-b0ea-c321c9536a24,0.8,0.974208066262647,0.8333333332916666 -mixture-rag-mixtral-8x7-instruct,b556f64b-8973-46d5-b106-a68a84d42ddc,0.5333333333333333,0.7912460234215003,0.3333333333 -mixture-rag-mixtral-8x7-instruct,f956def1-3f5d-48ad-a7c1-250d58de25c7,0.7142857142857143,0.9692922226109744,0.9999999999 -mixture-rag-mixtral-8x7-instruct,3d1e0234-896c-4bb2-9182-bacaf20e93c0,0.4117647058823529,0.6248956248447558,0.249999999975 -mixture-rag-mixtral-8x7-instruct,b6eec4fa-bda4-481d-800f-64bbcbbed145,0.6153846153846154,0.6703254856570627,0.9999999999 -mixture-rag-mixtral-8x7-instruct,b872ed3b-9689-422b-8531-01980cd7bd5b,0.7,0.9663442990105505,0.49999999995 -mixture-rag-mixtral-8x7-instruct,6393f905-90b3-4ea5-bbfc-24111b7f8384,0.6,0.9824451804350853,0.9999999999 -mixture-rag-mixtral-8x7-instruct,57d3390e-f223-417e-929e-7999c70f0a85,0.7857142857142857,0.9830251010857803,0.9999999999666667 -mixture-rag-mixtral-8x7-instruct,3229b880-ab3d-4b9b-8ab8-09865a23f77a,0.6,0.8890085053043496,0.8055555555287036 -mixture-rag-llama3.1-8b-instruct,1ebfcd39-cafc-42a8-90a7-a97cde059230,0.4642857142857143,0.7575874974984704,0.7499999999625 -mixture-rag-llama3.1-8b-instruct,9d5c5447-ef68-4cdd-8b32-9a268a1878b0,0.9736842105263158,0.4553476039842344,0.999999999975 -mixture-rag-llama3.1-8b-instruct,bf61b964-6c4e-4cba-9e6a-f43d71d3519c,0.8571428571428571,0.499860500381768,0.999999999975 -mixture-rag-llama3.1-8b-instruct,43886d8e-3b0e-4f05-84c5-98d723b31e3d,0.5833333333333334,0.6083801274682294,0.7499999999625 -mixture-rag-llama3.1-8b-instruct,1b1a1aab-7166-431b-8cc6-2db2f40077b4,1.0,0.8916191702652686,0.9999999999 -mixture-rag-llama3.1-8b-instruct,aadf8341-a5d3-4c72-9f61-e5944cd294c5,0.9523809523809523,0.9723849274629354,0.8333333332916666 -mixture-rag-llama3.1-8b-instruct,9cd93197-049b-4a4b-bc07-d9bd7b25c478,0.75,0.7733279037099465,0.3333333333 -mixture-rag-llama3.1-8b-instruct,d6f28e6d-ea4d-4da2-878d-12e471ee2358,0.7777777777777778,0.7444278589409986,0.9999999999 -mixture-rag-llama3.1-8b-instruct,4948c8a4-c753-4547-bdec-ad6a81fae0b3,0.8,0.6594314014099395,0.249999999975 -mixture-rag-llama3.1-8b-instruct,a5336210-d869-4f48-9a6a-a1a224a25197,0.7857142857142857,0.6807735367657081,0.9999999999 -mixture-rag-llama3.1-8b-instruct,01fe92d0-6667-4a22-8cba-f6f45782b93f,0.5,0.9348268701117112,0.49999999995 -mixture-rag-llama3.1-8b-instruct,f2804e67-99ae-49a8-b547-3defa3c012a4,0.1818181818181818,0.9947045503209968,0.9999999999 -mixture-rag-llama3.1-8b-instruct,dc6f259c-bf4c-4fae-9de6-c48b6142ff7d,0.7,0.9246586432618362,0.9999999999666667 -mixture-rag-llama3.1-8b-instruct,3dc2dfa3-5ca5-4491-ac6c-864ace77ba18,0.625,0.9679117087732756,0.8333333332916666 -mixture-rag-claude-3-haiku,395a9161-d701-4cf3-b9f1-f5ace39abd00,0.625,0.7594076403473613,0.8055555555287036 -mixture-rag-claude-3-haiku,b5250c9e-540e-4931-a01c-4f4f8bcf03b0,0.7567567567567568,0.5917719533985579,0.999999999975 -mixture-rag-claude-3-haiku,14c45ae9-6adc-4537-a165-e57ab55b799e,0.7391304347826086,0.5544537985811971,0.999999999975 -mixture-rag-claude-3-haiku,54bde2ee-964f-4ffa-9a03-f1baae4007db,0.5454545454545454,0.7658352370558137,0.9999999999 -mixture-rag-claude-3-haiku,31b4e10c-b8d5-4a51-8564-bb9a45d42eed,0.5652173913043478,0.9272119597998078,0.9999999999 -mixture-rag-claude-3-haiku,3272cc23-1d2b-4339-96d4-79cf5101497e,0.7272727272727273,0.9723849274629354,0.8333333332916666 -mixture-rag-claude-3-haiku,59c41b48-4e8d-4135-9673-8165f03e3481,0.6923076923076923,0.7733279037099465,0.3333333333 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+experiment_name,trace_id,question,answer,faithfulness,answer_relevancy,context_utilization +mixture-rag-claude-3-haiku-thought,5d7ae2d3-f2b8-4840-b877-69165f991599,How can attention be described in the Transformer?,"The response from the second model provides the most comprehensive and detailed explanation of how attention is used in the Transformer model. It covers the three different ways attention is used: encoder-decoder attention, encoder self-attention, and decoder self-attention. The explanation clearly describes the role of queries, keys, and values in each case, and how the multi-head attention mechanism allows the model to attend to different parts of the input sequence in parallel. This response is the most informative and accurate among the provided options.",0.7272727272727273,0.7230326973611291,0.9999999999 +mixture-rag-claude-3-haiku-thought,aa2067f5-33f7-4d70-b4c9-f1752084c8ae,What is Mixture of Agents?,"The response from the third model provides the most comprehensive and informative explanation of the Mixture-of-Agents (MoA) methodology. It clearly describes the layered architecture of the approach, the roles of the proposer and aggregator models, and how the collaborative process of iteratively refining and combining responses from multiple language models leads to improved performance on various natural language tasks. This response captures the key aspects of the MoA approach in a clear and concise manner, making it the best choice among the provided options.",0.5555555555555556,0.4661294316013642,0.8055555555287036 +mixture-rag-claude-3-haiku-thought,cefa79c4-cba0-4961-bc87-005e2c2b8837,Is Mixtral based on the idea of a mixture of experts?,"Based on the provided responses, the best response is the one from the second model. It provides a clear and concise explanation that Mixtral is a Sparse Mixture of Experts (SMoE) language model, where a router network selects a subset of experts to process each token, allowing for efficiency and scalability.",0.75,0.6362645825762212,0.9999999999666667 +mixture-rag-claude-3-haiku-thought,8f2ee9a4-72d8-4956-8131-fa0ed9bce4a0,What is sliding window attention?,"The response from the first model provides the most comprehensive and informative explanation of sliding window attention. It clearly explains how the technique works, including the concepts of limited attention, moving window, and recursive access. It also outlines the key benefits of sliding window attention, such as reduced computational cost, improved memory efficiency, and the ability to handle longer sequences. This response covers the essential details about sliding window attention in a clear and concise manner, making it the best choice among the provided responses.",0.5714285714285714,0.6911743469310316,0.9999999999 +mixture-rag-claude-3-haiku-thought,584e89e1-cc11-4101-8c96-f10cb725fa15,How many stages are there in the development of the Llama 3 model?,"The response from the second model provides the most comprehensive and accurate summary of the two main stages in the development of the Llama 3 model: + +""The development of the Llama 3 model comprises two main stages. The first stage is language model pre-training, where a large language model is pre-trained on a large multilingual text corpus to perform next-token prediction and learn the structure of language. The second stage is language model post-training, where the pre-trained language model is aligned with human feedback through supervised finetuning, rejection sampling, and direct preference optimization.""",1.0,0.9385616026326954,0.9999999999 +mixture-rag-claude-3-haiku-thought,8c5fb2f0-6559-4325-bb58-c0ff64d757d6,What is the difference between the Llama 2 and Llama 2-Chat ?,"The response from the third model provides the most comprehensive and informative explanation of the differences between Llama 2 and Llama 2-Chat. It clearly outlines the key differences in terms of purpose, training, and architecture, which helps to better understand the distinct capabilities of these two models. Therefore, I choose the response from the third model as the best one.",0.1428571428571428,0.8782115192683907,0.0 +mixture-rag-claude-3-haiku-thought,ed1b882f-bcab-4690-a2bf-9454739e7772,What is optimizer is used for LLaMA?,"The response from the third model is the most accurate and informative. It clearly states that the AdamW optimizer is used for training the LLaMA model, and provides the specific hyperparameters used (β1 = 0.9, β2 = 0.95, eps = 10^(-5)). This level of detail is not present in the other responses.",0.625,0.6787120780776216,0.3333333333 +mixture-rag-claude-3-haiku-thought,693fecca-5cef-42be-aa88-b109a7ac38a2,Can the GPT-4 model accept both text and image inputs?,"The second model's response is the most accurate and informative. It directly states that ""GPT-4 accepts prompts consisting of both images and text"" and provides additional details about the model's capabilities in handling visual inputs, as mentioned in the provided context.",0.4,0.6217516372737434,0.9999999999 +mixture-rag-claude-3-haiku-thought,e3a24e14-0ab7-424c-93a0-9275f7786d9c,On what architecture the GPT-3 model is based on?,"The response from the third model is the most comprehensive and informative. It provides details on the specific architectural improvements and modifications made to the original transformer architecture in the GPT-3 model, including pre-normalization, the SwiGLU activation function, and the use of rotary embeddings instead of absolute positional embeddings. This response gives the most detailed and accurate information about the technical aspects of the GPT-3 model's architecture.",0.625,0.6716087068605566,0.249999999975 +mixture-rag-claude-3-haiku-thought,033a740b-b1c2-48b8-9c67-da996761a160,How many stages of training are in the GPT model?,"The response from the second model is the most accurate and informative. It clearly explains the two-stage training process of the GPT model, including the initial training on a large internet dataset to predict the next word, followed by fine-tuning using reinforcement learning from human feedback (RLHF) to produce outputs preferred by human labelers.",0.6666666666666666,0.5660389037784205,0.9999999999 +mixture-rag-claude-3-haiku-thought,955be8f5-e941-497c-b3c2-82f8ce6bbb6d,What tokenizer is used in the Gemma2 model?,"The second model's response is the most accurate and informative. It clearly summarizes the key details about the Gemma 2 tokenizer, including that it uses the same SentencePiece tokenizer as Gemma 1 and Gemini, with split digits, preserved whitespace, and byte-level encodings, resulting in a 256k vocabulary.",0.8571428571428571,0.8715386377966728,0.49999999995 +mixture-rag-claude-3-haiku-thought,7bf06670-ee47-41ff-a93d-64df51ceaa12,On what architecture the Gemma model is based on?,The second response is the best choice as it provides the most detailed and informative description of the Gemma model architecture being based on the transformer decoder architecture as described in the Vaswani et al. (2017) paper.,0.6666666666666666,0.9824451804350853,0.9999999999 +mixture-rag-claude-3-haiku-thought,d8ba1e2e-a68a-4a8e-a0fe-b82f46d80172,Does Claude 3 models have vision capabilities?,"The second model's response is the most accurate and informative based on the provided context. It clearly states that Claude 3 models have vision capabilities, including the ability to recognize and identify objects visually, as well as interpret images in a table format. However, it also notes the important caveat that these models can sometimes generate inaccurate information and descriptions about images, and should not be used for consequential use cases without human validation.",0.7777777777777778,0.7456604597649018,0.9999999999666667 +mixture-rag-claude-3-haiku-thought,2df2f209-043f-42b2-9da2-a521e221b2ae,What are the two tasks in BERT?,"The response from the second model provides the most comprehensive and accurate information about the two main tasks in BERT - pre-training and fine-tuning. It clearly explains the purpose and process of each task, which aligns with the context provided in the user query. The other responses also cover the same information, but the second model's response is the most detailed and informative.",0.1111111111111111,0.7637943214634483,0.3333333333 +mixture-rag-gemma2-9b-it-thought,fee121c7-15a3-4fe4-b9c2-de1afc816a33,How can attention be described in the Transformer?,"content=' In the Transformer, attention is used to compute representations of the input and output sequences without relying on recurrence or convolution. It is a mechanism that relates different positions of a single sequence to compute a representation of the sequence. The Transformer uses a specific type of attention called multi-head attention, which consists of multiple attention heads that run in parallel. Each head Attention(Q, K, V) is calculated as the concatenation of headi, where headi is the result of the Attention function taking the query matrix Qwi, key matrix Kwi, and value matrix Vwi, which are the result of projecting the input matrices Q, K, and V using parameter matrices WQi, WKi, WVi, and WO.\n\nThe Transformer uses multi-head attention in three different ways:\n\n1. In ""encoder-decoder attention"" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence.\n2. The encoder contains self-attention layers. In a self-attention layer, all of the keys, values, and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.\n3. Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position.\n\nThe main advantage of using multi-head attention in the Transformer is that it allows the model to focus on different positions in the input sequence, and different aspects of the input representation, at the same time. This leads to improved performance and the ability to handle longer sequences.' response_metadata={'ResponseMetadata': {'RequestId': '196c7dc6-70c9-4c20-a78e-66c3192ae905', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Sat, 17 Aug 2024 19:11:46 GMT', 'content-type': 'application/json', 'content-length': '1991', 'connection': 'keep-alive', 'x-amzn-requestid': '196c7dc6-70c9-4c20-a78e-66c3192ae905'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': 6201}} id='run-868f21a1-4550-4c7d-8f33-12ec2131904f-0' usage_metadata={'input_tokens': 1574, 'output_tokens': 397, 'total_tokens': 1971} + + +",0.8181818181818182,0.638028510647524,0.8055555555287036 +mixture-rag-gemma2-9b-it-thought,6bddc866-0c7e-45bf-9223-cd23f2ace17e,What is Mixture of Agents?,"Mixture of Agents (MoA) is a methodology that leverages multiple large language models (LLMs) to iteratively enhance the generation quality of responses. It is a layered architecture where each layer comprises multiple LLM agents, and each agent takes the outputs from the previous layer as auxiliary information to generate its response. + +In the MoA approach, the first layer consists of multiple LLMs, known as proposers, which generate responses to a given prompt. These responses are then presented to the next layer, which consists of multiple LLMs, known as aggregators, that refine and synthesize the responses into a single, high-quality output. + + + +",0.6428571428571429,0.7036913399983197,0.8055555555287036 +mixture-rag-gemma2-9b-it-thought,dad41ae7-6b21-4194-bf32-3359722bb087,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is indeed based on the idea of a Mixture of Experts (MoE). It is a Sparse Mixture of Experts (SMoE) language model, where each layer is composed of multiple feedforward blocks (or ""experts""). For every token, a router network selects a few experts to process the current state and combine their outputs. This allows each token to have access to a large number of parameters, while only using a fraction of them during inference. +",1.0,0.8764177588434284,0.9999999999666667 +mixture-rag-gemma2-9b-it-thought,285109f3-b8ce-43a7-9efd-58d8a741b613,What is sliding window attention?,"Sliding Window Attention (SWA) is a mechanism used in the Mistral 7B model to manage the computational costs and inference latency associated with attention in large language models. In SWA, each token can only attend to a fixed number (W) of tokens from the previous layer. This approach reduces the number of operations from quadratic to linear in relation to the sequence length, and decreases memory usage. The window size (W) determines how many tokens each token can attend to; for example, if W is set to 3, each token can only attend to the previous 3 tokens. However, tokens outside the sliding window still influence next word prediction. At each attention layer, information can move forward by up to k x W tokens, where k is the number of attention layers. This mechanism allows for more parallelization and can improve translation quality while reducing computational costs and inference latency. +",0.7,0.6519607827221471,0.9999999999 +mixture-rag-gemma2-9b-it-thought,33decca1-3d62-48b3-a7b6-96deac332ce9,How many stages are there in the development of the Llama 3 model?,"The development of the Llama 3 model comprises two main stages. The first stage is language model pre-training, where a large language model is pre-trained on a large multilingual text corpus to perform next-token prediction and learn the structure of language. The second stage is language model post-training, where the pre-trained language model is aligned with human feedback through supervised finetuning, rejection sampling, and direct preference optimization. +",1.0,0.9385616026326954,0.9999999999 +mixture-rag-gemma2-9b-it-thought,7f3e926a-4ae5-494a-90b6-2b7ce5c55678,What is the difference between the Llama 2 and Llama 2-Chat ?,"Based on the provided context, the difference between Llama 2 and Llama 2-Chat is that Llama 2 is the base model with different parameter sizes (7B, 13B, and 70B), which is pretrained using publicly available data. On the other hand, Llama 2-Chat is a fine-tuned version of Llama 2, optimized for dialogue use cases. It is also available in variants with 7B, 13B, and 70B parameters. The Llama 2-Chat models demonstrate superior performance compared to open-source models in both single-turn and multi-turn prompts, as shown in Figure 12 of the given context. +",1.0,0.9815519471495716,0.8333333332916666 +mixture-rag-gemma2-9b-it-thought,dd3779ba-e0aa-4fe0-bbf7-82912953861b,What is optimizer is used for LLaMA?,"The LLaMA model was trained using the AdamW optimizer. The learning rate schedule used is a cosine learning rate schedule, with warmup of 2000 steps and decaying the final learning rate down to 10% of the peak learning rate. Additionally, a weight decay of 0.1 and gradient clipping of 1.0 were applied during training. +",1.0,0.7733279037099465,0.3333333333 +mixture-rag-gemma2-9b-it-thought,ecb93a29-c79f-4115-ae0c-d9236f899764,Can the GPT-4 model accept both text and image inputs?,"Yes, the GPT-4 model is capable of accepting both text and image inputs. This is mentioned in the context you provided: ""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task."" This means that the model can generate text outputs based on inputs that include arbitrarily interlaced text and images. Examples of GPT-4\'s visual input capabilities can be found in Table 3 of the provided context. +",0.8333333333333334,1.0,0.8333333332916666 +mixture-rag-gemma2-9b-it-thought,5cf86c25-ea40-4880-b08a-397b55206b2b,On what architecture the GPT-3 model is based on?,"The GPT-3 model is based on the transformer architecture, with several improvements drawn from more recent work in large language models. These improvements include pre-normalization, the SwiGLU activation function, and the removal of absolute positional embeddings in favor of rotary embeddings. +",0.3333333333333333,0.8329466056919473,0.249999999975 +mixture-rag-gemma2-9b-it-thought,6cbd93f4-36d1-4366-9ee3-b34c92b5d0e3,How many stages of training are in the GPT model?,"The GPT model is trained in two stages. In the first stage, the model is trained to predict the next word using a large dataset of text from the Internet. In the second stage, the model is fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers. +",1.0,0.6807735367657081,0.9999999999 +mixture-rag-gemma2-9b-it-thought,903243ac-0bb5-4de7-a343-6f767e20ebf7,What tokenizer is used in the Gemma2 model?,"Based on the provided context, the Gemma 2 models use a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings. The resulting vocabulary has 256k entries. +",1.0,0.9668872845590216,0.49999999995 +mixture-rag-gemma2-9b-it-thought,54b1f289-a23f-4475-a9e2-ef470427e654,On what architecture the Gemma model is based on?,"The Gemma model is based on the transformer decoder architecture, as described in the research paper by Vaswani et al. (2017). +",1.0,0.9947045503209968,0.9999999999 +mixture-rag-gemma2-9b-it-thought,95c8106b-9daf-49dd-8a24-c785010314e4,Does Claude 3 models have vision capabilities?,"Yes, Claude 3 models have vision capabilities. According to the text, they can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics. Additionally, the text mentions that Claude 3 models can process images, including tables, graphs, and photos, and can even generate text from images with hard-to-read handwriting. +",1.0,0.9942643083073888,0.9999999999666667 +mixture-rag-gemma2-9b-it-thought,d6223930-8f60-4c2c-9f4d-4d32140c56bb,What are the two tasks in BERT?,"Based on the provided context, the two tasks in BERT are: + +1. Pre-training: In this task, the model is trained on unlabeled data over different pre-training tasks. The purpose of pre-training is to learn general language understanding from large-scale data. + +2. Fine-tuning: After pre-training, the BERT model is fine-tuned using labeled data from downstream tasks. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters. Fine-tuning involves adjusting all the parameters of the pre-trained model to fit the specific task. + + +",0.7692307692307693,0.9679117087732756,0.8333333332916666 +mixture-rag-llama3.1-8b-instruct-thought,bdeb0866-6a4f-4f46-a66e-ffe63d238b09,How can attention be described in the Transformer?," +The Transformer uses a mechanism called **multi-head self-attention** in both its encoder and decoder. + +Here's a breakdown: + +* **Self-attention:** Allows each position in a sequence to attend to all other positions in the same sequence. This helps the model understand relationships between words regardless of their distance. +* **Multi-head:** The Transformer uses 8 parallel attention layers (heads). Each head processes the information in a slightly different way, allowing the model to capture diverse relationships within the sequence. + +Essentially, the Transformer leverages attention to process information in parallel and capture long-range dependencies between words in a sequence, making it more efficient and effective than traditional recurrent models.",0.7142857142857143,0.4983518398883557,0.7499999999625 +mixture-rag-llama3.1-8b-instruct-thought,39042da2-097f-4945-894d-9f45d2170e2a,What is Mixture of Agents?," +Mixture-of-Agents (MoA) is a methodology proposed to harness the collective strengths of multiple Large Language Models (LLMs) by constructing a layered architecture. In this approach, each layer consists of multiple LLM agents, and each agent takes all the outputs from agents in the previous layer as auxiliary information to generate its response. This methodology has been shown to achieve state-of-the-art performance on various benchmarks, surpassing models like GPT-4 Omni. The MoA structure encourages collaboration among LLMs, allowing them to generate higher quality responses based on outputs from other models. The agents in each layer can share the same model, and the iterative refinement process continues until obtaining a more robust and comprehensive response.",0.8,0.6074347367449137,0.8055555555287036 +mixture-rag-llama3.1-8b-instruct-thought,c951b9d5-5d8e-46b7-97f5-987d347380a7,Is Mixtral based on the idea of a mixture of experts?," +Yes, Mixtral is indeed based on the idea of a Mixture of Experts (MoE). It is a Sparse Mixture of Experts (SMoE) language model, where each layer is composed of multiple feedforward blocks (or ""experts""). For every token, a router network selects a few experts to process the current state and combine their outputs. This allows each token to have access to a large number of parameters, while only using a fraction of them during inference.",0.9,0.8764177588434284,0.9999999999666667 +mixture-rag-llama3.1-8b-instruct-thought,d59d96d6-4b00-4275-ac71-cbfe63907eb8,What is sliding window attention?," +Sliding Window Attention (SWA) is a technique used in the Mistral 7B language model to improve the efficiency and performance of attention mechanisms. Here's a breakdown of how it works: + +1. Attention in Transformer models typically allows each token to attend to all other tokens in the input sequence. This can be computationally expensive, especially for long sequences. +2. SWA introduces a constraint where each token can only attend to a fixed-size ""window"" of tokens from the previous layer, rather than the entire sequence. +3. Specifically, the hidden state at position i in layer k can only attend to hidden states between positions i-W and i in the previous layer k-1, where W is the window size. +4. By limiting the attention span, SWA reduces the computational and memory requirements of the attention mechanism, leading to faster inference speeds. +5. Importantly, even with this constrained attention, the model can still capture long-range dependencies by stacking multiple attention layers. After k attention layers, a token can effectively attend to tokens up to k*W positions away in the original input sequence. +6. In the case of Mistral 7B, the window size W is set to 4096, which allows the model to attend to tokens up to 131K positions away in the final layer, despite the constrained attention at each individual layer. + +In summary, Sliding Window Attention is a technique that balances the need for long-range dependencies in language models with the computational efficiency required for practical deployment. By limiting the attention span at each layer, it enables faster and more memory-efficient inference without sacrificing model performance.",0.6551724137931034,0.6052586311030111,0.9999999999 +mixture-rag-llama3.1-8b-instruct-thought,5c066138-4181-4df6-bf06-e81d01602c89,How many stages are there in the development of the Llama 3 model?," +The development of the Llama 3 model comprises two main stages. The first stage is language model pre-training, where a large language model is pre-trained on a large multilingual text corpus to perform next-token prediction and learn the structure of language. The second stage is language model post-training, where the pre-trained language model is aligned with human feedback through supervised finetuning, rejection sampling, and direct preference optimization.",1.0,0.9385616026326954,0.9999999999 +mixture-rag-llama3.1-8b-instruct-thought,26b73834-a07d-472e-a7ba-3b8e33f751e8,What is the difference between the Llama 2 and Llama 2-Chat ?," +Based on the provided context, the difference between Llama 2 and Llama 2-Chat is that Llama 2 is the base model with different parameter sizes (7B, 13B, and 70B), which is pretrained using publicly available data. On the other hand, Llama 2-Chat is a fine-tuned version of Llama 2, optimized for dialogue use cases. It is also available in variants with 7B, 13B, and 70B parameters. The Llama 2-Chat models demonstrate superior performance compared to open-source models in both single-turn and multi-turn prompts.",1.0,0.9815519471495716,0.8333333332916666 +mixture-rag-llama3.1-8b-instruct-thought,adf6d050-9d56-41c2-96fc-a681bc056570,What is optimizer is used for LLaMA?," +The LLaMA model was trained using the AdamW optimizer. The learning rate schedule used is a cosine learning rate schedule, with warmup of 2000 steps and decaying the final learning rate down to 10% of the peak learning rate. Additionally, a weight decay of 0.1 and gradient clipping of 1.0 were applied.",1.0,0.7733279037099465,0.3333333333 +mixture-rag-llama3.1-8b-instruct-thought,0c0d68b8-5693-4d24-92cd-99c5d09dd975,Can the GPT-4 model accept both text and image inputs?," +Yes, the GPT-4 model is capable of accepting both text and image inputs. This is mentioned in the context you provided, where it states, ""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task."" This means that the model can generate text outputs based on inputs that include arbitrarily interlaced text and images. Examples of GPT-4's visual input capabilities can be found in Table 3 of the provided context.",0.8333333333333334,0.9692922226109744,0.8333333332916666 +mixture-rag-llama3.1-8b-instruct-thought,ef5dad3e-921a-4cca-b8da-dabd7549ca32,On what architecture the GPT-3 model is based on?," +The GPT-3 model is based on the transformer architecture, with several improvements drawn from models like PaLM and GPTNeo. These improvements include pre-normalization, the SwiGLU activation function, and the use of rotary embeddings instead of absolute positional embeddings.",0.8571428571428571,0.8323559718333268,0.249999999975 +mixture-rag-llama3.1-8b-instruct-thought,898f48be-6a8c-4229-aa12-d4186a78e5bd,How many stages of training are in the GPT model?," +The GPT model is trained in two stages. In the first stage, the model is trained to predict the next word using a large dataset of text from the Internet. In the second stage, the model is fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers.",1.0,0.6807735367657081,0.9999999999 +mixture-rag-llama3.1-8b-instruct-thought,26fcb427-3f6a-4275-b8b7-91c6d6cd0580,What tokenizer is used in the Gemma2 model?," +Based on the provided responses, the most accurate and informative response is: + +According to the context provided, the Gemma 2 models use the same tokenizer as the Gemma 1 and Gemini models. Specifically, it states: ""Tokenizer. We use the same tokenizer as Gemma 1 and Gemini: a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings (Kudo and Richardson, 2018). The resulting vocabulary has 256k entries."" So the Gemma 2 models use a SentencePiece tokenizer with a 256k entry vocabulary.",1.0,0.9663442990105505,0.49999999995 +mixture-rag-llama3.1-8b-instruct-thought,cd2f94aa-1348-4cb9-89d7-500370999a62,On what architecture the Gemma model is based on?," +The Gemma model is based on the transformer decoder architecture, as described in the paper ""Attention is All You Need"" by Vaswani et al. (2017). This architecture is commonly used in large language models and is known for its ability to effectively handle sequential data and perform attention-based processing.",0.1666666666666667,0.9947045503209968,0.9999999999 +mixture-rag-llama3.1-8b-instruct-thought,84868441-a80f-40b4-9b95-454b3911f15f,Does Claude 3 models have vision capabilities?," +Yes, based on the provided context, Claude 3 models do have vision capabilities. They can recognize and identify objects visually, and can interpret images in a table format, organizing the text from these images into a JSON format. However, it's important to note that while these models excel in new multimodal capabilities, they can sometimes generate inaccurate information and descriptions about images. Therefore, they should not be used for consequential use cases that require high precision and accuracy without human validation. Additionally, performance can be lower for small or low-resolution images.",1.0,0.9942643083073888,0.9999999999666667 +mixture-rag-llama3.1-8b-instruct-thought,9f90199c-0589-4315-8951-228f2c5ad496,What are the two tasks in BERT?," +Based on the provided responses, the most accurate and informative response is from the third model: + +Based on the provided context, the two main tasks in BERT are: + +1. Pre-training: + - During pre-training, the BERT model is trained on unlabeled data over different pre-training tasks. This includes: + - Masked Language Model (Mask LM): The model is trained to predict masked tokens in the input sequence. + - Next Sentence Prediction (NSP): The model is trained to predict whether two given sentences are consecutive in the original text. + +2. Fine-tuning: + - After pre-training, the BERT model is fine-tuned on labeled data from downstream tasks. + - The pre-trained BERT model is used to initialize the model for different downstream tasks, and all the parameters are then fine-tuned using the labeled data. + - The fine-tuning process allows the BERT model to adapt to specific tasks, such as question answering, named entity recognition, sentiment analysis, and more. + +In summary, the two main tasks in BERT are the pre-training stage, where the model is trained on unlabeled data using the Masked Language Model and Next Sentence Prediction objectives, and the fine-tuning stage, where the pre-trained model is adapted to specific downstream tasks using labeled data.",0.5,0.9679117087732756,0.4166666666458333 +mixture-rag-mixtral-8x7-instruct-thought,3709a464-bf3b-4964-b342-99a6030a27d1,How can attention be described in the Transformer?," From the first model: +content=""The Transformer uses a mechanism called **multi-head self-attention** in both its encoder and decoder. \n\nHere's a breakdown:\n\n* **Self-attention:** Allows each position in the input sequence to attend to all other positions in the same sequence. This helps the model understand relationships between words regardless of their distance.\n\n* **Multi-head:** The Transformer uses 8 parallel attention layers (heads). Each head processes the information in a slightly different way, allowing the model to capture more nuanced relationships.\n\nEssentially, attention in the Transformer lets it weigh the importance of different words in a sequence when processing information. This is crucial for understanding context and long-range dependencies in text. \n\n\nLet me know if you'd like more details on a specific aspect of attention in the Transformer! \n"" + +This response provides a clear and concise explanation of how the Transformer model uses multi-head self-attention in both its encoder and decoder. It highlights the key aspects of self-attention and multi-head attention, making it an informative and accurate response.",0.3684210526315789,0.7518639187751166,0.7499999999625 +mixture-rag-mixtral-8x7-instruct-thought,3cedfaea-c5aa-4069-9e50-f9bd85cf48d3,What is Mixture of Agents?," From the second model: + +Content: 'Mixture of Agents (MoA) is a methodology introduced in this paper that leverages multiple large language models (LLMs) to iteratively enhance the generation quality of responses. The key ideas behind MoA are: + +1. Collaborativeness of LLMs: The paper shows that LLMs can improve their responses by referencing outputs from other models, even if the auxiliary responses are of lower quality. This indicates a collaborative capability among LLMs. + +2. MoA Architecture: MoA constructs a layered architecture where each layer consists of multiple LLM ""agents"". Each agent in a layer takes the outputs from all agents in the previous layer as auxiliary information to generate its own response. This iterative refinement process continues for several cycles. + +3. Roles of Agents: The agents in MoA are categorized into two roles: + - Proposers: These agents excel at generating useful reference responses that provide context and diverse perspectives. + - Aggregators: These agents are proficient in synthesizing responses from other models into a single, high-quality output. + +4. Benefits of MoA: The paper shows that the MoA approach significantly outperforms an LLM-based ranker that simply selects one of the answers generated by the proposers. MoA also achieves state-of-the-art performance on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK, surpassing even GPT-4 Omni. + +In summary, Mixture of Agents is a novel methodology that leverages the collaborative capabilities of multiple LLMs to iteratively refine and enhance the quality of generated responses, leading to significant performance improvements on various language tasks.' + +Response metadata: {'ResponseMetadata': {'RequestId': '5d5ee7c7-dc18-4db9-a7f4-c259d407ca86', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Sat, 17 Aug 2024 12:07:31 GMT', 'content-type': 'application/json', 'content-length': '1843', 'connection': 'keep-alive', 'x-amzn-requestid': '5d5ee7c7-dc18-4db9-a7f4-c259d407ca86'}, 'RetryAttempts': 0}, 'stopReason': 'stop_sequence', 'metrics': {'latencyMs': 3977}} + +Usage metadata: {'input_tokens': 1843, 'output_tokens': 379, 'total_tokens': 2222}",1.0,0.6027928736139344,0.999999999975 +mixture-rag-mixtral-8x7-instruct-thought,6e992059-3026-4612-b897-830ed866957f,Is Mixtral based on the idea of a mixture of experts?," Yes, Mixtral is a Sparse Mixture of Experts (SMoE) model, where each layer is composed of 8 feedforward blocks (experts). At each layer, for every token, a router network selects two of these 8 experts to process the current state and combine their outputs. This means that each token has access to the full 47B parameters of the model, but only actively uses 13B parameters during inference. This sparse mixture-of-experts approach allows Mixtral to achieve high performance while using fewer active parameters per token compared to a dense model.",0.8333333333333334,0.6036592578916512,0.999999999975 +mixture-rag-mixtral-8x7-instruct-thought,11943683-88ab-4150-bd5e-675baaef9354,What is sliding window attention?," From the first model: +content='Sliding window attention is a technique used in large language models (LLMs) to improve efficiency and handle longer sequences. ... The sliding window approach allows for a fixed-size cache, making it easier to manage memory, especially when dealing with very long sequences. ... Let me know if you have any other questions!' + +The first model provides a clear and concise explanation of sliding window attention, its functionality, and its benefits. It highlights the main advantages of this technique, such as reduced computational cost and improved memory management. Additionally, it offers an example to illustrate how the window size and number of layers can affect the range of information a word can access.",0.5333333333333333,0.7229723167233922,0.9999999999 +mixture-rag-mixtral-8x7-instruct-thought,cda2eeaf-946b-401f-8b0a-a014db027bce,How many stages are there in the development of the Llama 3 model?," From the second model: + +Content='According to the context provided, the development of the Llama 3 language models comprises two main stages: + +1. Language model pre-training: +- The model is pre-trained on a large, multilingual text corpus to perform next-token prediction. This allows the model to learn the structure of language and obtain knowledge about the world from the text. +- The pre-training is performed at a massive scale, with a 405B parameter model trained on 15.6T tokens using a context window of 8K tokens. This is followed by a continued pre-training stage that increases the supported context window to 128K tokens. + +2. Language model post-training: +- The pre-trained language model is then aligned with human feedback through several rounds of supervised finetuning, rejection sampling, and direct preference optimization. +- This post-training stage is aimed at making the model behave in the way expected of an assistant, beyond just having a rich understanding of language. + +So, in summary, the development of the Llama 3 model involves two main stages: language model pre-training and language model post-training.' + +Response metadata={'ResponseMetadata': {'RequestId': 'dbdb97ee-2ff1-48ad-a0e0-5c96f9ae29ff', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Sat, 17 Aug 2024 12:05:15 GMT', 'content-type': 'application/json', 'content-length': '1317', 'connection': 'keep-alive', 'x-amzn-requestid': 'dbdb97ee-2ff1-48ad-a0e0-5c96f9ae29ff'}, 'RetryAttempts': 0}, 'stopReason': 'stop_sequence', 'metrics': {'latencyMs': 2785}} + +Usage metadata={'input_tokens': 1493, 'output_tokens': 248, 'total_tokens': 1741} + +The second model provides the most detailed and informative response, including specific details about the pre-training and post-training stages of the Llama 3 model.",1.0,0.8254964353972353,0.9999999999 +mixture-rag-mixtral-8x7-instruct-thought,8aa2e14e-9837-40cf-803d-b21e145748b2,What is the difference between the Llama 2 and Llama 2-Chat ?," Based on the provided context, Llama 2 and Llama 2-Chat are two different models developed by Meta AI. Llama ",1.0,0.2004054968005838,0.8333333332916666 +mixture-rag-mixtral-8x7-instruct-thought,dc897e29-c6b8-4eef-9f29-253f9231a42c,What is optimizer is used for LLaMA?," Based on the provided context, the optimizer used for training the Llama models is AdamW. Specifically, the context states: + +""We trained using the AdamW optimizer (Loshchilov and Hutter, 2017), with β1 = 0.9, β2 = 0.95, eps = 10−5."" + +AdamW is a variant of the popular Adam optimizer that incorporates weight decay. It is a common choice for training large language models like Llama due to its effectiveness in optimizing the model parameters during the training process. + +(Response from the second model)",0.5,0.8127409402092972,0.3333333333 +mixture-rag-mixtral-8x7-instruct-thought,0cd42b13-8b7b-4c6f-b62c-29dac848cddf,Can the GPT-4 model accept both text and image inputs?," Yes, the GPT-4 model can accept both text and image inputs. It exhibits similar capabilities as it does on text-only inputs when given documents with text and photographs, diagrams, or screenshots. This means that the model can generate text outputs given inputs consisting of arbitrarily interlaced text and images. This multimodal input ability allows the model to handle a wider range of tasks and domains beyond just pure text. + +(This response is a summary of the responses provided by all three models.)",0.9,1.0,0.8333333332916666 +mixture-rag-mixtral-8x7-instruct-thought,de0d4b9d-8b54-4b5d-9ca2-82040f6dd85b,On what architecture the GPT-3 model is based on?," Based on the provided context, the GPT-3 model is based on the Transformer architecture, with some additional improvements and modifications compared to the original Transformer design. These modifications include the use of pre-normalization, the SwiGLU activation function, and rotary embeddings, which were inspired by recent work on large language models.",0.8571428571428571,0.9935035488418335,0.249999999975 +mixture-rag-mixtral-8x7-instruct-thought,f67ea058-65f5-4233-8eea-172a0d786730,How many stages of training are in the GPT model?," According to the context provided, the GPT models are trained in two stages. First, the models are trained using a large dataset of text from the Internet to predict the next word. This is the initial pre-training stage. The models are then fine-tuned with additional data, using an algorithm called reinforcement learning from human feedback (RLHF), to produce outputs that are preferred by human labelers. This is the fine-tuning stage. So in summary, the GPT models go through two main stages of training - the initial pre-training stage on a large text dataset, followed by a fine-tuning stage using reinforcement learning from human feedback.",0.7272727272727273,0.7985030137812528,0.9999999999 +mixture-rag-mixtral-8x7-instruct-thought,708c62e1-b8b2-4295-be1d-7bc2ee8bec5e,What tokenizer is used in the Gemma2 model?," The Gemma 2 models use a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings, and the vocabulary size is 256,000 entries.",1.0,0.9668872845590216,0.49999999995 +mixture-rag-mixtral-8x7-instruct-thought,60490cdc-40a6-4cff-b963-245dc4b517d9,On what architecture the Gemma model is based on?," The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017). + +(Response from the third model)",1.0,0.980834227715684,0.9999999999 +mixture-rag-mixtral-8x7-instruct-thought,7228f5a1-476d-42c9-8c09-270f3d8d5d2a,Does Claude 3 models have vision capabilities?," Yes, Claude 3 models have vision capabilities. They can recognize and identify objects visually, and can think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics. The models can process images, including tables, graphs, and photos, and can even generate text from images with hard-to-read handwriting. Additionally, they can handle multimodal input, which includes the ability to process images along with text prompts.",1.0,0.9942643083073888,0.8333333332916666 +mixture-rag-mixtral-8x7-instruct-thought,1b4c18e5-bc37-4d92-ad86-9aa66c77ad7d,What are the two tasks in BERT?," Based on the provided context, the two main tasks in BERT are: + +1. Pre-training: During this step, the BERT model is trained on large amounts of unlabeled data over different pre-training tasks. This allows the model to learn general language representations that can be leveraged for downstream tasks. + +2. Fine-tuning: In this step, the pre-trained BERT model is initialized with the learned parameters from the pre-training phase. The model is then fine-tuned on labeled data from specific downstream tasks, such as question answering, text classification, or named entity recognition. The fine-tuning process allows the model to adapt and specialize its learned representations for the target task. + +The key idea behind BERT is that the same pre-trained model can be effectively fine-tuned for a wide variety of natural language processing tasks, with minimal changes to the model architecture. This allows BERT to leverage the general language understanding gained during pre-training and apply it to specific applications. + +(Response from the second model)",0.4666666666666667,0.9679117087732756,0.3333333333 +mixture-rag-claude-3-haiku-modified,ab4c991e-1b03-41b6-9c47-db93384de179,How can attention be described in the Transformer?,"Based on the responses provided by the three small language models, here is a comprehensive and informative summary: + +The Transformer model utilizes a mechanism called multi-head self-attention in both its encoder and decoder components. This attention mechanism is a key feature that allows the Transformer to effectively model long-range dependencies and understand complex relationships within the input text. + +The core idea behind self-attention is that each position in the input sequence can attend to all other positions, enabling the model to capture the relationships between words regardless of their distance. The ""multi-head"" aspect refers to the fact that the Transformer employs 8 parallel attention layers (heads), each of which processes the information slightly differently. This allows the model to capture a richer and more nuanced understanding of the input. + +Attention in the Transformer is used in three distinct ways: + +1. Encoder-Decoder Attention: In this case, the queries come from the previous decoder layer, while the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. + +2. Encoder Self-Attention: The encoder contains self-attention layers, where the keys, values, and queries all come from the output of the previous encoder layer. This allows each position in the encoder to attend to all positions in the previous encoder layer. + +3. Decoder Self-Attention: Similarly, the self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. + +By using attention in these different ways, the Transformer is able to effectively model long-range dependencies and understand the complex relationships within the input text, enabling it to generate high-quality and informative outputs.",0.56,0.7575874974984704,0.7499999999625 +mixture-rag-claude-3-haiku-modified,d33b6fe0-f68a-4aad-bff6-fa70691e21b4,What is Mixture of Agents?,"Based on the responses provided by the three small language models, here is a synthesized response on the Mixture-of-Agents (MoA) approach: + +The Mixture-of-Agents (MoA) is a novel methodology that leverages the collective strengths of multiple Large Language Models (LLMs) to achieve superior performance compared to any single LLM. The key idea behind MoA is to construct a layered architecture where each layer consists of multiple LLM agents. + +In the MoA approach: + +1. The first layer consists of ""Proposer Agents"" - these LLMs are skilled at generating diverse and useful initial responses to a given prompt. + +2. The outputs from the Proposer Agents are then passed to the next layer, which consists of ""Aggregator Agents"". These LLMs are adept at taking the diverse responses and combining them into a single, high-quality output. + +3. The iterative process continues, with each layer of agents using the outputs from the previous layer as additional context to refine and improve the responses. + +This collaborative approach allows the MoA system to harness the complementary capabilities of the individual LLMs. By building on each other's strengths, the MoA methodology has been shown to outperform even state-of-the-art models like GPT-4 Omni on various benchmarks. + +In essence, the Mixture-of-Agents approach is a powerful way to leverage the collective intelligence of multiple large language models, leading to more accurate, comprehensive, and robust responses than any single model could achieve alone.",0.7307692307692307,0.5917719533985579,0.999999999975 +mixture-rag-claude-3-haiku-modified,ad82cf3b-7c50-47fd-951f-9ae21e039343,Is Mixtral based on the idea of a mixture of experts?,"Based on the responses provided by the three small language models, it is clear that Mixtral is a Sparse Mixture of Experts (SMoE) language model, which is a type of neural network architecture that combines the outputs of multiple ""expert"" modules to produce a final output. + +The key points are: + +1. Mixtral is explicitly described as a ""Sparse Mixture of Experts (SMoE) language model"" in the text. + +2. Each layer of Mixtral is composed of multiple feedforward blocks or ""expert"" modules. + +3. A routing mechanism, or ""router network"", selects a subset of these experts to process the current token and combine their outputs. + +4. This allows Mixtral to use a fraction of its total parameters for each token, while still achieving state-of-the-art performance. + +In summary, Mixtral is a language model that uses a Mixture of Experts (MoE) architecture, where multiple specialized ""expert"" modules are combined dynamically to process each input token. This sparse and selective use of parameters is a key aspect of the Mixtral model.",0.875,0.5544537985811971,0.9999999999666667 +mixture-rag-claude-3-haiku-modified,e3226837-27ec-4d43-91ee-2315e95731e6,What is sliding window attention?,"Based on the responses provided by the three small language models, here is a consolidated response on sliding window attention: + +Sliding window attention is a technique used in large language models (LLMs) to improve efficiency and handle longer sequences. It works by limiting the attention mechanism, where each token only attends to a fixed number of previous tokens (defined by the ""window size"") instead of attending to the entire sequence. + +The key aspects of sliding window attention are: + +1. Limited Attention: Each token only attends to a subset of the previous tokens, defined by the window size, rather than the entire sequence. + +2. Moving Window: As the model processes the sequence, this ""window"" of attended tokens slides forward, allowing each token to access information from a growing context. + +3. Recursive Access: Because the model has multiple layers, the effective window size expands with each layer. A token at a later layer can access information from tokens much earlier in the sequence, up to a limit determined by the window size and the number of layers. + +The benefits of sliding window attention include: + +- Reduced Computational Cost: By limiting attention to a smaller window, the number of calculations required is significantly reduced, making the model faster and more efficient. +- Improved Memory Management: The sliding window approach allows for a fixed-size cache, making it easier to manage memory, especially when dealing with very long sequences. + +Sliding window attention is a key component of the Mistral 7B model, where the window size is set to 4096, allowing the model to attend to information from up to 131K tokens. This is a significant improvement over traditional attention mechanisms, which can be computationally expensive and memory-intensive, especially for longer sequences. + +Overall, sliding window attention is a technique that enables large language models to handle longer sequences more effectively while maintaining efficiency and performance.",0.7272727272727273,0.7658352370558137,0.9999999999 +mixture-rag-claude-3-haiku-modified,9ba17321-4b63-42fb-9a49-397682efe62d,How many stages are there in the development of the Llama 3 model?,"Based on the responses provided by the three small language models, here is a consolidated response summarizing the key points about the development of the Llama 3 model: + +The development of the Llama 3 model involves two main stages: + +1. Language Model Pre-Training: + - This stage involves training the model on a massive, multilingual text dataset to learn the structure of language and acquire general world knowledge. + - The pre-training process converts the text corpus into discrete tokens and trains a large language model to perform next-token prediction tasks. + +2. Language Model Post-Training: + - In this stage, the pre-trained language model is further aligned with human feedback and preferences through supervised fine-tuning, rejection sampling, and direct preference optimization. + - The goal of this stage is to make the model better at following instructions and behaving like a helpful assistant, tailored to human preferences. + +The responses from the three models provide a consistent and comprehensive overview of the two-stage development process for the Llama 3 model. By synthesizing the key points from the individual responses, this consolidated response aims to provide an accurate and informative summary of the model's development lifecycle.",0.65,0.9385616026326954,0.9999999999 +mixture-rag-claude-3-haiku-modified,931f60b9-2c2d-4cd8-8dcc-126e7154a03d,What is the difference between the Llama 2 and Llama 2-Chat ?,"Based on the responses provided by the three small language models, here is a consolidated and high-quality response: + +Llama 2 and Llama 2-Chat are two distinct models developed by Meta AI. + +Llama 2 is the base language model, trained on a large corpus of publicly available data. It is a general-purpose model that can be used for a variety of natural language processing tasks. Llama 2 comes in different parameter sizes (7B, 13B, and 70B), allowing for flexibility in model capacity and performance. + +In contrast, Llama 2-Chat is a specialized version of Llama 2 that has been fine-tuned for dialogue tasks. The Llama 2-Chat model has been further trained on conversational data, making it better equipped to understand and respond to questions and prompts in a chat-like format. Like Llama 2, Llama 2-Chat is also available in 7B, 13B, and 70B parameter sizes. + +The key differences between the two models are: + +1. Purpose: Llama 2 is a general-purpose language model, while Llama 2-Chat is specifically designed for dialogue and conversational tasks. +2. Training: Llama 2 is pre-trained on a large corpus of text data, while Llama 2-Chat is fine-tuned on a smaller dataset focused on dialogue. +3. Architecture: While both models use a transformer-based architecture, Llama 2-Chat may have additional components or modifications to better support dialogue tasks. + +In summary, Llama 2 is a powerful, general-purpose language model, while Llama 2-Chat is a specialized version optimized for dialogue and conversational interactions.",0.6086956521739131,0.974208066262647,0.8333333332916666 +mixture-rag-claude-3-haiku-modified,d361e252-0a25-4d42-88e0-0096f74879a1,What is optimizer is used for LLaMA?,"Based on the responses from the three small language models, the following key points can be summarized: + +1. The LLaMA model was trained using the AdamW optimizer, which is a variant of the Adam optimizer. + +2. The specific hyperparameters used for the AdamW optimizer in the LLaMA training are: + - β1 = 0.9 + - β2 = 0.95 + - ε = 10^(-5) + +3. Additionally, the training used a cosine learning rate schedule with a warmup of 2000 steps and a final learning rate of 10% of the peak learning rate. + +4. A weight decay of 0.1 and gradient clipping of 1.0 were also applied during the training. + +In summary, the LLaMA model was trained using the AdamW optimizer with the specified hyperparameters, along with a cosine learning rate schedule, weight decay, and gradient clipping. This information provides a comprehensive overview of the key training details for the LLaMA model based on the responses from the small language models.",0.8461538461538461,0.8783241435593233,0.3333333333 +mixture-rag-claude-3-haiku-modified,286c7533-1909-4667-bacb-9f774e24a946,Can the GPT-4 model accept both text and image inputs?,"Based on the responses from the three small language models, the consensus is that GPT-4 can indeed accept both text and image inputs. The key points are: + +1. The provided context states that ""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task."" This clearly indicates that GPT-4 has the capability to process inputs with both text and images. + +2. The second model's response provides more details, mentioning that ""examples of GPT-4's visual input capabilities can be found in Table 3 of the provided context."" This suggests that the model can generate text outputs based on inputs that include a combination of text and images. + +3. The third model's response also confirms that GPT-4 ""can generate text outputs given inputs consisting of arbitrarily interlaced text and images."" + +In summary, the consensus from the three small language models is that GPT-4 can indeed accept and process inputs that consist of both text and images, and can generate relevant text outputs based on these multimodal inputs. The responses are consistent and provide a clear and informative answer to the original query.",0.4285714285714285,0.9692922226109744,0.8333333332916666 +mixture-rag-claude-3-haiku-modified,1b11eff0-59ef-4f42-bfd8-9dd8268879dc,On what architecture the GPT-3 model is based on?,"Based on the responses provided by the three small language models, here is a consolidated and high-quality response: + +The GPT-3 model is based on the transformer architecture, which was first introduced by Vaswani et al. in 2017. However, the GPT-3 model incorporates several improvements and modifications to the original transformer architecture: + +1. Pre-normalization: The model uses pre-normalization, where the input of each transformer sub-layer is normalized using the RMSNorm function, instead of normalizing the output. + +2. SwiGLU activation function: The model uses the SwiGLU activation function, which replaces the standard ReLU non-linearity to improve performance. + +3. Rotary embeddings: The model removes the use of absolute positional embeddings and instead uses rotary embeddings, which are added to the input. + +These modifications were inspired by recent advancements in large language models, such as the PaLM model. The goal of these improvements is to enhance the performance and capabilities of the GPT-3 model compared to the original transformer architecture. + +Overall, the GPT-3 model builds upon the foundational transformer architecture, while incorporating several innovative techniques to push the boundaries of large language model performance.",0.8421052631578947,0.6515175821310805,0.249999999975 +mixture-rag-claude-3-haiku-modified,fa6860a2-cbab-4be0-9e25-71403910e98b,How many stages of training are in the GPT model?,"Based on the responses provided by the three small language models, here is a consolidated response: + +The GPT model is trained in two main stages. In the first stage, the model is trained on a large dataset of text from the internet to learn how to predict the next word in a sequence. This allows the model to develop a general understanding of language and text. + +In the second stage, the GPT model is fine-tuned using a technique called Reinforcement Learning from Human Feedback (RLHF). In this stage, the model is further trained on additional data, with the goal of producing outputs that are preferred by human reviewers. The RLHF process helps refine the model's outputs to be more aligned with human preferences and judgments. + +Through this two-stage training process, the GPT model is able to leverage large-scale unsupervised learning on internet data, while also being fine-tuned to generate text that is more coherent, relevant, and preferred by humans. This combination of broad language understanding and targeted human feedback allows the GPT model to produce high-quality, contextually appropriate text outputs.",0.5714285714285714,0.6807735367657081,0.9999999999 +mixture-rag-claude-3-haiku-modified,a5f08a8c-c4e2-4503-bb0e-98143b138354,What tokenizer is used in the Gemma2 model?,"Based on the responses provided by the three small language models, the key information about the tokenizer used in the Gemma 2 model can be summarized as follows: + +The Gemma 2 model uses the same SentencePiece tokenizer as Gemma 1 and Gemini. This tokenizer has the following features: + +1. **Split digits**: Digits are split into individual tokens. +2. **Preserved whitespace**: Whitespace is preserved as tokens. +3. **Byte-level encodings**: Text is encoded at the byte level. + +The resulting vocabulary size of this tokenizer is 256,128 entries. + +The responses from the three models are consistent in describing the key characteristics of the Gemma 2 tokenizer. By combining the information from the individual responses, we can provide a comprehensive and accurate summary of the tokenizer used in the Gemma 2 model.",0.6666666666666666,0.9813343521521736,0.49999999995 +mixture-rag-claude-3-haiku-modified,7fe65040-0299-41cb-81d3-32742750f3d1,On what architecture the Gemma model is based on?,"Based on the responses provided by the three small language models, I can summarize the key information about the Gemma model as follows: + +The Gemma model is based on the transformer decoder architecture, as described in the paper ""Attention is All You Need"" by Vaswani et al. (2017). This architecture is commonly used in large language models and is known for its effectiveness in handling a wide range of natural language processing tasks. + +The transformer decoder architecture is a type of neural network that uses the attention mechanism to process sequential data, such as text. It has been shown to be highly effective in tasks like language modeling, machine translation, and text generation. + +The responses from the three models consistently mention that the Gemma model is built on the transformer decoder architecture, providing a clear and coherent understanding of the model's underlying structure. By combining the information from the individual responses, I can provide a comprehensive and accurate summary of the Gemma model's architecture.",0.05882352941176471,0.9824451804350853,0.9999999999 +mixture-rag-claude-3-haiku-modified,a03b08cf-4e11-4272-87c2-39f6ce8702cd,Does Claude 3 models have vision capabilities?,"Based on the responses from the three small language models, it is clear that the Claude 3 models have vision capabilities. The key points that can be synthesized from the responses are: + +1. The Claude 3 models can recognize and identify objects visually. They can understand both the appearance of an object and its connections to concepts like mathematics. + +2. The Claude 3 models, including the Claude 3 Haiku model, can process multimodal input that includes images, such as tables, graphs, and photos. They can even generate text from images with hard-to-read handwriting. + +3. While the Claude 3 models have impressive visual capabilities, it is important to note that their performance may be less robust for low-resource languages and they may generate inaccurate information about images. Therefore, they should not be used for consequential use cases that require high precision and accuracy without human validation. + +In summary, the Claude 3 models have strong vision capabilities, allowing them to recognize and understand visual information in a variety of contexts. However, their outputs should be carefully validated, especially for critical applications, to ensure accuracy and reliability.",0.7727272727272727,0.9830251010857803,0.5833333333041666 +mixture-rag-claude-3-haiku-modified,ca25bb01-f5e2-4c5d-afd0-a8e3fc878dec,What are the two tasks in BERT?,"Based on the responses from the three small language models, the key points about the two main tasks in BERT are: + +1. Pre-training: + - The BERT model is trained on a massive amount of unlabeled text data using techniques like masked language modeling (MLM) and next sentence prediction (NSP). + - The purpose of pre-training is to learn general language understanding from large-scale data. + +2. Fine-tuning: + - After pre-training, the BERT model is further trained on smaller, labeled datasets specific to downstream tasks such as question answering, sentiment analysis, or text classification. + - The fine-tuning process adjusts all the parameters of the pre-trained model to fit the specific downstream task. + +In summary, the two main tasks in BERT are: +1. Pre-training on unlabeled data to learn general language understanding +2. Fine-tuning the pre-trained model on labeled data for specific downstream tasks + +The responses from the three models are consistent and provide a comprehensive overview of the two key tasks in BERT. By synthesizing the information from the individual responses, I have created a single, high-quality response that is accurate, informative, and faithful to the provided context.",0.4736842105263158,0.9679117087732756,0.3333333333 +mixture-rag-gemma2-9b-it-modified,c701d363-7016-48b1-9ad0-b1c9cb4179d7,How can attention be described in the Transformer?,"The Transformer model utilizes multi-head attention in three distinct ways: + +**1. Encoder-Decoder Attention:** + +- Queries originate from the previous decoder layer. +- Memory keys and values are derived from the encoder's output. +- This enables each position in the decoder to attend to all positions within the input sequence, mirroring traditional encoder-decoder attention mechanisms found in sequence-to-sequence models. + +**2. Encoder Self-Attention:** + +- All keys, values, and queries stem from the output of the preceding layer in the encoder. +- Each position within the encoder can attend to all positions in the previous encoder layer. + +**3. Decoder Self-Attention:** + +- Self-attention layers in the decoder allow each position to attend to all positions within the decoder up to and including its current position. +- This mechanism prevents ""leftward"" information flow, ensuring that a position only attends to previously generated information. + +The Transformer employs multi-head attention, computing attention scores using multiple attention heads in parallel. This allows the model to simultaneously focus on diverse representation subspaces and positions within the input. The outputs of these multiple heads are then concatenated and linearly transformed to produce the final output. + +This self-attention mechanism empowers the Transformer to capture long-range dependencies within both input and output sequences, eliminating the need for recurrent or convolutional layers. Consequently, the Transformer achieves greater computational efficiency, parallelizable computation, leading to faster training and inference. + + +",0.5,0.6909779734621088,0.9999999999 +mixture-rag-gemma2-9b-it-modified,0380ae35-ab05-4f7e-8df8-7ee50e417cbb,What is Mixture of Agents?,"Mixture of Agents (MoA) is a novel methodology that leverages the collaborative power of multiple large language models (LLMs) to significantly enhance the quality of generated responses. + +Here's how it works: + +**1. Layered Architecture:** MoA employs a layered architecture where each layer consists of multiple LLM ""agents."" Each agent in a layer utilizes the outputs from all agents in the preceding layer as auxiliary information to generate its own response. This iterative refinement process continues for several cycles, culminating in a more robust and comprehensive final response. + +**2. Proposer and Aggregator Roles:** The agents within MoA are categorized into two distinct roles: + +* **Proposers:** These agents excel at generating useful reference responses. +* **Aggregators:** These agents are adept at synthesizing responses from other models into a single, high-quality output. + +**3. Collaborative Enhancement:** MoA capitalizes on the ability of LLMs to learn and improve from each other. By referencing outputs from other models, even those of lower quality, LLMs can refine their own responses and collectively achieve higher accuracy and comprehensiveness. + +**4. State-of-the-Art Performance:** MoA has demonstrated remarkable performance on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK, surpassing even powerful models like GPT-4 Omni. This highlights the effectiveness of leveraging the collective expertise of multiple LLMs through the MoA methodology. + + +In essence, Mixture of Agents represents a paradigm shift in how we utilize LLMs, enabling them to collaborate and achieve performance levels that surpass the capabilities of individual models. +",0.88,0.7036913399983197,0.8055555555287036 +mixture-rag-gemma2-9b-it-modified,0adb645e-4519-430d-bf2b-f9e9b106039c,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is based on a Sparse Mixture of Experts (SMoE) architecture. + +Here's a breakdown of how it works: + +* **Experts:** Each layer in Mixtral is composed of 8 feedforward blocks, which act as ""experts."" +* **Router Network:** A special network called a router selects two of these experts for each token at each layer. +* **Dynamic Selection:** The selected experts can change at every timestep, allowing a token to access the full 47B parameters of the model while only actively using 13B during inference. + +This sparse mixture-of-experts approach allows Mixtral to achieve high performance while being more efficient than a standard dense transformer model. + + +",0.8888888888888888,0.6248105141574571,0.9999999999666667 +mixture-rag-gemma2-9b-it-modified,7db341ea-a19a-4625-81ec-5d55e94902a8,What is sliding window attention?,"Sliding Window Attention (SWA) is a technique used in the Mistral 7B language model to improve efficiency and performance. + +Here's how it works: + +Traditional attention mechanisms allow each token to attend to all other tokens in the input sequence. This can be computationally expensive, especially for long sequences. SWA addresses this by limiting the attention span of each token. + +Instead of considering all tokens, each token can only attend to a fixed number (W) of tokens from the previous layer. This ""window size"" (W) is set to 4096 in Mistral 7B, enabling the model to attend to information from up to 131,072 tokens (32 layers * 4096 window size) at the final layer. + +This constraint reduces the computational cost from quadratic to linear, making the model more efficient. Information can still flow across longer sequences because the model stacks multiple attention layers. After k attention layers, a token can effectively attend to tokens up to k*W positions away in the original input sequence. + +SWA allows Mistral 7B to handle longer sequences more effectively while reducing memory requirements and computational costs during decoding, making it more practical for real-world applications. + + +",0.5909090909090909,0.56767737031783,0.9999999999 +mixture-rag-gemma2-9b-it-modified,4016bd04-51f0-4300-8655-909cc5a301b1,How many stages are there in the development of the Llama 3 model?,"The development of the Llama 3 language model involves two main stages: + +**1. Language Model Pre-training:** + +* A large language model is pre-trained on a massive multilingual text corpus. +* This pre-training involves learning the structure of language and acquiring knowledge from the text through next-token prediction. +* A 405B parameter model is trained on 15.6T tokens with an initial context window of 8K tokens, followed by continued pre-training to increase the context window to 128K tokens. + +**2. Language Model Post-training:** + +* The pre-trained language model is further refined by aligning it with human feedback. +* This is achieved through several rounds of supervised fine-tuning, rejection sampling, and direct preference optimization. +* The goal of post-training is to ensure the model behaves as expected of an assistant, going beyond just understanding language. + + +This two-stage process allows Llama 3 to develop a strong foundation in language understanding and be fine-tuned for helpful and aligned behavior. +",0.8823529411764706,0.8247055771772133,0.9999999999 +mixture-rag-gemma2-9b-it-modified,9a1b345e-5b0a-4ff4-869b-5d9017ad3901,What is the difference between the Llama 2 and Llama 2-Chat ?,"Llama 2 and Llama 2-Chat are two language models developed by Meta AI, both built upon an optimized transformer architecture and trained on a new mix of publicly available data. + +Here's a breakdown of their key differences: + +* **Purpose:** Llama 2 is a general-purpose language model, capable of handling a variety of natural language generation tasks. Llama 2-Chat, on the other hand, is specifically fine-tuned for dialogue use cases, aiming to be helpful and safe in interactive conversations. + +* **Training:** While Llama 2 undergoes pre-training on a large corpus of text, Llama 2-Chat is further fine-tuned on a smaller dataset focused on dialogue tasks. This fine-tuning process helps Llama 2-Chat excel in conversational settings. + +* **Capabilities:** Llama 2 is available in different parameter sizes (7B, 13B, and 70B) and can be adapted for various NLP tasks. Llama 2-Chat also comes in the same parameter sizes and demonstrates superior performance compared to other open-source models in both single-turn and multi-turn dialogue prompts. + +* **Safety and Alignment:** Llama 2-Chat models have been evaluated for safety and alignment with human preferences, outperforming other open-source models in these areas. This suggests additional safety and alignment efforts were made during the fine-tuning process for Llama 2-Chat. + + + +In essence, Llama 2 provides a strong foundation for general language understanding, while Llama 2-Chat builds upon that foundation to become a specialized and capable conversational AI. +",0.7142857142857143,0.974208066262647,0.8333333332916666 +mixture-rag-gemma2-9b-it-modified,e4fc31a6-7268-481d-8c3f-254ac54d995f,What is optimizer is used for LLaMA?,"The LLaMA model was trained using the AdamW optimizer. AdamW is a variant of the Adam optimizer that incorporates weight decay, making it a popular choice for training large language models. + +The training process used a cosine learning rate schedule with a warmup period of 2000 steps. The learning rate decayed to 10% of its peak value. Additional hyperparameters included a weight decay of 0.1 and gradient clipping of 1.0. + +Specifically, the AdamW optimizer used the following settings: + +* β1 = 0.9 +* β2 = 0.95 +* eps = 10-5 + + +These hyperparameters, along with the chosen optimizer, contribute to the effective training and performance of the LLaMA model. +",0.5384615384615384,0.8987633724662137,0.3333333333 +mixture-rag-gemma2-9b-it-modified,3f24a335-b83f-4da8-aa57-9c67da754140,Can the GPT-4 model accept both text and image inputs?,"Yes, the GPT-4 model can accept both text and image inputs. + +As stated in the provided context, GPT-4 ""accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task."" This means the model can generate text outputs based on inputs that include arbitrarily interlaced text and images. + +GPT-4 demonstrates similar capabilities to its text-only counterpart when presented with documents containing text and visuals like photographs, diagrams, or screenshots. + + +",1.0,1.0,0.8333333332916666 +mixture-rag-gemma2-9b-it-modified,e5b20cc5-8fcd-437a-b7ef-d831049e252c,On what architecture the GPT-3 model is based on?,"The GPT-3 model is based on the transformer architecture, originally proposed by Vaswani et al. in 2017. + +However, GPT-3 incorporates several key improvements inspired by subsequent work on large language models like PaLM and GPTNeo. These include: + +* **Pre-normalization:** Normalizing the input of each transformer sub-layer instead of the output, using the RMSNorm function. +* **SwiGLU activation function:** Replacing the standard ReLU non-linearity to potentially improve performance. +* **Rotary embeddings:** Replacing absolute positional embeddings with rotary embeddings, which may offer advantages in handling longer sequences. + + +These modifications build upon the foundational transformer architecture to enhance the capabilities of the GPT-3 model. +",0.875,0.6380521118475492,0.249999999975 +mixture-rag-gemma2-9b-it-modified,79ad24ca-021b-4dfb-8fd7-d46ff9cc6cc0,How many stages of training are in the GPT model?,"GPT models are trained in two main stages. + +The first stage involves training the model on a massive dataset of text from the internet to predict the next word in a sequence. This foundational training allows the model to learn language patterns and structures. + +The second stage involves fine-tuning the model using reinforcement learning from human feedback (RLHF). During this stage, the model is exposed to additional data and is rewarded for producing outputs that are preferred by human labelers. This process helps the model generate more human-like and contextually relevant responses. + + +This summary combines the information provided by all three models, emphasizing the two-stage training process and highlighting the key aspects of each stage. +",0.5,0.6703254856570627,0.9999999999 +mixture-rag-gemma2-9b-it-modified,b3374f91-fa1a-4d0d-9007-4ad0d0688f08,What tokenizer is used in the Gemma2 model?,"The Gemma 2 models use the same tokenizer as Gemma 1 and Gemini. This tokenizer is a SentencePiece tokenizer with the following characteristics: + +* **Split digits:** Digits are separated into individual tokens. +* **Preserved whitespace:** Whitespace characters are treated as separate tokens. +* **Byte-level encodings:** Tokens are represented using byte-level encodings. + +The resulting vocabulary size for this tokenizer is 256,000 entries. + + +This information is consistent across all three provided model responses. +",0.8571428571428571,0.9359972863514265,0.49999999995 +mixture-rag-gemma2-9b-it-modified,11c805b8-fa70-4c55-8080-812bc4948728,On what architecture the Gemma model is based on?,"The Gemma model architecture is based on the transformer decoder architecture, as described in the research paper by Vaswani et al. (2017). +",1.0,0.9813712119554845,0.9999999999 +mixture-rag-gemma2-9b-it-modified,c2b814bd-a3e5-46a9-adc0-c7cf89c0aa6b,Does Claude 3 models have vision capabilities?,"Yes, Claude 3 models possess vision capabilities. + +They can recognize and identify objects visually, understanding not only their appearance but also their connections to concepts like mathematics. This allows them to process images, including tables, graphs, and photos, and even generate text from images with hard-to-read handwriting. + +Furthermore, Claude 3 models can accept multimodal input, meaning they can process both text and images together. This expands their capabilities and allows for richer context in understanding user requests. + +It's important to note, however, that the performance of these models may be less robust for low-resource languages and they might generate inaccurate information about images. Therefore, for critical applications requiring high precision and accuracy, human validation is recommended. +",1.0,0.9942643083073888,0.9999999999666667 +mixture-rag-gemma2-9b-it-modified,1ef5a7ca-e615-4899-aafd-678ff614af7f,What are the two tasks in BERT?,"The two main tasks in BERT are **pre-training** and **fine-tuning**. + +**Pre-training** involves training the model on a massive amount of unlabeled text data using tasks like Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). This allows BERT to learn general language representations and understanding. + +**Fine-tuning** takes the pre-trained BERT model and further trains it on a smaller, labeled dataset specific to a downstream task, such as question answering, sentiment analysis, or text classification. This step adapts the pre-existing knowledge to the specific requirements of the target task. + + +Essentially, pre-training equips BERT with a broad understanding of language, while fine-tuning specializes it for a particular application. +",0.5294117647058824,0.9679117087732756,0.8333333332916666 +mixture-rag-llama3.1-8b-instruct-modified,939f4a44-5db5-43c3-87eb-77f574a0dd74,How can attention be described in the Transformer?," +The Transformer model uses a mechanism called multi-head self-attention in both its encoder and decoder. This allows the model to understand relationships between words regardless of their distance and capture long-range dependencies in the input and output sequences. + +There are three different ways in which the Transformer uses attention: + +1. **Encoder-Decoder Attention**: The queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence, mimicking traditional encoder-decoder attention mechanisms in sequence-to-sequence models. +2. **Encoder Self-Attention**: All of the keys, values, and queries come from the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder. +3. **Decoder Self-Attention**: Each position in the decoder can attend to all positions in the decoder up to and including that position. This allows the model to handle long-range dependencies within the decoder sequence. + +The Transformer uses a multi-head attention mechanism, which allows the model to focus on different positions and aspects of the input simultaneously. This is achieved by splitting the input into multiple ""heads,"" each with its own attention weights, and then concatenating the results. The attention function itself is implemented as a dot product of the query, key, and value vectors, followed by a softmax operation to compute the weights. + +The use of self-attention in the Transformer allows the model to capture long-range dependencies in the input and output sequences, without the need for recurrent or convolutional layers. This makes the Transformer more computationally efficient and able to parallelize the computation, which is particularly beneficial for long sequences.",0.7419354838709677,0.7575874974984704,0.8055555555287036 +mixture-rag-llama3.1-8b-instruct-modified,849f120d-e3d1-4957-8b7d-f96ceb73d9c3,What is Mixture of Agents?," +Mixture-of-Agents (MoA) is a novel methodology that leverages the collective strengths of multiple large language models (LLMs) through an iterative, collaborative process to generate high-quality, robust responses. This approach involves multiple layers of agents, where each layer's agents take the outputs from agents in the previous layer as auxiliary information to generate their responses. The agents in MoA are categorized into two roles: Proposers and Aggregators. Proposers excel at generating useful reference responses, while Aggregators are proficient in synthesizing responses from other models into a single high-quality output. + +The MoA methodology has demonstrated state-of-the-art performance on various benchmarks, including AlpacaEval 2.0, MT-Bench, and FLASK. For example, an MoA model using only open-source LLMs achieved the top position on AlpacaEval 2.0, outperforming even GPT-4 Omni. This is because MoA harnesses the collective intelligence of multiple LLMs, allowing them to build upon each other's ideas and produce better results than any single LLM could achieve alone. + +The key ideas behind MoA are: + +1. Collaborativeness of LLMs: LLMs can improve their responses by referencing outputs from other models, even if the auxiliary responses are of lower quality. +2. MoA Architecture: MoA constructs a layered architecture where each layer comprises multiple LLM ""agents"". Each agent in a layer takes the outputs from all agents in the previous layer as auxiliary information to generate its own response. +3. Roles of Agents: The agents in MoA are categorized into two roles - ""Proposers"" and ""Aggregators"". Proposers excel at generating useful reference responses, while Aggregators are proficient in synthesizing responses from other models into a single high-quality output. + +In summary, Mixture of Agents is a novel methodology that leverages the collective strengths of multiple LLMs through an iterative, collaborative process to generate high-quality, robust responses, outperforming individual LLM models.",0.875,0.6236265068677259,0.8055555555287036 +mixture-rag-llama3.1-8b-instruct-modified,7349b908-2c71-4c27-b9ec-281943452d2c,Is Mixtral based on the idea of a mixture of experts?," +Yes, Mixtral is indeed based on the idea of a Mixture of Experts (MoE) architecture, specifically a Sparse Mixture of Experts (SMoE) model. It is designed to have a large number of parameters, but only use a fraction of them during inference. Each layer of Mixtral is composed of multiple feedforward blocks (or ""experts""), and for every token, a router network selects a few experts to process the current state and combine their outputs. This allows each token to have access to a large number of parameters, but only use a fraction of them during inference. The specific architecture of Mixtral is similar to the Mistral 7B model, but with each layer composed of 8 feedforward blocks (experts), and the selected experts can be different at each timestep. This sparse mixture-of-experts approach increases the model's parameter count while controlling the cost and latency, as the model only uses a fraction of the total parameters per token.",1.0,0.6214621404013302,0.9999999999666667 +mixture-rag-llama3.1-8b-instruct-modified,64329326-3ca8-4cd3-a53b-b16d7d18d062,What is sliding window attention?," +Sliding Window Attention (SWA) is a technique used in large language models (LLMs) to improve efficiency and handle longer sequences. It works by limiting the attention span of each token to a fixed-size ""window"" of tokens from the previous layer, rather than the entire sequence. This approach reduces the number of operations from quadratic to linear in relation to the sequence length, and decreases memory usage. + +Here's how it works: + +1. **Limited Attention:** Each token can only attend to a fixed number (W) of tokens from the previous layer. +2. **Moving Window:** As the model processes the sequence, this ""window"" slides forward, allowing each word to access information from a growing context. +3. **Recursive Access:** Because the model has multiple layers, the window effectively expands with each layer. A word at a later layer can access information from words much earlier in the sequence, as the window size is multiplied by the number of layers. + +The benefits of SWA include: + +* **Reduced Computational Cost:** By limiting attention to a smaller window, the number of calculations is significantly reduced, making the model faster and more efficient. +* **Improved Memory Management:** The sliding window approach allows for a fixed-size cache, making it easier to manage memory, especially when dealing with very long sequences. + +In the case of the Mistral 7B model, the window size (W) is set to 4096, which allows the model to attend to up to 131,072 tokens (32 layers * 4096 window size) at the final layer, despite only attending to a local window at each individual layer. This mechanism allows for more parallelization and can improve the translation quality of the model, making it more efficient and practical for real-world use. + +Overall, Sliding Window Attention is a powerful technique for improving the efficiency and performance of attention mechanisms in large language models, making it more practical for real-world applications.",0.5714285714285714,0.6434670148375657,0.9999999999 +mixture-rag-llama3.1-8b-instruct-modified,58dbb2f1-8b7c-4e47-9934-72d840e2bdc4,How many stages are there in the development of the Llama 3 model?," +The development of Llama 3 has two main stages: + +1. **Language model pre-training:** This involves training the model on a massive text dataset to understand language structure and world knowledge. The pre-training is performed at a massive scale, with a large, multilingual text corpus used to perform next-token prediction and learn the structure of language. This is followed by a continued pre-training stage that increases the supported context window to 128K tokens. + +2. **Language model post-training:** This stage aligns the pre-trained model with human feedback through several rounds of supervised fine-tuning, rejection sampling, and direct preference optimization. This post-training stage is aimed at making the model behave in the way expected of an assistant, beyond just having a rich understanding of language. + +In summary, the development of the Llama 3 model involves two main stages: language model pre-training and language model post-training.",1.0,0.8916191702652686,0.9999999999 +mixture-rag-llama3.1-8b-instruct-modified,42d89012-85c4-428c-9752-f1c32688d9a3,What is the difference between the Llama 2 and Llama 2-Chat ?," +Based on the responses from the three models, here is a single, high-quality response that combines the information and provides a clear and concise explanation of the differences between Llama 2 and Llama 2-Chat: + +Llama 2 is the base model, trained on a massive dataset of public text. It is a general-purpose language model that can be adapted for various tasks such as writing creative content, translating languages, and answering questions in an informative way. Llama 2-Chat, on the other hand, is a fine-tuned version of Llama 2, specifically optimized for dialogue use cases. It is designed to be excellent at conversations, understanding context in a back-and-forth dialogue, and providing more natural and engaging interactions. + +The key differences between Llama 2 and Llama 2-Chat are: + +1. **Pretraining vs. Fine-tuning**: Llama 2 is a base model that was pre-trained on a new mix of publicly available data, while Llama 2-Chat is a fine-tuned version of Llama 2, optimized specifically for dialogue use cases. +2. **Intended Use Cases**: Llama 2 is intended for commercial and research use in English, while Llama 2-Chat is intended for assistant-like chat applications. +3. **Safety Improvements**: Llama 2-Chat has undergone additional safety-focused fine-tuning or optimization compared to the base Llama 2 model, resulting in better performance on safety metrics. + +In summary, Llama 2-Chat is a fine-tuned version of the Llama 2 base model, optimized specifically for dialogue and assistant-like applications, with a focus on improving safety and alignment with human preferences.",0.7692307692307693,0.974208066262647,0.8333333332916666 +mixture-rag-llama3.1-8b-instruct-modified,8e65c36e-7292-4676-b724-d1f63bc9f82c,What is optimizer is used for LLaMA?," +Based on the responses from the three models, I can provide a single, high-quality response that combines the information from each model. + +The LLaMA model was trained using the AdamW optimizer, which is a variant of the popular Adam optimizer that incorporates weight decay. The learning rate schedule used is a cosine learning rate schedule, with a warmup of 2000 steps and decaying the final learning rate decaying down to 10% of the peak learning rate. Additionally, a weight decay of 0.1 and gradient clipping of 1.0 were applied. The specific hyperparameters used for AdamW are β1 = 0.9, β2 = 0.95, and eps = 10−5. + +This response combines the information from each model, providing a comprehensive overview of the training process used for the LLaMA model.",0.6,0.7733279037099465,0.3333333333 +mixture-rag-llama3.1-8b-instruct-modified,77818770-3942-49db-9dc2-906ae24bfa6d,Can the GPT-4 model accept both text and image inputs?," +Yes, the GPT-4 model can accept both text and image inputs. It is designed to process and generate text outputs based on inputs that consist of arbitrarily interlaced text and images. This capability is demonstrated across a range of domains, including documents with text and photographs, diagrams, or screenshots. The model exhibits similar capabilities in handling these multimodal inputs as it does with text-only inputs. Preliminary results on a narrow set of academic vision benchmarks have been released, with more information about GPT-4's visual capabilities planned for future work. + +This capability allows the model to be used for a wider range of tasks that involve both language and vision, such as generating text descriptions of images, understanding the content of images, and more. The model's ability to accept both text and image inputs makes it a powerful tool for a variety of applications, including but not limited to: + +* Image captioning: generating text descriptions of images +* Visual question answering: understanding the content of images and answering questions about them +* Image classification: categorizing images into different categories +* Object detection: identifying objects within images + +Overall, the GPT-4 model's ability to accept both text and image inputs makes it a versatile and powerful tool for a wide range of applications.",0.6842105263157895,1.0,0.9999999999 +mixture-rag-llama3.1-8b-instruct-modified,3c826d64-8f76-4fd2-8a7c-6c5a6de9f839,On what architecture the GPT-3 model is based on?," +The GPT-3 model is based on the Transformer architecture, with several improvements drawn from models like PaLM and GPTNeo. These improvements include pre-normalization, the SwiGLU activation function, and the use of rotary embeddings instead of absolute positional embeddings. The core underlying architecture is the Transformer, which was originally proposed in the paper by Vaswani et al. in 2017. + +This response is a combination of the three provided responses, with the most accurate and informative information. The first response provides the basic information about the Transformer architecture, the second response provides more details about the improvements made to the original Transformer, and the third response provides a summary of the architecture and its improvements.",0.25,0.923064671539441,0.249999999975 +mixture-rag-llama3.1-8b-instruct-modified,99b2a7e4-3744-4f98-b08f-db19522eed49,How many stages of training are in the GPT model?," +The GPT model is trained in two stages. In the first stage, the model is trained to predict the next word using a large dataset of text from the Internet. In the second stage, the model is fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers. This two-stage training process involves pre-training on a large text dataset, followed by fine-tuning using reinforcement learning from human feedback. + +Note: I combined the information from all three models to create a single, high-quality response. The response is accurate, informative, and faithful to the previous responses.",0.6666666666666666,0.6807735367657081,0.9999999999 +mixture-rag-llama3.1-8b-instruct-modified,b9ac512c-9113-4ae0-9308-122514c8b8ae,What tokenizer is used in the Gemma2 model?," +The Gemma 2 model uses the same tokenizer as Gemma 1 and Gemini: a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings. The resulting vocabulary has 256k entries. + +This response is based on the majority of the responses from the three models, which all agree on the type of tokenizer used by the Gemma 2 model. The first and second models mention that the tokenizer is a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings, while the third model provides more details about the tokenizer, including the fact that it has a 256k entry vocabulary.",0.5454545454545454,0.9805458999710387,0.49999999995 +mixture-rag-llama3.1-8b-instruct-modified,8580cae6-5b91-45b5-ac2b-50188aefec68,On what architecture the Gemma model is based on?," +The Gemma model is based on the transformer decoder architecture, as described in the research paper by Vaswani et al. (2017). The transformer decoder architecture, proposed by Vaswani et al. in 2017, is a type of neural network architecture that has been widely used in natural language processing tasks, including language modeling, machine translation, and text generation. It is characterized by the use of self-attention mechanisms to capture long-range dependencies in the input sequence, and a decoder-only structure that generates output tokens one at a time. + +This response is a combination of the three provided responses, with the first two sentences from the first model, the third sentence from the second model, and the last two paragraphs from the third model. This response maintains the accuracy and relevance of the original responses while providing a more comprehensive and detailed explanation of the Gemma model architecture.",0.1176470588235294,0.9824451804350853,0.9999999999 +mixture-rag-llama3.1-8b-instruct-modified,bed2a6e4-48d9-4e47-8901-bdab633b59fb,Does Claude 3 models have vision capabilities?," +Yes, based on the provided context, Claude 3 models do have vision capabilities. They can recognize and identify objects visually, and can interpret images in a table format, organizing the text from these images into a JSON format. They can also think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics. However, it's essential to note that while they excel in new multimodal capabilities, they can sometimes generate inaccurate information and descriptions about images. Therefore, they should not be used for consequential use cases that require high precision and accuracy without human validation, and performance can be lower for small or low-resolution images. + +This response is based on the majority of the information provided by the three models, which all agree that Claude 3 models have vision capabilities. The response also includes some additional information from the second model about the potential limitations of the models' vision capabilities, such as the possibility of generating inaccurate information and the importance of human validation.",0.8,0.9942643083073888,0.9999999999666667 +mixture-rag-llama3.1-8b-instruct-modified,85d24519-f088-4e7f-acf5-cbdec781fec7,What are the two tasks in BERT?," +The two main tasks in BERT are: + +1. **Pre-training:** This involves training the model on a massive amount of unlabeled text data using various techniques like masked language modeling (MLM) and next sentence prediction (NSP). The purpose of pre-training is to learn general language understanding from large-scale data. + +2. **Fine-tuning:** After pre-training, the BERT model is fine-tuned using labeled data from downstream tasks. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters. Fine-tuning involves adjusting all the parameters of the pre-trained model to fit the specific task. Examples of downstream tasks include question-answering, sentiment analysis, linguistic acceptability, semantic textual similarity, and named entity recognition. + +The key idea is that the same pre-trained BERT model can be fine-tuned for various downstream tasks, with minimal changes to the model architecture. This allows BERT to achieve strong performance on a wide range of natural language processing tasks by leveraging the knowledge acquired during the pre-training stage. + +Note: The responses from the three models are consistent in describing the two main tasks in BERT, which are pre-training and fine-tuning. The pre-training stage involves training the model on large amounts of unlabeled data, while the fine-tuning stage involves adjusting the pre-trained model to fit specific downstream tasks.",0.64,0.9679117087732756,0.8333333332916666 +mixture-rag-mixtral-8x7-instruct-modified,9465f127-24fa-4863-9fbd-560405067ace,How can attention be described in the Transformer?," The Transformer model uses attention in several ways to process the input sequence and compute a representation of it. The attention mechanism is implemented through a multi-head self-attention mechanism, where multiple attention heads attend to different parts of the input sequence simultaneously. This allows the model to weigh the importance of different positions in the sequence and capture long-range dependencies. + +In more detail, the Transformer uses attention in the following ways: + +1. Encoder self-attention: Each position in the encoder can attend to all positions in the previous layer of the encoder, allowing the model to capture dependencies between distant positions in the input sequence. +2. Decoder self-attention: Each position in the decoder can attend to all positions in the decoder up to and including that position, preventing leftward information flow and ensuring that the prediction for a position can depend only on the known outputs at positions less than that position. +3. Encoder-decoder attention: Every position in the decoder can attend over all positions in the input sequence. This allows the decoder to focus on relevant parts of the input when generating the output. + +The key aspects of attention in the Transformer are that it allows the model to draw global dependencies between input and output without regard to their distance, it is used in multiple ways (self-attention in the encoder, self-attention in the decoder, and encoder-decoder attention), and it enables more parallelization compared to recurrent models, as the attention computations can be done in parallel for all positions. Overall, the Transformer's use of multi-head attention is a crucial component that allows it to effectively model long-range dependencies in the input and output sequences, without relying on sequential computation like recurrent models.",0.65,0.6779143079969745,0.8055555555287036 +mixture-rag-mixtral-8x7-instruct-modified,464927bd-6757-4bd0-92ac-f06ec3ac9628,What is Mixture of Agents?," The Mixture-of-Agents (MoA) is a new approach that leverages the collective strengths of multiple Large Language Models (LLMs) in a layered architecture to achieve boosted performance. In this methodology, each layer consists of multiple LLM agents. The agents in each layer take the outputs generated by the agents in the previous layer as auxiliary information, refining and improving the responses iteratively in several cycles. The goal is to obtain a more robust and comprehensive final response, which outperforms single LLM models and LLM-based rankers in terms of robustness, correctness, factuality, and completeness. + +In the MoA methodology, LLMs are categorized into two distinct roles: Proposers and Aggregators. Proposer models excel at generating useful reference responses, offering more context and diverse perspectives. These contributions enhance the final responses when used by an aggregator. Aggregator models are proficient in synthesizing responses from other models into a single, high-quality output, maintaining or enhancing output quality even when using responses from other models. + +The MoA approach has been shown to significantly outperform LLM rankers and achieve state-of-the-art performance on various benchmarks, surpassing models such as GPT-4 Omni on tests like AlpacaEval 2.0, MT-Bench, and FLASK.",1.0,0.5828538215371643,0.999999999975 +mixture-rag-mixtral-8x7-instruct-modified,4701b040-6e63-474f-a5aa-f99b7fbb17d1,Is Mixtral based on the idea of a mixture of experts?," Yes, Mixtral is a Sparse Mixture of Experts (SMoE) language model, as stated in the responses from all three models. This architecture is characterized by each layer being composed of multiple ""expert"" groups of parameters, with a router network selecting a subset of these experts for each token at each layer. The outputs of the selected experts are then combined. This setup allows Mixtral to have a large number of parameters (47B) while only using a smaller subset (13B) for each token during inference, making it more efficient. Each token can access the full set of parameters, while only the selected experts are actively used. This approach enables Mixtral to achieve high performance while using a smaller number of active parameters compared to other models like Llama 2 70B.",0.9230769230769231,0.5473960036485092,0.9999999999666667 +mixture-rag-mixtral-8x7-instruct-modified,0c829e65-d74f-4052-86cb-4bdf8f45b364,What is sliding window attention?," Sliding Window Attention (SWA) is a technique used in large language models, such as Mistral 7B, to improve the efficiency and scalability of attention mechanisms. It allows each token to only attend to a fixed-size ""window"" of tokens from the previous layer, rather than the entire sequence. This reduces the computational cost and memory requirements of the attention mechanism. By stacking multiple attention layers, the effective attention span can be increased, allowing the model to capture long-range dependencies while maintaining efficient computation and memory usage, especially for long input sequences. The window size is a hyperparameter that determines the number of tokens that can be attended to. For example, if the window size is 4096, each token can attend to at most 4096 tokens from the previous layer. This approach has several benefits, including reduced computational costs, improved memory efficiency, and better handling of long sequences. In the context of Mistral 7B, SWA is used in conjunction with grouped-query attention (GQA) to accelerate inference speed and reduce memory requirements.",0.5238095238095238,0.6045555727819492,0.7499999999625 +mixture-rag-mixtral-8x7-instruct-modified,f642337b-5955-476b-9826-ad55edd89421,How many stages are there in the development of the Llama 3 model?," The development of the Llama 3 model consists of two main stages: + +1. **Language model pre-training:** In this stage, a large, multilingual text corpus is converted into discrete tokens and used to pre-train a large language model (LLM) to perform next-token prediction. The pre-training is carried out at a massive scale, with a 405B parameter model trained on 15.6T tokens using a context window of 8K tokens. This is followed by a continued pre-training stage that increases the supported context window to 128K tokens. + +2. **Language model post-training:** After pre-training, the model is aligned with human feedback through several rounds of supervised finetuning, rejection sampling, and direct preference optimization. Additionally, multimodal capabilities are integrated into Llama 3 during this stage. + +In summary, the Llama 3 model undergoes two main stages: language model pre-training and language model post-training, which involve learning the structure of language, obtaining world knowledge, and aligning the model with desired behaviors and human feedback.",0.9565217391304348,0.9385616026326954,0.9999999999 +mixture-rag-mixtral-8x7-instruct-modified,b86c21a9-4ab2-44ea-87e1-ac17c8d5aa99,What is the difference between the Llama 2 and Llama 2-Chat ?," Llama 2 and Llama 2-Chat are both models based on the same foundational model, Llama 2, which has been updated with improvements in pretraining. The key differences between the two lie in their purpose, training process, and intended use. + +Llama 2 is a general-purpose language model that can be adapted for a variety of natural language generation tasks, such as text completion, language translation, and text summarization. It is trained on a large corpus of text data using an auto-regressive transformer architecture. + +Llama 2-Chat, however, is a fine-tuned version of Llama 2 specifically designed for dialogue use cases. It is trained using Reinforcement Learning with Human Feedback (RLHF) methodologies, which involve iterative refinement of the model using human feedback to improve its performance on conversational dialogue tasks. + +In terms of training process, Llama 2 undergoes pretraining on a new mix of publicly available data, while Llama 2-Chat is created through supervised fine-tuning and further refinement using RLHF techniques, such as rejection sampling and Proximal Policy Optimization (PPO). + +The models are available in different parameter sizes, including 7B, 13B, and 70B variants, with Llama 2-Chat being optimized for dialogue and chat use cases.",0.84,0.974208066262647,0.8333333332916666 +mixture-rag-mixtral-8x7-instruct-modified,973e05f9-cced-47d9-8cf7-b32e18cd2350,What is optimizer is used for LLaMA?," Based on the responses from the small language models, the optimizer used for training the Llama 2 model is the AdamW optimizer. This information was consistently reported by all the models, providing a strong consensus on the answer. The AdamW optimizer is a variant of the popular Adam optimizer, which is widely used for training deep learning models. The context also specified other hyperparameters used, such as the cosine learning rate schedule, weight decay, and gradient clipping. In summary, the use of the AdamW optimizer for training the Llama models is a confirmed detail.",0.5454545454545454,0.7695362337969436,0.3333333333 +mixture-rag-mixtral-8x7-instruct-modified,83cb144a-4534-4ff5-a1be-7623c13c023e,Can the GPT-4 model accept both text and image inputs?," Yes, the GPT-4 model is capable of accepting both text and image inputs. This has been consistently stated across the responses from the small language models. The model can generate text outputs given inputs consisting of arbitrarily interlaced text and images. It exhibits similar capabilities when handling documents that contain text and photographs, diagrams, or screenshots, as it does with text-only inputs.",0.8888888888888888,0.9692922226109744,0.9999999999 +mixture-rag-mixtral-8x7-instruct-modified,e51ce39f-1554-4fa7-b2cc-4f40b94f6fe4,On what architecture the GPT-3 model is based on?," The GPT-3 model is based on the transformer architecture, as described in the paper by Vaswani et al., 2",0.6666666666666666,0.9935035488418335,0.0 +mixture-rag-mixtral-8x7-instruct-modified,96c3bf5c-f7dd-4461-a6ad-e0cce0be39c8,How many stages of training are in the GPT model?," The GPT models are trained in two stages, according to all the responses. In the first stage, they are trained on a large dataset of text from the Internet to predict the next word, which is the initial pre-training stage. Then, in the second stage, the models are fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF). This fine-tuning stage helps produce outputs that are preferred by human labelers. Therefore, the GPT models go through two main stages of training - the initial pre-training on a large text dataset, followed by fine-tuning using reinforcement learning from human feedback.",0.9230769230769231,0.6703254856570627,0.9999999999 +mixture-rag-mixtral-8x7-instruct-modified,b3c1cfe5-9c33-4213-a316-8d71100ff5eb,What tokenizer is used in the Gemma2 model?," The Gemma 2 models use a SentencePiece tokenizer, which is the same as the one used in Gemma 1 and Gemini models. This tokenizer includes split digits, preserved whitespace, and byte-level encodings, and has a vocabulary size of 256k entries. + +(This response is based on the majority of the responses from the small language models, which provided consistent information.)",1.0,0.9668872845590216,0.49999999995 +mixture-rag-mixtral-8x7-instruct-modified,6c492325-3148-44a5-bbde-3bf10ad612ca,On what architecture the Gemma model is based on?," The Gemma model is based on the transformer decoder architecture, as described in the paper by Vaswani et al. in 2017. This information is provided in the context given and is consistent across responses from the small language models.",0.75,0.9947045503209968,0.9999999999 +mixture-rag-mixtral-8x7-instruct-modified,4a9db86f-838d-4bc9-b16b-81debf44cc5c,Does Claude 3 models have vision capabilities?," Yes, the Claude 3 models have vision capabilities. This has been consistently stated by all the small language models. They can recognize and identify objects visually, and have the ability to understand an object's appearance and its connections to concepts like mathematics. Furthermore, they have multimodal input capabilities, which allow users to upload images along with text prompts for richer context and expanded use cases. This enables the Claude 3 models to handle a wider range of tasks and use cases that involve both text and images.",0.8181818181818182,0.9830251010857803,0.9999999999666667 +mixture-rag-mixtral-8x7-instruct-modified,3c4cbe40-0fce-4448-ae14-cd804ad52ebd,What are the two tasks in BERT?," Based on the responses from the small language models, the two main tasks in BERT are: + +1. **Pre-training:** This phase involves training the model on a massive amount of unlabeled text data using techniques such as Masked Language Modeling and Next Sentence Prediction. The model learns general language understanding during this stage. + +2. **Fine-tuning:** After pre-training, the model is further trained on a smaller, labeled dataset specific to a downstream task, such as question answering, sentiment analysis, or text classification. The pre-trained model's parameters are used to initialize the models for different downstream tasks, and all parameters are then fine-tuned during this stage. + +These two tasks are the core components of the BERT framework, and they are used to train and fine-tune the model for various downstream tasks. The same underlying BERT architecture is used in both the pre-training and fine-tuning stages, with minimal differences between the pre-trained and final downstream architectures. This allows the pre-trained BERT model to be effectively transferred and fine-tuned for various downstream tasks.",0.7222222222222222,0.9679117087732756,0.8333333332916666 +mixture-rag-mixtral-8x7-instruct,d97df213-83f6-42b2-8a55-d7f3a8086e4c,How can attention be described in the Transformer?," The Transformer model utilizes a mechanism called multi-head self-attention in both its encoder and decoder. This allows the model to build a representation of the input sequence where each word's meaning is enriched by considering the context of all other words. + +Multi-head self-attention works in three distinct ways: + +1. **Encoder-Decoder Attention:** This type of attention allows every position in the decoder to attend over all positions in the input sequence. The queries come from the previous decoder layer, while the memory keys and values come from the output of the encoder. + +2. **Encoder Self-Attention:** The encoder contains self-attention layers where all of the keys, values, and queries come from the same place, specifically, the output of the previous layer in the encoder. This allows each position in the encoder to attend to all positions in the previous layer of the encoder. + +3. **Decoder Self-Attention:** The self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. This is done to prevent ""leftward"" attention, which would allow a position to attend to future positions. + +The Transformer model uses multi-head attention, where the model employs 8 parallel attention layers or ""heads"". This allows the model to jointly attend to information from different representation subspaces at different positions. The reduced dimension of each head keeps the total computational cost similar to that of single-head attention with full dimensionality. + +In essence, attention in the Transformer model is a mechanism that allows the model to weigh the importance of different input elements when computing a representation of the input sequence. It does this by allowing the model to attend to different parts of the input sequence in parallel and weigh the importance of different input elements based on their relevance to the current position in the sequence.",0.6818181818181818,0.6579449711329778,0.8055555555287036 +mixture-rag-mixtral-8x7-instruct,7b76dc10-0b46-4b80-9591-fea4f426b8d7,What is Mixture of Agents?," Mixture of Agents (MoA) is a methodology that leverages multiple large language models (LLMs) to iteratively enhance the generation quality of responses. In this approach, a layered architecture is constructed where each layer consists of multiple LLM ""agents"". The first layer has agents that generate initial responses to a prompt. Subsequent layers take the responses from the previous layer as input and use them to refine and improve the responses. This iterative process continues until a final, more comprehensive and robust response is generated. + +The agents in MoA are categorized into two roles: Proposers and Aggregators. Proposers excel at generating useful reference responses that provide context and diverse perspectives. Aggregators, on the other hand, are proficient in synthesizing responses from other models into a single, high-quality output. + +MoA has been shown to significantly outperform LLM-based rankers and achieve state-of-the-art performance on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK. It demonstrates improvements in various aspects like robustness, correctness, efficiency, factuality, commonsense, insightfulness, and completeness, surpassing even GPT-4 Omni. + +In summary, MoA harnesses the collective intelligence of multiple LLMs, allowing them to learn from each other and produce better results than any single model could achieve alone. By leveraging the collaborative capabilities of LLMs, MoA can achieve state-of-the-art performance on various natural language understanding and generation tasks.",0.8620689655172413,0.7036913399983197,0.999999999975 +mixture-rag-mixtral-8x7-instruct,c367a6e3-b54f-4332-9643-fcb5641df2f8,Is Mixtral based on the idea of a mixture of experts?," Yes, Mixtral is a language model based on the Sparse Mixture of Experts (SMoE) architecture. Each layer of Mixtral is composed of 8 feedforward blocks, or ""experts."" When processing a token, a router network selects two of these eight experts to process the current state and combine their outputs. Although each token only uses two experts, the selected experts can be different at each timestep, allowing each token to have access to the full 47B parameters of the model during inference. This sparse mixture-of-experts approach increases the model's parameter count while controlling cost and latency, as the model only uses a fraction of the total parameters per token. This design allows Mixtral to achieve high performance while being more efficient than a standard dense transformer model.",0.8235294117647058,0.4946264511836652,0.9999999999666667 +mixture-rag-mixtral-8x7-instruct,645a072c-1632-46a6-a19a-a4112386cdc0,What is sliding window attention?," Sliding Window Attention (SWA) is a technique used in the Mistral 7B language model to enhance efficiency and manage longer sequences. This approach introduces a constraint where each token can only attend to a fixed-size ""window"" of tokens from the previous layer, rather than the entire sequence. Specifically, the hidden state at position i in layer k can only attend to hidden states between positions i-W and i in the previous layer k-1, where W is the window size. + +The primary benefits of SWA include reduced computational and memory requirements for the attention mechanism, leading to faster inference speeds. Despite the constrained attention, the model can still capture long-range dependencies by stacking multiple attention layers. After k attention layers, a token can effectively attend to tokens up to k*W positions away in the original input sequence. + +In the case of Mistral 7B, the window size W is set to 4096, which allows the model to attend to tokens up to 131,072 positions away (32 layers * 4096 window size) at the final layer. This mechanism significantly improves the model's ability to handle longer sequences more effectively at a reduced computational cost. It limits the number of tokens that the model needs to consider at each attention layer, reducing memory requirements and computational costs during decoding. + +In summary, Sliding Window Attention is a crucial component of the Mistral 7B model, enabling it to achieve high performance while maintaining efficiency by trading off some attention flexibility for significant improvements in computational efficiency, making it more practical for real-world applications.",0.75,0.605109979287087,0.9999999999 +mixture-rag-mixtral-8x7-instruct,f5dc38db-2a3a-4d9c-a288-7bda627cbfe8,How many stages are there in the development of the Llama 3 model?," The development of the Llama 3 language model encompasses two primary stages: + +1. **Language model pre-training**: In this stage, the model is trained on a vast, multilingual text corpus, with the text converted into discrete tokens. The model learns the structure of language and acquires world knowledge from the text during this phase. For Llama 3, a 405B parameter model was initially pre-trained on 15.6T tokens using a context window of 8K tokens. This was followed by a continued pre-training stage that expanded the supported context window to 128K tokens. + +2. **Language model post-training**: After pre-training, the model is aligned with human feedback through multiple rounds of supervised fine-tuning, rejection sampling, and direct preference optimization. This stage helps the model to follow instructions and behave in a manner expected of a helpful assistant. + +In summary, Llama 3's development consists of a language model pre-training stage, where the model learns from a large text corpus, and a post-training stage, where the model is aligned with human feedback to improve its ability to follow instructions and behave helpfully.",0.9565217391304348,0.8247055771772133,0.9999999999 +mixture-rag-mixtral-8x7-instruct,e17c6bad-7b15-4ff6-b0ea-c321c9536a24,What is the difference between the Llama 2 and Llama 2-Chat ?," Llama 2 and Llama 2-Chat are two models developed by Meta AI, with distinct differences in purpose, training, and architecture. Llama 2 is a general-purpose language model that uses an optimized transformer architecture and is pre-trained on a large corpus of publicly available text data. It is available in various parameter sizes (7B, 13B, and 70B) and can be adapted for a variety of natural language generation tasks. + +Llama 2-Chat, on the other hand, is a fine-tuned version of Llama 2 specifically optimized for dialogue use cases. It is also available in the same parameter sizes and is fine-tuned on a smaller dataset tailored for dialogue tasks. The fine-tuning process for Llama 2-Chat involves supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), aiming to improve its helpfulness, safety, and alignment with human preferences for interactive dialogue. + +The main differences between the two models are: + +1. Purpose: Llama 2 is a general-purpose language model, while Llama 2-Chat is designed for dialogue use cases. +2. Training: Llama 2 is pre-trained on a large corpus of text, while Llama 2-Chat is fine-tuned on a smaller dataset specifically for dialogue tasks. +3. Architecture: Both models use the same transformer architecture, but Llama 2-Chat may have additional components or modifications to support dialogue tasks. + +In summary, Llama 2 is a more versatile language model suitable for various natural language generation tasks, while Llama 2-Chat is a specialized model designed for dialogue tasks, offering improved safety, helpfulness, and alignment with human preferences.",0.8,0.974208066262647,0.8333333332916666 +mixture-rag-mixtral-8x7-instruct,b556f64b-8973-46d5-b106-a68a84d42ddc,What is optimizer is used for LLaMA?," The Llama 2 models were trained using the AdamW optimizer, as reported by all the small language models you provided. This information is mentioned in Section 2.2, ""Training Details,"" of the context, and it is also specified that the authors used the AdamW optimizer with the hyperparameters β1 = 0.9, β2 = 0.95, and eps = 10^(-5). AdamW is a variant of the Adam optimizer that includes weight decay, making it a popular choice for training large language models like Llama due to its effectiveness in optimizing model parameters during training. + +Further details from the context indicate that the training process for Llama models involved a cosine learning rate schedule with warmup, a weight decay of 0.1, and gradient clipping of 1.0. These hyperparameters are essential for the stable and efficient training of large-scale language models like Llama.",0.5333333333333333,0.7912460234215003,0.3333333333 +mixture-rag-mixtral-8x7-instruct,f956def1-3f5d-48ad-a7c1-250d58de25c7,Can the GPT-4 model accept both text and image inputs?," Yes, GPT-4 is capable of accepting both text and image inputs. This ability allows it to process and generate outputs based on prompts that contain both text and images, expanding its range of applications to tasks involving both visual and language components. The model can generate text outputs given inputs consisting of arbitrarily interlaced text and images, and it exhibits similar capabilities as it does on text-only inputs when dealing with documents containing text and photographs, diagrams, or screenshots. + +The information provided by the small language models is consistent in stating that GPT-4 can handle multimodal inputs. I have critically evaluated their responses and synthesized a coherent and accurate answer based on the given context.",0.7142857142857143,0.9692922226109744,0.9999999999 +mixture-rag-mixtral-8x7-instruct,3d1e0234-896c-4bb2-9182-bacaf20e93c0,On what architecture the GPT-3 model is based on?," The GPT-3 model is indeed based on the transformer architecture, as reported by all the language models you've provided. This architecture was first introduced by Vaswani et al. in 2017. The GPT-3 model incorporates several improvements and modifications to the original transformer architecture. These include the use of pre-normalization, where the input of each transformer sub-layer is normalized instead of the output, using the RMSNorm normalizing function. The SwiGLU activation function is another modification, replacing the ReLU non-linearity to improve performance. Lastly, rotary embeddings are used instead of absolute positional embeddings. These modifications have been influenced by recent work on large language models, including the PaLM model. + +In summary, while the GPT-3 model is based on the transformer architecture, it also features significant enhancements and alterations compared to the original Transformer design.",0.4117647058823529,0.6248956248447558,0.249999999975 +mixture-rag-mixtral-8x7-instruct,b6eec4fa-bda4-481d-800f-64bbcbbed145,How many stages of training are in the GPT model?," The GPT models, including the one you're interacting with now, are typically trained in two main stages. + +In the first stage, the models are trained on a vast dataset of text harvested from the Internet. The primary objective of this stage is to prepare the model to predict the next word in a given text sequence. This is achieved through a process known as pre-training. + +The second stage involves fine-tuning the model with additional data. This is done using a technique called reinforcement learning from human feedback (RLHF). The purpose of this stage is to produce outputs that are preferred by human reviewers or labelers. This process helps the model align its responses more closely with what humans would expect or consider appropriate. + +In summary, the GPT models undergo an initial pre-training stage on a large text dataset, followed by a fine-tuning stage that uses reinforcement learning from human feedback to shape the model's responses.",0.6153846153846154,0.6703254856570627,0.9999999999 +mixture-rag-mixtral-8x7-instruct,b872ed3b-9689-422b-8531-01980cd7bd5b,What tokenizer is used in the Gemma2 model?," The Gemma 2 models utilize a SentencePiece tokenizer, which also happens to be the same tokenizer used in the Gemma 1 and Gemini models. This tokenizer includes features such as split digits, preserved whitespace, and byte-level encodings. The resulting vocabulary has a total of 256,000 entries. This information is consistent across the responses received from the different language models, indicating a high level of agreement and reliability. + +To summarize: +- The Gemma 2 models use the SentencePiece tokenizer. +- This tokenizer is also used in the Gemma 1 and Gemini models. +- The SentencePiece tokenizer has split digits, preserved whitespace, and byte-level encodings. +- The vocabulary size of the SentencePiece tokenizer in the Gemma 2 models is 256,000 entries. + +Please note that the specifics of the tokenizer and its configuration can significantly impact the performance of language models. In this case, the SentencePiece tokenizer has been chosen for its ability to handle a wide range of language features effectively.",0.7,0.9663442990105505,0.49999999995 +mixture-rag-mixtral-8x7-instruct,6393f905-90b3-4ea5-bbfc-24111b7f8384,On what architecture the Gemma model is based on?," After synthesizing the responses from the small language models, I can confirm that the Gemma model architecture is indeed based on the transformer decoder, as described in the paper ""Attention is All You Need"" by Vaswani et al. (2017). This information is consistent across all three responses. Additionally, the Gemma models are reportedly inspired by the Gemini model family, which were developed by Google DeepMind, and thus inherit the architecture and design principles from the Gemini models, which also utilize the transformer decoder architecture. To summarize, the Gemma model architecture is based on the transformer decoder, and its development is influenced by the Gemini model family from Google DeepMind.",0.6,0.9824451804350853,0.9999999999 +mixture-rag-mixtral-8x7-instruct,57d3390e-f223-417e-929e-7999c70f0a85,Does Claude 3 models have vision capabilities?," Based on the consistent information provided by all three small language models, I can confirm that the Claude 3 models do indeed have vision capabilities. This ability includes recognizing and identifying objects visually, understanding an object's appearance, and its connections to concepts like mathematics. Furthermore, the Claude 3 models can process various types of images, such as tables, graphs, and photos. They can even generate text from images with challenging handwriting. This confirms that the models can handle multimodal input, which reinforces their visual information processing abilities.",0.7857142857142857,0.9830251010857803,0.9999999999666667 +mixture-rag-mixtral-8x7-instruct,3229b880-ab3d-4b9b-8ab8-09865a23f77a,What are the two tasks in BERT?," Based on the responses from the small language models, the two main tasks in BERT (Bidirectional Encoder Representations from Transformers) are: + +1. **Pre-training**: This is the first stage where the BERT model is trained on a massive amount of unlabeled text data. During this phase, the model utilizes techniques such as masked language modeling (MLM) and next sentence prediction (NSP) to learn general language representations. This pre-training stage enables the model to understand language context and relationships, which can be applied to various downstream tasks. + +2. **Fine-tuning**: After pre-training, the model undergoes fine-tuning on a smaller, labeled dataset specific to a particular downstream task, such as question answering, sentiment analysis, or text classification. In this stage, the pre-trained BERT model is initialized with the pre-trained parameters, and all the parameters are then fine-tuned using labeled data from the specific downstream tasks. Each downstream task has its own fine-tuned model, even though they all start from the same pre-trained BERT parameters. + +The two-stage approach of pre-training and fine-tuning allows BERT to learn general language understanding during the pre-training stage, which can then be effectively transferred to a wide range of specific tasks through the fine-tuning process. This method has been proven to be highly effective for many natural language processing tasks.",0.6,0.8890085053043496,0.8055555555287036 +mixture-rag-llama3.1-8b-instruct,1ebfcd39-cafc-42a8-90a7-a97cde059230,How can attention be described in the Transformer?," + +The Transformer model uses a mechanism called multi-head self-attention in both its encoder and decoder. This mechanism allows the model to draw global dependencies between input and output sequences, without the need for recurrence. + +There are three different ways in which attention is used in the Transformer: + +1. **Encoder-Decoder Attention**: The queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence, mimicking traditional encoder-decoder attention mechanisms in sequence-to-sequence models. +2. **Encoder Self-Attention**: All of the keys, values, and queries come from the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder. +3. **Decoder Self-Attention**: Each position in the decoder can attend to all positions in the decoder up to and including that position. This allows the model to handle long-range dependencies within the decoder sequence. + +The Transformer uses a multi-head attention mechanism, which allows the model to focus on different positions and aspects of the input simultaneously. This is achieved by splitting the input into multiple heads, each with its own attention weights, and then concatenating the results. The attention function itself is implemented as a dot product of the query, key, and value vectors, followed by a softmax operation to normalize the weights. + +The use of multi-head attention helps the Transformer model capture different types of dependencies without increasing the model's complexity significantly. This is necessary to prevent ""leftward"" information flow, which could allow a position to attend to future positions that have not yet been generated. + +In summary, the Transformer model uses multi-head self-attention in three different ways: encoder-decoder attention, encoder self-attention, and decoder self-attention. This allows the model to draw global dependencies between input and output sequences, handle long-range dependencies within the decoder sequence, and capture different types of dependencies without increasing the model's complexity.",0.4642857142857143,0.7575874974984704,0.7499999999625 +mixture-rag-llama3.1-8b-instruct,9d5c5447-ef68-4cdd-8b32-9a268a1878b0,What is Mixture of Agents?," + +Mixture-of-Agents (MoA) is a methodology that leverages multiple large language models (LLMs) to iteratively enhance the quality of generation. The approach involves dividing LLMs into different layers, with each layer's models independently generating responses to a given prompt. These responses are then presented to the next layer's models for further refinement. This iterative refinement process continues until a more robust and comprehensive response is obtained. + +The MoA methodology has been shown to significantly improve the robustness, correctness, efficiency, factuality, commonsense, insightfulness, and completeness of the generated responses compared to a single model's performance. The key ideas behind MoA are: + +1. Collaborativeness of LLMs: LLMs can improve their responses by referencing outputs from other models, even if the auxiliary responses are of lower quality. +2. MoA Architecture: MoA constructs a layered architecture where each layer comprises multiple LLM ""agents."" Each agent in a layer takes the outputs from all agents in the previous layer as auxiliary information to generate its own response. +3. Roles of Agents: The agents in MoA are categorized into two roles - ""Proposers"" and ""Aggregators."" Proposers excel at generating useful reference responses, while Aggregators are proficient in synthesizing responses from other models into a single high-quality output. + +The MoA approach has been shown to significantly outperform both individual LLMs and LLM-based rankers on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK. For example, an MoA model using only open-source LLMs achieved the top position on AlpacaEval 2.0, outperforming even GPT-4 Omni. + +In summary, Mixture of Agents is a novel methodology that harnesses the collective strengths of multiple LLMs to generate higher-quality and more robust responses through an iterative collaboration process.",0.9736842105263158,0.4553476039842344,0.999999999975 +mixture-rag-llama3.1-8b-instruct,bf61b964-6c4e-4cba-9e6a-f43d71d3519c,Is Mixtral based on the idea of a mixture of experts?," + +Mixtral is a Sparse Mixture of Experts (SMoE) language model. It is based on the idea of a Mixture of Experts (MoE), where each layer is composed of multiple feedforward blocks (or ""experts""). For every token, a router network selects a subset of these experts to process the current state and combine their outputs. This allows each token to have access to a large number of parameters, while only using a fraction of them during inference. + +The key points about Mixtral's architecture are: + +1. Mixtral has the same overall architecture as the Mistral 7B model, but with each layer composed of 8 feedforward blocks (experts). +2. At each layer, for every token, a router network selects two of these 8 experts to process the current state and combine their outputs additively. +3. This means that even though each token only uses 2 out of the 8 experts at each layer, the selected experts can be different for each token. +4. As a result, each token has access to the full 47B parameters of the model, but only actively uses 13B parameters during inference. +5. This sparse mixture-of-experts approach allows Mixtral to achieve high performance while using fewer active parameters per token compared to a dense model like Llama 2 70B. + +In summary, the core innovation of Mixtral is its sparse mixture-of-experts architecture, which selectively activates a subset of the model's parameters for each token, leading to efficient and high-performing language modeling.",0.8571428571428571,0.499860500381768,0.999999999975 +mixture-rag-llama3.1-8b-instruct,43886d8e-3b0e-4f05-84c5-98d723b31e3d,What is sliding window attention?," + +Sliding Window Attention (SWA) is a technique used in large language models (LLMs) to improve efficiency and handle longer sequences. It is a mechanism used in the Mistral 7B Large Language Model to manage the computational costs and inference latency associated with attention-based models. + +Here's how it works: + +1. **Attention Span Limitation**: In standard transformer models, the attention mechanism allows each token to attend to all other tokens in the input sequence. This results in quadratic computational complexity and memory usage, which can limit the length of sequences that can be processed efficiently. +2. **Sliding Window Approach**: To address this limitation, SWA introduces a sliding window approach. Instead of attending to all tokens, each token can only attend to a fixed-size window of tokens from the previous layer, typically set to a value of W (e.g., W=4096). +3. **Recursive Attention**: By stacking multiple attention layers, the effective attention span can be increased recursively. After k attention layers, a token can attend to tokens up to a distance of k×W from the input layer. +4. **Improved Efficiency**: The sliding window approach significantly reduces the computational complexity and memory requirements of the attention mechanism, from quadratic to linear in the sequence length. This allows the model to process longer sequences more efficiently, without sacrificing the ability to capture long-range dependencies. + +The benefits of Sliding Window Attention include: + +* Reduced computational cost: Attention is computationally expensive. Limiting the window size reduces the number of calculations needed. +* Improved memory efficiency: Storing attention information for every word is memory-intensive. Sliding window attention helps manage memory usage. +* Longer sequence handling: By recursively accessing information across layers, the effective attention span can be much longer than the window size. + +In the case of Mistral 7B, with a window size of W=4096 and 32 attention layers, the model can effectively attend to tokens up to a distance of around 131,000 tokens. This enables the model to handle long input sequences, which is crucial for real-world applications. + +In summary, Sliding Window Attention is a key architectural innovation in the Mistral 7B language model that enhances the efficiency and performance of the attention mechanism, allowing the model to process longer sequences without compromising its ability to capture long-range dependencies.",0.5833333333333334,0.6083801274682294,0.7499999999625 +mixture-rag-llama3.1-8b-instruct,1b1a1aab-7166-431b-8cc6-2db2f40077b4,How many stages are there in the development of the Llama 3 model?," + +The development of Llama 3 involves two main stages: language model pre-training and language model post-training. + +**Language Model Pre-Training:** + +The first stage is language model pre-training, where a large language model is pre-trained on a massive text dataset to learn the structure of language and world knowledge. This stage involves training the model on a large multilingual text corpus to perform next-token prediction. The pre-training is performed at a massive scale, with a large model trained on a large number of tokens using a context window of 8K tokens, followed by a continued pre-training stage that increases the supported context window to 128K tokens. + +**Language Model Post-Training:** + +The second stage is language model post-training, where the pre-trained model is aligned with human feedback through several rounds of supervised fine-tuning, rejection sampling, and direct preference optimization. This is done to make the model behave in the way expected of an assistant. + +In summary, the development of Llama 3 involves two main stages: language model pre-training, where the model learns the structure of language and world knowledge, and language model post-training, where the model is fine-tuned to behave like a helpful assistant.",1.0,0.8916191702652686,0.9999999999 +mixture-rag-llama3.1-8b-instruct,aadf8341-a5d3-4c72-9f61-e5944cd294c5,What is the difference between the Llama 2 and Llama 2-Chat ?," + +The key differences between Llama 2 and Llama 2-Chat are: + +1. **Purpose**: Llama 2 is the base model, trained on a massive dataset of public text, while Llama 2-Chat is a fine-tuned version of Llama 2, specifically optimized for dialogue and chat use cases. +2. **Training Process**: Llama 2 went through the pretraining stage using publicly available data, while Llama 2-Chat was created through supervised fine-tuning of the Llama 2 model, followed by further refinement using Reinforcement Learning with Human Feedback (RLHF) techniques. +3. **Model Sizes**: Both Llama 2 and Llama 2-Chat are available in multiple parameter sizes, including 7B, 13B, and 70B. Additionally, a 34B variant of Llama 2 was trained but is not being publicly released. +4. **Safety and Alignment**: The Llama 2-Chat models have been specifically optimized for safety and alignment with human preferences through the RLHF process, and have been shown to outperform other open-source and closed-source models in terms of safety evaluations. + +In summary, Llama 2 is the updated base model, while Llama 2-Chat is a fine-tuned version of Llama 2 that is optimized for dialogue and chat use cases, with a focus on safety and alignment with human preferences.",0.9523809523809523,0.9723849274629354,0.8333333332916666 +mixture-rag-llama3.1-8b-instruct,9cd93197-049b-4a4b-bc07-d9bd7b25c478,What is optimizer is used for LLaMA?," + +The LLaMA model was trained using the AdamW optimizer. The learning rate schedule used is a cosine learning rate schedule, with warmup of 2000 steps and decaying the final learning rate down to 10% of the peak learning rate. Additionally, a weight decay of 0.1 and gradient clipping of 1.0 were applied during training. The AdamW optimizer incorporates weight decay, which can help improve the generalization performance of the model.",0.75,0.7733279037099465,0.3333333333 +mixture-rag-llama3.1-8b-instruct,d6f28e6d-ea4d-4da2-878d-12e471ee2358,Can the GPT-4 model accept both text and image inputs?," + +The GPT-4 model is capable of accepting both text and image inputs. It can generate text outputs based on inputs that consist of arbitrarily interlaced text and images. This is similar to its text-only setting, allowing users to specify any vision or language task. + +The model can process and generate outputs based on prompts that contain both text and images, in addition to being able to handle text-only inputs. This multimodal capability allows the model to be used for a wider range of tasks that involve both visual and language components. + +Examples of GPT-4's visual input can be found in Table 3 of the provided context, and preliminary results on a narrow set of academic vision benchmarks are discussed in the GPT-4 blog post. More information about GPT-4's visual capabilities will be released in follow-up work. + +The model's ability to accept both text and image inputs is a significant feature that sets it apart from other language models. It allows users to specify any vision or language task, making it a versatile tool for a wide range of applications. + +In summary, the GPT-4 model is a powerful tool that can handle both text and image inputs, making it a valuable resource for tasks that require both visual and language components.",0.7777777777777778,0.7444278589409986,0.9999999999 +mixture-rag-llama3.1-8b-instruct,4948c8a4-c753-4547-bdec-ad6a81fae0b3,On what architecture the GPT-3 model is based on?," + +The GPT-3 model is based on the transformer architecture, which was originally proposed in the paper by Vaswani et al. in 2017. The transformer architecture has been improved upon in the GPT-3 model, drawing from models like PaLM and GPTNeo. Some of the key improvements include: + +* Pre-normalization: This is a technique used to normalize the input data before it is processed by the model. +* SwiGLU activation function: This is a type of activation function used in the model, which is designed to be more efficient and effective than traditional activation functions. +* Rotary embeddings: This is a technique used to represent positional information in the model, which is more efficient and effective than traditional absolute positional embeddings. + +Overall, the GPT-3 model is a variant of the transformer architecture that has been improved upon through the incorporation of these and other techniques.",0.8,0.6594314014099395,0.249999999975 +mixture-rag-llama3.1-8b-instruct,a5336210-d869-4f48-9a6a-a1a224a25197,How many stages of training are in the GPT model?," + +The GPT model is trained in two stages. In the first stage, it is trained on a massive dataset of text from the internet to predict the next word. This is the initial pre-training stage. In the second stage, the model is fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers. This fine-tuning stage refines the model's performance and ensures that it produces outputs that are more aligned with human preferences. + +It's worth noting that the GPT model's training process involves a large dataset of text from the internet, which is used to train the model to predict the next word. The model is then fine-tuned with additional data using RLHF, which helps to improve its performance and ensure that it produces outputs that are preferred by human labelers.",0.7857142857142857,0.6807735367657081,0.9999999999 +mixture-rag-llama3.1-8b-instruct,01fe92d0-6667-4a22-8cba-f6f45782b93f,What tokenizer is used in the Gemma2 model?," + +The Gemma 2 model uses the same tokenizer as Gemma 1 and Gemini, which is a SentencePiece tokenizer. This tokenizer has several features: + +* Split digits: Digits are split into individual tokens. +* Preserved whitespace: Whitespace is preserved as tokens. +* Byte-level encodings: Text is encoded at the byte level. + +This results in a vocabulary of 256,000 entries. + +The tokenizer is used to process text data and generate a vocabulary of 256,000 entries, which is a large vocabulary size. This suggests that the Gemma 2 model is designed to handle a wide range of text data and can learn to represent a large number of unique words and phrases. + +It's worth noting that the vocabulary size of 256,000 entries is a significant improvement over the vocabulary size of the Gemma 1 and Gemini models, which suggests that the Gemma 2 model has a more comprehensive understanding of language and can learn to represent a wider range of text data.",0.5,0.9348268701117112,0.49999999995 +mixture-rag-llama3.1-8b-instruct,f2804e67-99ae-49a8-b547-3defa3c012a4,On what architecture the Gemma model is based on?," +The Gemma model is based on the transformer decoder architecture, as described in the paper ""Attention is All You Need"" by Vaswani et al. (2017). This architecture is commonly used in large language models and is known for its effectiveness in handling a wide range of natural language processing tasks. + +The transformer decoder architecture, proposed by Vaswani et al. in 2017, is a type of neural network architecture that has been widely used in natural language processing tasks, including language modeling, machine translation, and text generation. It consists of a stack of transformer layers, each of which includes an attention mechanism and a feedforward neural network. + +By basing the Gemma model on the transformer decoder architecture, the researchers were able to leverage the strong performance and capabilities of this well-established model architecture for their own open-source language model. + +Note: I have synthesized the responses from the three models, ensuring that the information is accurate and comprehensive. I have also removed any redundant or unnecessary information to provide a clear and concise answer.",0.1818181818181818,0.9947045503209968,0.9999999999 +mixture-rag-llama3.1-8b-instruct,dc6f259c-bf4c-4fae-9de6-c48b6142ff7d,Does Claude 3 models have vision capabilities?," + +Based on the responses from the three small language models, I can synthesize the information to provide a comprehensive and accurate answer. + +The Claude 3 models have vision capabilities, which enable them to recognize and identify objects visually. They can process and analyze images, including tables, graphs, and photos, and can even understand the connections between visual information and concepts like mathematics. This multimodal input capability allows users to upload images along with text prompts for richer context and expanded use cases. + +While the models can generate descriptions about images, it's essential to note that their accuracy may not always be high, and therefore, they should not be used for consequential use cases that require high precision and accuracy without human validation. + +In summary, the Claude 3 models possess vision capabilities that allow them to recognize and understand visual information, in addition to their text-based capabilities, making them a powerful tool for a wide range of tasks and applications. + +Sources: + +* The text states: ""Claude 3 models can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object’s appearance and its connections to concepts like mathematics."" +* Figure 1 and Appendix B of the provided context demonstrate the multimodal input capabilities of the Claude 3 family. +* The context also mentions that the models can handle multimodal input, which includes the ability to process images along with text prompts. + +Note: The information provided by the three models is consistent and accurate, and there is no indication of any bias or incorrect information.",0.7,0.9246586432618362,0.9999999999666667 +mixture-rag-llama3.1-8b-instruct,3dc2dfa3-5ca5-4491-ac6c-864ace77ba18,What are the two tasks in BERT?," + +The two main tasks in BERT are: + +1. **Pre-training:** This involves training the model on a massive amount of unlabeled text data using various techniques like Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). The purpose of pre-training is to learn general language understanding from large-scale data. During the pre-training stage, the BERT model is trained on large amounts of unlabeled data over different pre-training tasks, allowing the model to learn general language representations that can be leveraged for downstream tasks. + +2. **Fine-tuning:** After pre-training, the BERT model is fine-tuned on labeled data from specific downstream tasks. Fine-tuning involves adjusting all the parameters of the pre-trained model to fit the specific task. The fine-tuned BERT model can then be used for various downstream tasks, such as question answering, sentiment analysis, linguistic acceptability, semantic textual similarity, and named entity recognition, among others. + +In summary, the two main tasks in BERT are pre-training on large unlabeled datasets to learn general language representations, followed by fine-tuning on labeled data for specific downstream tasks.",0.625,0.9679117087732756,0.8333333332916666 +mixture-rag-claude-3-haiku,395a9161-d701-4cf3-b9f1-f5ace39abd00,How can attention be described in the Transformer?,"Based on the responses provided by the small language models, here is a synthesized and accurate explanation of the role of attention in the Transformer model: + +The Transformer model uses a mechanism called multi-head self-attention in both its encoder and decoder components. Self-attention is a key aspect of the Transformer, as it allows each position in the input sequence to attend to all other positions in the same sequence. This enables the model to better understand the context of each word by considering its relationship to all the other words in the input. + +The multi-head aspect of the attention mechanism means that the Transformer uses multiple ""heads"" of attention simultaneously. Each attention head focuses on different aspects of the input, allowing the model to capture more complex relationships between words and build a richer representation of the input sequence. + +The Transformer uses multi-head attention in three distinct ways: + +1. Encoder-decoder attention: In this case, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. + +2. Encoder self-attention: Here, all the keys, values, and queries come from the output of the previous layer in the encoder. This allows each position in the encoder to attend to all positions in the previous layer of the encoder. + +3. Decoder self-attention: Similarly, the self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. + +The use of multi-head attention in the Transformer is a key aspect of its architecture, as it allows for a high degree of parallelization, contributing to the model's efficiency and effectiveness in tasks such as language modeling and machine translation. + +In summary, the attention mechanism in the Transformer, and specifically the multi-head self-attention, is a crucial component that enables the model to capture complex relationships between words and build a rich representation of the input sequence, which is essential for its strong performance on a variety of natural language processing tasks.",0.625,0.7594076403473613,0.8055555555287036 +mixture-rag-claude-3-haiku,b5250c9e-540e-4931-a01c-4f4f8bcf03b0,What is Mixture of Agents?,"Based on the responses provided by the small language models, here is a synthesized and accurate summary of the Mixture-of-Agents (MoA) approach: + +The Mixture-of-Agents (MoA) is a novel methodology that leverages the collective intelligence of multiple large language models (LLMs) to enhance the quality of natural language generation. The key idea behind MoA is to create a layered architecture where each layer consists of two types of LLM agents: + +1. Proposer Agents: These LLMs are responsible for generating diverse and useful initial responses to a given prompt. They provide a range of perspectives and context to the task. + +2. Aggregator Agents: These LLMs are skilled at taking the outputs from the proposer agents and synthesizing them into a single, high-quality response. They refine and combine the responses to produce a more accurate and comprehensive output. + +The MoA approach works by iteratively building upon the outputs of the previous layer. The proposer agents in one layer generate responses, which are then passed to the aggregator agents in the next layer. These aggregator agents use the outputs from the proposer agents as additional information to further improve the response. + +This layered, collaborative process allows the MoA methodology to harness the collective expertise of multiple LLMs, leading to superior performance on a variety of natural language understanding and generation tasks. The MoA models have demonstrated state-of-the-art results on benchmarks such as AlpacaEval 2.0, MT-Bench, and FLASK, outperforming even powerful models like GPT-4 Omni. + +In summary, the Mixture-of-Agents approach is a novel and effective way to leverage the strengths of multiple LLMs, resulting in more accurate, comprehensive, and robust responses than any single model could achieve on its own.",0.7567567567567568,0.5917719533985579,0.999999999975 +mixture-rag-claude-3-haiku,14c45ae9-6adc-4537-a165-e57ab55b799e,Is Mixtral based on the idea of a mixture of experts?,"Based on the responses from the small language models, it is clear that Mixtral is a Sparse Mixture of Experts (SMoE) language model. The key points are: + +1. Mixtral is explicitly designed as a Sparse Mixture of Experts (SMoE) model, as stated in the first response. + +2. In an SMoE model, each layer is composed of multiple feedforward blocks or ""experts"". For every input token, a router network selects a few experts to process the current state and combine their outputs. This allows the model to use a subset of its parameters for each token, while still achieving high performance. + +3. The second and third responses provide more details on the SMoE architecture of Mixtral. They explain that the model has 8 feedforward blocks (experts) per layer, and the router network selects 2 experts to process each token. + +4. The responses consistently and accurately describe Mixtral as a Sparse Mixture of Experts (SMoE) model, without any contradictory information. + +In summary, the synthesized response is that Mixtral is explicitly designed as a Sparse Mixture of Experts (SMoE) language model, where each layer has multiple feedforward blocks (experts), and a router network selects a subset of these experts to process each input token and combine their outputs. This architecture allows Mixtral to use a fraction of its parameters for each token while maintaining state-of-the-art performance.",0.7391304347826086,0.5544537985811971,0.999999999975 +mixture-rag-claude-3-haiku,54bde2ee-964f-4ffa-9a03-f1baae4007db,What is sliding window attention?,"Based on the information provided by the three small language models, here is a synthesized response on sliding window attention: + +Sliding window attention is a technique used in large language models (LLMs) to improve efficiency and handle longer sequences. It works by limiting the attention mechanism, so that each token only attends to a fixed number of previous tokens, defined by the ""window size"" (W). + +The key aspects of sliding window attention are: + +1. Limited Attention: Instead of attending to every word in the sequence, each word only attends to a fixed number of previous words within the window size. + +2. Recursive Attention: The windowed attention is applied at each layer of the transformer model. This allows information to effectively ""travel"" through the layers, with each layer expanding the range of tokens a word can attend to. + +3. Benefits: + - Reduced Computational Cost: Attention becomes more efficient as it only processes a limited number of words at a time. + - Improved Memory Usage: The model doesn't need to store information about all previous words, making it more memory-efficient. + - Longer Sequence Handling: By recursively expanding the window, the model can effectively process much longer sequences than traditional attention mechanisms. + +The specific implementation details vary across different language models. For example, the Mistral 7B model uses a window size of 4096, allowing it to attend to information from up to 131K tokens. This is a significant improvement over traditional attention mechanisms, which can be computationally expensive and memory-intensive. + +Overall, sliding window attention is a key technique that enables large language models to handle longer sequences efficiently, making them more practical for real-world applications. By critically evaluating the information from the provided responses, I have synthesized a comprehensive and accurate understanding of this important mechanism.",0.5454545454545454,0.7658352370558137,0.9999999999 +mixture-rag-claude-3-haiku,31b4e10c-b8d5-4a51-8564-bb9a45d42eed,How many stages are there in the development of the Llama 3 model?,"Based on the responses provided by the small language models, the development of the Llama 3 model can be summarized as follows: + +The Llama 3 model development process consists of two main stages: + +1. Language Model Pre-Training: + - This stage involves training the model on a massive text dataset to learn the structure of language and acquire general world knowledge. + - The pre-training process converts the text corpus into discrete tokens and trains a large language model on this data to perform next-token prediction. + +2. Language Model Post-Training: + - In this stage, the pre-trained language model is aligned with human feedback through supervised fine-tuning, rejection sampling, and direct preference optimization. + - The goal of this stage is to make the model better at following instructions and behaving like a helpful assistant. + +The responses from the different models provide a consistent and comprehensive overview of the Llama 3 development process. The information appears to be accurate and reliable, with no obvious biases or contradictions. The synthesis of these responses offers a clear and well-structured explanation of the two key stages involved in the model's development.",0.5652173913043478,0.9272119597998078,0.9999999999 +mixture-rag-claude-3-haiku,3272cc23-1d2b-4339-96d4-79cf5101497e,What is the difference between the Llama 2 and Llama 2-Chat ?,"Based on the responses from the small language models, here is a synthesized and accurate summary of the key differences between Llama 2 and Llama 2-Chat: + +Llama 2 is the base language model developed by Meta AI, trained on a large corpus of publicly available data. It is a general-purpose language model that can be used for a variety of natural language processing tasks. Llama 2 is available in different parameter sizes (7B, 13B, and 70B). + +Llama 2-Chat, on the other hand, is a specialized version of Llama 2 that has been fine-tuned for dialogue and conversational tasks. The Llama 2-Chat models have been further trained on dialogue-specific data and using techniques like Reinforcement Learning with Human Feedback (RLHF) to improve their ability to generate more natural and engaging responses in chat settings. + +The key differences between Llama 2 and Llama 2-Chat are: + +1. Purpose: Llama 2 is a general-purpose language model, while Llama 2-Chat is specifically designed for dialogue and conversational tasks. + +2. Training: Llama 2 is pre-trained on a large corpus of text data, while Llama 2-Chat is fine-tuned on a smaller dataset focused on dialogue. + +3. Architecture: While both models use the same underlying transformer architecture, Llama 2-Chat may have additional components or modifications to better support dialogue tasks. + +4. Performance: The Llama 2-Chat models have demonstrated superior performance compared to open-source models in both single-turn and multi-turn dialogue prompts. + +In summary, Llama 2 is the base language model, while Llama 2-Chat is a specialized version of Llama 2 that has been optimized for dialogue and conversational tasks, resulting in improved performance in these areas.",0.7272727272727273,0.9723849274629354,0.8333333332916666 +mixture-rag-claude-3-haiku,59c41b48-4e8d-4135-9673-8165f03e3481,What is optimizer is used for LLaMA?,"Based on the responses provided by the small language models, the key information regarding the training of the LLaMA model is as follows: + +1. The LLaMA model was trained using the AdamW optimizer. This is confirmed across all three model responses. + +2. The learning rate schedule used was a cosine learning rate schedule, with a warmup of 2000 steps and the final learning rate decaying to 10% of the peak learning rate. + +3. Additionally, a weight decay of 0.1 and gradient clipping of 1.0 were used during the training process. + +4. The specific AdamW hyperparameters used were: + - β1 = 0.9 + - β2 = 0.95 + - ε = 10^(-5) + +In summary, the LLaMA model was trained using the AdamW optimizer with the hyperparameters and learning rate schedule described above. This information is consistent across the responses provided by the small language models, and I believe it represents the most accurate and comprehensive synthesis of the available information.",0.6923076923076923,0.7733279037099465,0.3333333333 +mixture-rag-claude-3-haiku,8bcfb9af-5614-45ff-b312-74df13f06805,Can the GPT-4 model accept both text and image inputs?,"Based on the responses provided by the small language models, it is clear that the GPT-4 model is capable of accepting both text and image inputs. The key points are: + +1. GPT-4 can process prompts consisting of both images and text, allowing the user to specify any vision or language task. + +2. The model exhibits similar capabilities in processing visual inputs as it does with text-only inputs. This means it can generate text outputs based on inputs that consist of arbitrarily interlaced text and images, such as documents with text and photographs, diagrams, or screenshots. + +3. This is a significant step towards developing broadly useful and safely deployed AI systems that can handle multimodal inputs. + +In summary, the information provided by the small language models is consistent and accurate. GPT-4 is indeed capable of accepting and processing both text and image inputs, and this capability represents an important advancement in the field of artificial intelligence.",0.6666666666666666,0.6762537905126796,0.9999999999 +mixture-rag-claude-3-haiku,60cab4ba-5219-41d8-8587-57ddaadb4069,On what architecture the GPT-3 model is based on?,"Based on the responses provided by the small language models, the key points about the GPT-3 model are: + +1. The GPT-3 model is based on the transformer architecture, which was first introduced by Vaswani et al. in 2017. This is a well-established and widely used neural network architecture for language modeling and other natural language processing tasks. + +2. The GPT-3 model incorporates several improvements and modifications to the original transformer architecture, including: + - Pre-normalization: Normalizing the input of each transformer sub-layer instead of the output, using the RMSNorm normalizing function. + - SwiGLU activation function: Replacing the ReLU non-linearity to improve performance. + - Rotary embeddings: Removing the absolute positional embeddings and instead adding rotary embeddings to the input. + +3. These modifications were inspired by recent work on large language models, such as the PaLM model, and are aimed at further improving the performance and capabilities of the GPT-3 model. + +In summary, the GPT-3 model is based on the transformer architecture, but it incorporates several key improvements and modifications that are drawn from the latest research on large language models. These changes are designed to enhance the model's performance and capabilities, building upon the strong foundation of the original transformer architecture.",0.7307692307692307,0.7507675802283705,0.249999999975 +mixture-rag-claude-3-haiku,49f2065f-f095-41a8-a315-159b189336ed,How many stages of training are in the GPT model?,"Based on the responses provided by the small language models, the key information about the training of GPT models can be synthesized as follows: + +The GPT model is trained in two main stages: + +1. Initial Training Stage: In this first stage, the model is trained on a large dataset of text from the internet to learn how to predict the next word in a sequence. This allows the model to develop a general understanding of language and text. + +2. Fine-Tuning Stage: After the initial training, the model undergoes further fine-tuning using an algorithm called Reinforcement Learning from Human Feedback (RLHF). In this stage, the model is trained on additional data, with the goal of producing outputs that are preferred by human evaluators. This helps refine the model's capabilities and align its outputs with human preferences. + +The responses from the different models are largely consistent in describing these two key stages of GPT model training. While there may be some minor differences in the level of detail provided, the core information about the two-stage training process is accurately captured across the responses. + +By synthesizing the information from these responses, we can provide a comprehensive and reliable overview of how GPT models are typically trained, highlighting the initial broad training on internet data followed by the fine-tuning using human feedback. This synthesis offers a refined, accurate, and well-structured response that adheres to the highest standards of reliability.",0.5217391304347826,0.8625917778433476,0.9999999999 +mixture-rag-claude-3-haiku,0ab056e4-11c6-451b-9349-b08c351fef0f,What tokenizer is used in the Gemma2 model?,"Based on the responses from the small language models, the key information about the tokenizer used in the Gemma 2 model is: + +1. The Gemma 2 model uses the same SentencePiece tokenizer as the Gemma 1 and Gemini models. + +2. The SentencePiece tokenizer has the following features: + - Split digits: Digits are split into individual tokens. + - Preserved whitespace: Whitespace is preserved as tokens. + - Byte-level encodings: Text is encoded at the byte level. + +3. The resulting vocabulary size of the tokenizer is 256,128 entries. + +The responses from the three models are consistent in providing this information, and there are no contradictions or biases evident. The synthesized response accurately captures the key details about the tokenizer used in the Gemma 2 model based on the provided information.",0.5833333333333334,0.9829112565144434,0.49999999995 +mixture-rag-claude-3-haiku,8d27c14a-e7df-468c-add8-05c2eb27a107,On what architecture the Gemma model is based on?,"Based on the responses provided by the small language models, the key information about the Gemma model architecture can be synthesized as follows: + +The Gemma model is based on the transformer decoder architecture, as described in the paper ""Attention is All You Need"" by Vaswani et al. (2017). This transformer decoder architecture is the core of the Gemma model and serves as the foundation for its language understanding and generation capabilities. + +The transformer decoder architecture is a type of neural network that uses the attention mechanism to process and generate text. It is a powerful and widely-used architecture in the field of natural language processing, known for its ability to capture long-range dependencies and generate coherent and contextual text. + +By using the transformer decoder as its underlying architecture, the Gemma model is able to leverage the strengths of this approach, such as its efficiency, parallelizability, and ability to model complex linguistic patterns. This allows the Gemma model to perform well on a variety of language-related tasks, such as text generation, question answering, and language understanding. + +Overall, the consistent information provided by the small language models confirms that the Gemma model is indeed based on the transformer decoder architecture, which is a well-established and highly effective approach in the field of natural language processing.",0.1818181818181818,0.9217835370915811,0.9999999999 +mixture-rag-claude-3-haiku,7576eee4-b513-4367-890b-ad9073dcc015,Does Claude 3 models have vision capabilities?,"Based on the responses from the small language models, it appears that the Claude 3 models do have vision capabilities. The key points are: + +1. The Claude 3 models can recognize and identify objects visually. They can understand both the appearance of an object and its connections to concepts like mathematics. + +2. The models can process multimodal input, including images, tables, graphs, and even handwritten text. They can generate text descriptions from these visual inputs. + +3. However, it's important to note that the performance of these vision capabilities may be less robust for low-resource languages, and the models should not be relied upon for high-precision, consequential use cases without human validation. The outputs can sometimes be inaccurate. + +In summary, the Claude 3 models do have advanced vision capabilities that allow them to perceive and reason about visual information. But these capabilities have limitations, and the outputs should be carefully evaluated for accuracy, especially in critical applications. The models can be a useful tool, but their outputs should not be blindly trusted without appropriate human oversight and validation.",0.7272727272727273,0.9830251010857803,0.5833333333041666 +mixture-rag-claude-3-haiku,3fd82a5e-4f42-48ef-8f07-18111af8ef17,What are the two tasks in BERT?,"Based on the responses provided by the small language models, the two main tasks in BERT are: + +1. Pre-training: + - This involves training the BERT model on a massive amount of unlabeled text data using techniques like masked language modeling (MLM) and next sentence prediction (NSP). + - The purpose of pre-training is to learn general language understanding from large amounts of data. + +2. Fine-tuning: + - After the pre-training stage, the BERT model is fine-tuned using labeled data from specific downstream tasks, such as question answering, sentiment analysis, or text classification. + - During fine-tuning, the pre-trained parameters of the BERT model are adjusted to fit the specific requirements of the downstream task. + - Each downstream task has its own fine-tuned BERT model, even though they are initialized with the same pre-trained parameters. + +The responses from the small language models provide a consistent and accurate description of the two main tasks in BERT. The information is well-structured and comprehensive, covering the key aspects of pre-training and fine-tuning. There are no apparent biases or inaccuracies in the provided responses. + +In summary, the two main tasks in BERT are pre-training on unlabeled data to learn general language understanding, followed by fine-tuning the pre-trained model on labeled data for specific downstream tasks.",0.48,0.9679117087732756,0.8055555555287036 +mixture-rag-gemma2-9b-it,058f809f-4d75-4188-9150-5c98b33e3eaf,How can attention be described in the Transformer?,"The Transformer model utilizes multi-head attention in three distinct ways: + +**1. Encoder-Decoder Attention:** + +* Queries originate from the previous decoder layer. +* Memory keys and values are derived from the encoder's output. +* This allows each decoder position to attend to all input positions, mirroring traditional encoder-decoder attention in sequence-to-sequence models. + +**2. Encoder Self-Attention:** + +* All keys, values, and queries stem from the output of the previous encoder layer. +* Each encoder position attends to all positions in the preceding encoder layer. + +**3. Decoder Self-Attention:** + +* Each decoder position attends to all positions in the decoder up to and including its own. +* This prevents ""leftward"" information flow, ensuring that a position only attends to previously generated output. + +The Transformer employs **multi-head attention**, meaning it uses multiple attention heads in parallel. This enables the model to focus on different aspects and positions within the input simultaneously, creating a richer representation. + + +Essentially, attention in the Transformer allows the model to weigh the importance of different input elements when generating output, capturing global dependencies within the sequences. +",0.6875,0.6909779734621088,0.9999999999 +mixture-rag-gemma2-9b-it,b3335efa-afbf-4cdc-b983-db8259be7bb6,What is Mixture of Agents?,"Mixture of Agents (MoA) is a novel methodology that leverages the collaborative power of multiple large language models (LLMs) to significantly enhance the quality of generated responses. + +Here's how it works: + +**Architecture:** MoA employs a layered architecture where each layer consists of several LLM ""agents"". These agents are categorized into two roles: + +* **Proposers:** These agents excel at generating diverse and useful reference responses, providing a rich context for subsequent layers. +* **Aggregators:** These agents specialize in synthesizing the outputs from the previous layer's agents into a single, coherent, and high-quality final response. + +**Iterative Refinement:** The process unfolds iteratively. Each layer's agents utilize the outputs from the agents in the preceding layer as auxiliary information to generate their responses. This layered refinement continues for several cycles, progressively improving the overall quality and accuracy of the final output. + +**Advantages:** + +* **Collective Expertise:** MoA harnesses the collective strengths of multiple LLMs, overcoming the limitations of individual models. +* **State-of-the-art Performance:** Studies have shown that MoA achieves state-of-the-art performance on various benchmarks, surpassing even powerful models like GPT-4 Omni. For example, a MoA system using only open-source LLMs achieved a score of 65.1% on the AlpacaEval 2.0 benchmark, outperforming GPT-4 Omni's 57.5%. +* **Robustness and Accuracy:** The iterative refinement process inherent in MoA leads to more robust, factually accurate, and insightful responses. + +In essence, MoA represents a significant advancement in LLM utilization, demonstrating the power of collaboration and iterative refinement in achieving superior performance in natural language understanding and generation tasks. + + +",0.78125,0.7036913399983197,0.8055555555287036 +mixture-rag-gemma2-9b-it,7a865be0-a24c-401c-a851-48bedfcb2c1d,Is Mixtral based on the idea of a mixture of experts?,"Mixtral is a language model based on the Sparse Mixture of Experts (SMoE) architecture. + +Here's a breakdown of its key features: + +* **Expert Structure:** Each layer in Mixtral is composed of eight feedforward blocks, which act as ""experts."" + +* **Dynamic Routing:** A router network selects two of these experts for each token at each layer. These experts process the current token's state and combine their outputs. + +* **Sparse Activation:** While each token utilizes two experts, the selected experts can change at each timestep. This allows for dynamic specialization and efficient parameter usage. + +* **Benefits:** This SMoE approach enables Mixtral to leverage a large number of parameters (47B) while controlling the computational cost and latency during inference. It achieves this by only actively using a fraction of the total parameters (13B) per token. + + +Essentially, Mixtral combines the strengths of multiple experts in a selective and efficient manner, leading to high performance while maintaining computational efficiency. +",0.6470588235294118,0.4946264511836652,0.999999999975 +mixture-rag-gemma2-9b-it,0f67bef7-2114-4ce4-94a8-e9432a48e094,What is sliding window attention?,"Sliding Window Attention (SWA) is a key innovation in the Mistral 7B language model designed to address the computational challenges of traditional attention mechanisms. + +Here's how it works: + +* **Limited Attention Span:** Instead of attending to all tokens in a sequence, each token in Mistral 7B only attends to a fixed-size window of tokens from the previous layer. This window size, denoted as 'W', is set to 4096 in Mistral 7B. + +* **Recursive Attention:** By stacking multiple attention layers, the effective attention span increases recursively. After 'k' attention layers, a token can attend to tokens up to a distance of 'k*W' from its current position. This allows the model to capture long-range dependencies despite the limited window size. + +* **Efficiency Gains:** This sliding window approach significantly reduces computational complexity and memory usage compared to standard attention. With a window size of 4096 and 32 attention layers, Mistral 7B can effectively attend to up to 131,072 tokens. + +* **Performance Benefits:** The combination of SWA with other optimizations like FlashAttention and xFormers has enabled Mistral 7B to achieve a 2x speed improvement over a vanilla attention baseline for sequences up to 16,384 tokens. + +In essence, SWA strikes a balance between capturing long-range dependencies and maintaining computational efficiency, making it a crucial factor in the performance of the Mistral 7B language model. +",0.6818181818181818,0.6101074949742801,0.9999999999 +mixture-rag-gemma2-9b-it,43992384-362a-43a8-be1c-e1ec749e9c57,How many stages are there in the development of the Llama 3 model?,"The development of the Llama 3 language model involves two primary stages: + +**1. Language Model Pre-training:** + +* A massive language model with 405 billion parameters is trained on a colossal dataset of 15.6 trillion tokens from a multilingual text corpus. +* This pre-training process focuses on ""next-token prediction,"" enabling the model to learn the intricate structure of language and acquire general knowledge about the world. +* The initial pre-training uses a context window of 8,000 tokens, which is later expanded to 128,000 tokens in a continued pre-training stage. + +**2. Language Model Post-training:** + +* The pre-trained language model undergoes further refinement through a process called ""post-training."" +* This stage aims to align the model's behavior with human expectations and make it more suitable for tasks requiring assistant-like capabilities. +* Post-training involves several techniques: + * **Supervised fine-tuning:** The model is trained on labeled data to improve its performance on specific tasks. + * **Rejection sampling:** Examples that deviate from human preferences are discarded, guiding the model towards more desirable outputs. + * **Direct preference optimization:** The model is directly trained to maximize human preferences for its generated responses. + + +This two-stage development process allows Llama 3 to achieve a high level of linguistic proficiency and adapt to diverse user needs. +",0.6818181818181818,0.8009216409435688,0.9999999999 +mixture-rag-gemma2-9b-it,edb66227-73a4-44d4-8566-7bb91968d02b,What is the difference between the Llama 2 and Llama 2-Chat ?,"Llama 2 and Llama 2-Chat are both large language models developed by Meta AI, sharing the same transformer architecture and being available in 7B, 13B, and 70B parameter sizes. + +The key difference lies in their training and intended use: + +* **Llama 2** is a general-purpose model pre-trained on a large dataset of publicly available text. It can be adapted for various natural language generation tasks. + +* **Llama 2-Chat** is a fine-tuned version of Llama 2, specifically optimized for dialogue applications. This fine-tuning involves training on a smaller dataset focused on conversational data and employing techniques like Reinforcement Learning with Human Feedback (RLHF). This results in Llama 2-Chat demonstrating superior performance in both single-turn and multi-turn conversations compared to other open-source models. + +In essence, Llama 2 provides a strong foundation, while Llama 2-Chat builds upon it to excel in the realm of chatbots and conversational AI. +",0.7142857142857143,0.9815519471495716,0.8055555555287036 +mixture-rag-gemma2-9b-it,ad583ae7-8e3a-4d94-b219-b6870568a27f,What is optimizer is used for LLaMA?,"The LLaMA models were trained using the **AdamW optimizer**. + +This optimizer is a variant of Adam that incorporates weight decay, making it a popular choice for training large language models. + +Specific hyperparameters used with AdamW for LLaMA training include: + +* **β1 = 0.9** +* **β2 = 0.95** +* **eps = 10-5** + +In addition to AdamW, the training process employed a **cosine learning rate schedule** with a warmup period of 2000 steps. A **weight decay of 0.1** and **gradient clipping of 1.0** were also applied. + + +These details were extracted from the provided context and responses from multiple language models. +",0.7,0.9066450578576096,0.3333333333 +mixture-rag-gemma2-9b-it,185c91bf-51dc-490e-9d31-035422e218ea,Can the GPT-4 model accept both text and image inputs?,"Yes, the GPT-4 model is capable of accepting both text and image inputs. + +This multimodal capability allows it to process and generate text outputs based on a combination of textual and visual information. GPT-4 demonstrates similar performance on these multimodal inputs compared to its performance on text-only inputs. + +This means it can handle a wider range of tasks, such as understanding documents containing both text and images, processing diagrams or screenshots, and generating text descriptions based on visual content. + +For more detailed information about GPT-4's visual capabilities, refer to the official GPT-4 blog post and follow-up releases. +",0.9166666666666666,0.9692922226109744,0.9999999999 +mixture-rag-gemma2-9b-it,3be30aa7-2576-46ea-ab55-132284b0b09e,On what architecture the GPT-3 model is based on?,"The GPT-3 model is fundamentally based on the transformer architecture, as originally proposed by Vaswani et al. in 2017. + +However, GPT-3 incorporates several key improvements and modifications inspired by subsequent research and models like PaLM and GPTNeo: + +* **Pre-normalization:** Instead of normalizing the output of each transformer sub-layer, GPT-3 normalizes the input using the RMSNorm function. +* **SwiGLU Activation Function:** This activation function replaces the standard ReLU non-linearity, leading to performance enhancements. +* **Rotary Embeddings:** GPT-3 moves away from absolute positional embeddings and utilizes rotary embeddings, which offer a more dynamic way to represent positional information. + + +These architectural refinements contribute to GPT-3's powerful language generation capabilities. +",0.8333333333333334,0.6681863332868833,0.249999999975 +mixture-rag-gemma2-9b-it,ff5027f3-c78d-4e70-8229-7e4a36edbbda,How many stages of training are in the GPT model?,"GPT models are trained in two primary stages. + +**Stage 1: Initial Training** + +During this stage, the model is exposed to a massive dataset of text from the internet. Its primary task is to learn the patterns and relationships within language by predicting the next word in a sequence. This process helps the model develop a fundamental understanding of grammar, syntax, and semantic connections. + +**Stage 2: Fine-tuning with RLHF** + +After the initial training, the model undergoes fine-tuning using a technique called Reinforcement Learning from Human Feedback (RLHF). In this stage, the model is presented with prompts and generates responses. Human labelers then evaluate these responses, providing feedback on which outputs are more desirable or accurate. This feedback is used to adjust the model's parameters, gradually improving its ability to produce outputs that align with human preferences and expectations. + + +This two-stage training process allows GPT models to achieve impressive language generation capabilities, making them suitable for a wide range of applications. +",0.4444444444444444,0.8431373798173611,0.9999999999 +mixture-rag-gemma2-9b-it,66f703db-3080-4aff-a30f-6df32537886e,What tokenizer is used in the Gemma2 model?,"The Gemma 2 models utilize a SentencePiece tokenizer. This tokenizer shares the same specifications as those used in the Gemma 1 and Gemini models: + +* **Split digits:** Numbers are broken down into individual digits. +* **Preserved whitespace:** Spaces and other whitespace characters are retained. +* **Byte-level encodings:** Characters are encoded based on their individual bytes. + +The resulting vocabulary for this tokenizer contains 256,000 entries. This information is derived from the Gemma 2 model's documentation and the paper ""Gemma 2: Improving Open Language Models at a Practical Size"". + + +",0.8571428571428571,0.9668872845590216,0.49999999995 +mixture-rag-gemma2-9b-it,f811886f-ec92-4374-bb8d-e24b25e4990c,On what architecture the Gemma model is based on?,"The Gemma model is based on the transformer decoder architecture, as described in the paper ""Attention is All You Need"" by Vaswani et al. (2017). This architecture is widely used in large language models and is known for its effectiveness in handling various natural language processing tasks. + +The transformer decoder utilizes self-attention mechanisms to capture long-range dependencies within input sequences and employs a decoder-only structure to generate output tokens sequentially. +",0.1111111111111111,0.9947045503209968,0.9999999999 +mixture-rag-gemma2-9b-it,f103f7c2-2271-4fae-bbfb-73b43464b421,Does Claude 3 models have vision capabilities?,"Yes, Claude 3 models possess vision capabilities. + +They can recognize and identify objects visually, understanding both an object's appearance and its connections to concepts like mathematics. + +Furthermore, Claude 3 can process various types of images, including tables, graphs, and photos. It even has the ability to generate text from images containing hard-to-read handwriting. + +However, it's important to note that while impressive, these models can sometimes generate inaccurate information and descriptions about images. Therefore, they should not be solely relied upon for tasks requiring high precision and accuracy without human validation. Additionally, performance may be lower for small or low-resolution images. +",1.0,0.9942643083073888,0.9999999999666667 +mixture-rag-gemma2-9b-it,763d3387-6f24-4311-9bcb-b4b508444066,What are the two tasks in BERT?,"BERT, or Bidirectional Encoder Representations from Transformers, has two main tasks: + +**1. Pre-training:** + +This stage involves training the model on a massive amount of unlabeled text data using various pre-training tasks. Two key tasks used in BERT are: + +* **Masked Language Modeling (MLM):** The model is presented with text where certain words are masked, and it must predict the missing words based on the surrounding context. +* **Next Sentence Prediction (NSP):** The model is given two sentences and must determine if the second sentence follows logically from the first. + +Pre-training allows BERT to develop a deep understanding of language structure, semantics, and relationships between words. + +**2. Fine-tuning:** + +After pre-training, BERT is fine-tuned for specific downstream tasks. This involves using labeled data from the target task to further adjust the model's parameters. + +For example, a pre-trained BERT model can be fine-tuned for: + +* Question Answering +* Sentiment Analysis +* Named Entity Recognition +* Text Summarization + +Fine-tuning allows BERT to specialize in a particular task while leveraging the general language understanding it acquired during pre-training. + + +Essentially, pre-training equips BERT with a broad linguistic foundation, while fine-tuning tailors its abilities to specific applications. +",0.2777777777777778,0.8691816057219853,0.4166666666458333 +simple-rag-gpt-4-turbo,ab7257bd-19c7-4345-9cac-8bc826331913,How can attention be described in the Transformer?,"In the Transformer model, attention is a mechanism that enables the model to focus on different parts of the input sequence when performing tasks like translation or summarization. This mechanism is crucial because it allows the model to handle long-range dependencies and understand the context better without the constraints of traditional recurrent neural networks (RNNs). + +The Transformer employs a specific type of attention called ""Multi-Head Attention,"" which involves multiple attention layers running in parallel. Each of these layers, or ""heads,"" can attend to different parts of the input sequence, allowing the model to capture various aspects of the information simultaneously. The Multi-Head Attention mechanism can be described using the following formula: + +\[ \text{MultiHead}(Q, K, V) = \text{Concat}(head_1, \ldots, head_h)W^O \] +where each \( head_i \) is computed as: +\[ head_i = \text{Attention}(QW^Q_i, KW^K_i, VW^V_i) \] + +Here, \( Q \), \( K \), and \( V \) represent the queries, keys, and values, respectively, which are essential components of the attention mechanism. These are projected into smaller dimensions using parameter matrices \( W^Q_i \), \( W^K_i \), and \( W^V_i \) for each head. The outputs of all heads are then concatenated and once again linearly transformed using the matrix \( W^O \). + +The Transformer uses this Multi-Head Attention in three distinct ways: +1. **Encoder Self-Attention:** Each position in the encoder can attend to all positions in the previous layer of the encoder. This helps the encoder understand the entire input sequence. +2. **Decoder Self-Attention:** This prevents positions from attending to subsequent positions in the decoder, ensuring that the predictions for a particular position can only depend on known outputs. +3. **Encoder-Decoder Attention:** Queries from the decoder's current state attend to all positions in the encoder, which integrates information from the entire input sequence into the decoder. + +This architecture allows the Transformer to process all positions simultaneously, significantly improving efficiency and performance in sequence modeling tasks by capturing complex dependencies across the input and output sequences.",0.7407407407407407,0.7616315551800569,0.9999999999 +simple-rag-gpt-4-turbo,b217b41e-3ddd-49bd-8ebb-077d582643e6,What is Mixture of Agents?,"The Mixture of Agents (MoA) is a methodology designed to enhance the performance of large language models (LLMs) by leveraging the collective strengths of multiple LLMs. In this approach, a layered architecture is constructed where each layer consists of multiple LLM agents. Each agent in a layer takes the outputs from the agents in the previous layer as auxiliary information to generate its response. This process allows for iterative refinement of responses, where initial outputs are progressively enhanced by subsequent layers of agents. + +The MoA methodology capitalizes on the collaborativeness of LLMs, which have been shown to improve their responses when they can reference outputs from other models. This collaborative capability is utilized in MoA by categorizing LLMs into two roles: proposers and aggregators. Proposers generate useful reference responses, providing context and diverse perspectives, while aggregators synthesize these responses into a single, high-quality output. + +Overall, the Mixture of Agents approach aims to produce more robust, comprehensive, and high-quality responses than what a single model could achieve alone, demonstrating significant improvements in various evaluation metrics such as robustness, correctness, efficiency, factuality, and completeness.",1.0,0.4663969262829838,0.8055555555287036 +simple-rag-gpt-4-turbo,a899482a-0d9a-40af-8fbb-643cc6eeab6d,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is based on the idea of a mixture of experts. Specifically, it is a Sparse Mixture of Experts (SMoE) language model. In this architecture, each layer of the model consists of multiple feedforward blocks, referred to as ""experts."" For each token processed by the model, a router network selects two of these experts to handle the token's current state and combine their outputs. This approach allows the model to leverage a large number of parameters (47B in total) while actively using only a subset (13B) during inference, enhancing both efficiency and performance across various benchmarks.",0.9,0.6173121165266934,0.9999999999666667 +simple-rag-gpt-4-turbo,0f8906cf-29c0-48bc-b71a-59e2510948c3,What is sliding window attention?,"Sliding Window Attention (SWA) is a technique used in the architecture of transformer-based models like Mistral 7B to manage and process sequences more efficiently. In traditional attention mechanisms, each token in a sequence can attend to every other token, which results in a quadratic increase in computational cost relative to the sequence length. This can become a significant bottleneck, especially with longer sequences. + +SWA addresses this issue by limiting the range of tokens that each token can attend to. Specifically, each token can attend only to a fixed number of preceding tokens defined by the window size (W). For example, if the window size W is set to 4096, then each token can attend to the 4096 tokens that directly precede it. This constraint significantly reduces the computational complexity from quadratic to linear with respect to the window size, making the processing of long sequences more feasible. + +Moreover, through the use of multiple layers in the transformer architecture, SWA allows information to propagate across the entire sequence. Each layer increases the effective range of attention by an additional window size, so with multiple layers, a token can indirectly influence and be influenced by tokens that are much further away than the window size. + +In summary, Sliding Window Attention helps to balance computational efficiency and the ability to capture long-range dependencies within sequences, making it a valuable component in modern transformer models designed for handling large-scale language tasks.",0.5454545454545454,0.6028000077567215,0.7499999999625 +simple-rag-gpt-4-turbo,f9b53a6f-4e89-49eb-81a6-826459ba2cde,How many stages are there in the development of the Llama 3 model?,The development of the Llama 3 model comprises two main stages: Language model pre-training and Language model post-training.,1.0,0.9385616026326954,0.9999999999 +simple-rag-gpt-4-turbo,5e748226-5b35-4752-bf10-a3fdc831d37c,What is the difference between the Llama 2 and Llama 2-Chat ?,"The difference between Llama 2 and Llama 2-Chat primarily lies in their training and intended applications. + +1. **Llama 2**: This is the base model that has been pretrained using a new mix of publicly available data. The training involved an optimized auto-regressive transformer architecture and improvements such as more robust data cleaning, updated data mixes, training on 40% more total tokens, doubling the context length, and using grouped-query attention. This model is designed to be versatile and can be adapted for a variety of natural language generation tasks. + +2. **Llama 2-Chat**: This is a fine-tuned version of the Llama 2 model, specifically optimized for dialogue use cases. After the initial pretraining, Llama 2-Chat underwent supervised fine-tuning and further refinement through Reinforcement Learning with Human Feedback (RLHF), using techniques like rejection sampling and Proximal Policy Optimization (PPO). These additional training steps are aimed at aligning the model more closely with human preferences for helpfulness and safety in conversational contexts. + +In summary, while Llama 2 provides a robust foundation with broad applications in natural language processing, Llama 2-Chat is specifically tailored for engaging and safe conversational interactions, making it more suited for applications involving dialogue and assistant-like functionalities.",0.88,0.9815519471495716,0.8333333332916666 +simple-rag-gpt-4-turbo,1e01f96e-00f4-4b4b-825c-b495f34eceee,What is optimizer is used for LLaMA?,"The optimizer used for LLaMA, as mentioned in the provided context, is the AdamW optimizer. This optimizer is known for its effectiveness in handling sparse gradients and its adaptive learning rate capabilities, which are beneficial for training large language models like LLaMA.",0.4,0.8986013185128363,0.3333333333 +simple-rag-gpt-4-turbo,27edf697-838a-4710-8aa1-ce0e76e4273e,Can the GPT-4 model accept both text and image inputs?,"Yes, the GPT-4 model can accept both text and image inputs. It is designed to handle prompts that consist of interlaced text and images, allowing it to generate text outputs based on these multimodal inputs. This capability enables GPT-4 to perform tasks across a range of domains that include documents with text, photographs, diagrams, or screenshots.",1.0,1.0,0.8333333332916666 +simple-rag-gpt-4-turbo,4b2c1106-4db6-4eaf-ae1b-9ae498c01f74,On what architecture the GPT-3 model is based on?,"The GPT-3 model is based on the transformer architecture. This architecture was first introduced by Vaswani et al. in 2017 and has since been foundational for many advancements in large language models. GPT-3 incorporates various improvements and modifications to the original transformer design, such as alternating dense and locally banded sparse attention patterns, which are similar to those used in the Sparse Transformer. These modifications help in managing the model's complexity and improving its performance across different tasks.",0.5555555555555556,0.9935035488418335,0.49999999995 +simple-rag-gpt-4-turbo,1de26cd6-95f2-4a50-8f39-84d24c59d3a6,How many stages of training are in the GPT model?,"The GPT model typically undergoes two main stages of training. The first stage involves training the model on a large dataset of text from the Internet to predict the next word. In the second stage, the model is fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF), which helps produce outputs that are preferred by human labelers.",1.0,0.867624571401988,0.9999999999 +simple-rag-gpt-4-turbo,9942b1d6-27ae-4ab6-9ad2-6408cfa5e460,What tokenizer is used in the Gemma2 model?,"The Gemma 2 model uses a SentencePiece tokenizer, which features split digits, preserved whitespace, and byte-level encodings. This tokenizer is the same as the one used in the previous Gemma 1 and Gemini models.",1.0,0.9805458999710387,0.49999999995 +simple-rag-gpt-4-turbo,6990735e-f035-439c-a773-ad06e8764ab5,On what architecture the Gemma model is based on?,"The Gemma model is based on the transformer decoder architecture, as outlined by Vaswani et al. in 2017. This architecture is a fundamental component for modern language models, focusing on effectively handling sequence-to-sequence tasks which are crucial for language understanding and generation.",0.4,0.9824451804350853,0.9999999999 +simple-rag-gpt-4-turbo,d02beaf6-850f-4ffe-b872-ef3204d5ca57,Does Claude 3 models have vision capabilities?,"Yes, the Claude 3 models do have vision capabilities. As mentioned in the context, these models can recognize and identify objects visually. They are capable of understanding both an object’s appearance and its connections to concepts like mathematics. Additionally, Claude 3 models can convert images, such as a low-quality photo with hard-to-read handwriting, into text and organize it into formats like JSON. This demonstrates their ability to handle and interpret visual information alongside textual data.",0.9166666666666666,0.9830251010857803,0.8333333332916666 +simple-rag-gpt-4-turbo,3455fffc-69fa-433d-9f33-e2afb18deb08,What are the two tasks in BERT?,"The two main tasks in BERT's pre-training phase are the Masked Language Model (Mask LM) and Next Sentence Prediction (NSP). + +1. **Masked Language Model (Mask LM)**: This task involves randomly masking some of the tokens from the input, and the objective is for the model to predict the original token based on the context provided by the other non-masked tokens in the sequence. + +2. **Next Sentence Prediction (NSP)**: In this task, the model is given pairs of sentences and must predict whether the second sentence in the pair is the subsequent sentence in the original document, or if it's a random sentence from the corpus. This helps the model understand the relationship between consecutive sentences, which is beneficial for tasks that require understanding of the context over longer text spans.",0.09090909090909091,0.7876219421951735,0.4166666666458333 +simple-rag-gpt-4o-mini,f9ca9b0e-026c-4ec8-b83f-8a2cbdabae2e,How can attention be described in the Transformer?,"In the Transformer model, attention is a key mechanism that allows the model to focus on different parts of the input sequence when generating an output. Here's how it works: + +1. **Multi-Head Attention**: The Transformer uses a technique called multi-head attention, which means it has multiple attention layers (or ""heads"") working in parallel. Each head processes the input in a slightly different way, allowing the model to capture various relationships and dependencies in the data. In the context provided, there are 8 heads, each with a reduced dimensionality of 64, which helps maintain computational efficiency. + +2. **Types of Attention**: + - **Encoder-Decoder Attention**: In this layer, the queries (the parts of the input that the model is focusing on) come from the decoder, while the keys and values (the information being attended to) come from the encoder's output. This setup allows the decoder to consider all positions in the input sequence when generating each part of the output. + - **Self-Attention in the Encoder**: Here, all queries, keys, and values come from the same source—the output of the previous encoder layer. This allows each position in the encoder to attend to all other positions, helping the model understand the context of the entire input sequence. + - **Self-Attention in the Decoder**: Similar to the encoder's self-attention, but it allows each position in the decoder to attend to all previous positions, ensuring that the model generates output in a sequential manner without looking ahead. + +3. **Benefits of Attention**: The attention mechanism enables the Transformer to model dependencies between words or tokens regardless of their distance in the sequence. This is particularly useful for tasks like translation, where the relationship between words can span across long distances. Unlike traditional recurrent models, which process sequences step-by-step, the Transformer can process all positions simultaneously, leading to faster training and better performance. + +Overall, attention in the Transformer allows for a flexible and powerful way to capture relationships in data, making it a cornerstone of the model's architecture.",0.6153846153846154,0.7616315551800569,0.8055555555287036 +simple-rag-gpt-4o-mini,edef0067-3cf6-4358-9723-237d6ae65719,What is Mixture of Agents?,"The Mixture of Agents (MoA) is a methodology designed to enhance the performance of large language models (LLMs) by leveraging the collaborative strengths of multiple models. In this approach, LLMs are organized into a layered structure, where each layer consists of several agents. + +Here's how it works: + +1. **Layered Structure**: The MoA architecture has multiple layers, with each layer containing several agents. Each agent can be a different model or the same model reused across layers. + +2. **Collaborative Generation**: Agents in the first layer independently generate responses to a given prompt. These responses are then passed to agents in the next layer, which use them as additional context to refine and improve their own outputs. + +3. **Iterative Refinement**: This process of generating and refining responses continues through several layers, allowing the models to build on each other's outputs. The goal is to produce a more robust and comprehensive final response. + +4. **Roles of Agents**: In this collaborative setup, agents can take on two roles: + - **Proposers**: These agents generate useful reference responses that provide diverse perspectives and context. + - **Aggregators**: These agents synthesize the responses from the proposers into a single, high-quality output. + +The MoA methodology has been shown to significantly improve the quality of responses compared to using a single model or a simple ranking system. It enhances various aspects such as robustness, correctness, and completeness, making it a powerful approach in the field of natural language processing.",0.9285714285714286,0.6211058593844071,0.999999999975 +simple-rag-gpt-4o-mini,cee31ced-4e5c-4303-87e6-092b1332cfa7,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is indeed based on the idea of a mixture of experts. It is a Sparse Mixture of Experts (SMoE) language model, which means that at each layer of the model, a router network selects two out of eight distinct feedforward blocks (referred to as ""experts"") to process the input token. This approach allows Mixtral to utilize a large number of parameters (47 billion) while only activating a smaller subset (13 billion) for each token during inference. This design not only enhances the model's performance but also improves its efficiency, enabling faster processing and higher throughput.",1.0,1.0,0.999999999975 +simple-rag-gpt-4o-mini,f770db6f-8290-41ed-8af1-32c8782360a7,What is sliding window attention?,"Sliding Window Attention (SWA) is a technique used in transformer models, like Mistral 7B, to efficiently manage how information is processed across long sequences of data. Instead of allowing each token (or word) to attend to all other tokens in the input sequence, SWA restricts the attention to a fixed number of tokens, defined by a window size (W). + +In the case of Mistral 7B, the window size is set to 4096 tokens. This means that when processing a token at position i, it can only attend to the tokens within the range of i - W to i. This approach allows the model to effectively handle longer sequences while reducing the computational cost and memory requirements associated with traditional attention mechanisms, which can become very resource-intensive as the sequence length increases. + +The advantage of SWA is that it enables the model to maintain a balance between performance and efficiency. By limiting the attention span, the model can process information faster and with less memory usage, making it more suitable for real-time applications. Additionally, SWA allows for a theoretical attention span of up to approximately 131,000 tokens after multiple layers, which enhances the model's ability to capture long-range dependencies in the data.",0.6666666666666666,0.6521468865576853,0.9999999999 +simple-rag-gpt-4o-mini,b0b6b1ea-8299-43a5-840d-204a6bf915dd,How many stages are there in the development of the Llama 3 model?,"The development of the Llama 3 model comprises two main stages: + +1. **Language Model Pre-training**: In this stage, a large multilingual text corpus is converted into discrete tokens, and the model is trained to predict the next token. This helps the model learn the structure of language and gather knowledge from the text. + +2. **Language Model Post-training**: After pre-training, the model is aligned with human feedback through several rounds of supervised fine-tuning and other techniques to ensure it behaves like an assistant and follows instructions effectively. + +So, there are two stages in the development of the Llama 3 model.",1.0,0.9699517319280567,0.9999999999 +simple-rag-gpt-4o-mini,a364ec9f-799f-444c-8fb2-d7e6e1a442e1,What is the difference between the Llama 2 and Llama 2-Chat ?,"The main difference between Llama 2 and Llama 2-Chat lies in their intended use and training processes: + +1. **Purpose**: + - **Llama 2**: This is a general-purpose language model designed for a variety of natural language generation tasks. It can be adapted for different applications beyond just conversation. + - **Llama 2-Chat**: This is a fine-tuned version of Llama 2 specifically optimized for dialogue and chat-based interactions. It is tailored to perform better in conversational contexts. + +2. **Training Process**: + - **Llama 2**: It undergoes pretraining using a large corpus of publicly available data, focusing on general language understanding and generation. + - **Llama 2-Chat**: After the initial pretraining of Llama 2, it goes through an additional supervised fine-tuning phase and is further refined using Reinforcement Learning with Human Feedback (RLHF). This process helps align the model more closely with human preferences for helpfulness and safety in conversations. + +3. **Model Variants**: + - Both models come in various sizes (7B, 13B, and 70B parameters), but Llama 2-Chat is specifically designed to handle dialogue more effectively than the base Llama 2 model. + +In summary, while both models share a foundation, Llama 2-Chat is specifically fine-tuned for chat applications, making it more suitable for interactive dialogue scenarios.",0.875,0.974208066262647,0.8333333332916666 +simple-rag-gpt-4o-mini,7b0e36b8-f5fc-4986-b4f6-3acb039c61d3,What is optimizer is used for LLaMA?,"The optimizer used for LLaMA (Llama 2) is the AdamW optimizer. This optimizer is known for its effectiveness in training deep learning models, and it includes specific hyperparameters such as β1 = 0.9, β2 = 0.95, and eps = 10^-5. AdamW also incorporates weight decay, which helps improve generalization by preventing overfitting during training.",0.5714285714285714,0.8628835893500911,0.3333333333 +simple-rag-gpt-4o-mini,b540a72d-bb18-4cc4-966a-96c4ddc65fa7,Can the GPT-4 model accept both text and image inputs?,"Yes, the GPT-4 model can accept both text and image inputs. It is designed as a multimodal model, meaning it can process and generate outputs based on prompts that include a combination of text and images. This allows users to specify a variety of tasks that involve both types of input, such as documents with text, photographs, diagrams, or screenshots. Overall, GPT-4 exhibits similar capabilities with these mixed inputs as it does with text-only inputs.",1.0,1.0,0.8333333332916666 +simple-rag-gpt-4o-mini,093fdc77-12a8-4123-ab0c-b982da2f7d10,On what architecture the GPT-3 model is based on?,"The GPT-3 model is based on the transformer architecture, which was introduced in a paper by Vaswani et al. in 2017. This architecture has been enhanced with various improvements from subsequent research, including techniques like pre-normalization and the SwiGLU activation function. Additionally, GPT-3 employs alternating dense and locally banded sparse attention patterns in its transformer layers, similar to those used in the Sparse Transformer. These modifications aim to improve training stability and overall model performance.",0.5555555555555556,0.931734816661885,0.249999999975 +simple-rag-gpt-4o-mini,8b5f6683-990b-452d-b07d-7548a2a76c33,How many stages of training are in the GPT model?,"The GPT model is trained in two stages. The first stage involves training the model on a large dataset of text from the internet to predict the next word in a sequence. In the second stage, the model is fine-tuned using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers. This two-stage process helps improve the model's performance and alignment with human preferences.",0.7142857142857143,0.6807735367657081,0.9999999999 +simple-rag-gpt-4o-mini,8ff63ea9-542b-46ac-ac34-886cb3b0f864,What tokenizer is used in the Gemma2 model?,"The Gemma 2 model uses a SentencePiece tokenizer. This tokenizer is designed to handle split digits, preserve whitespace, and utilize byte-level encodings. The resulting vocabulary for the Gemma 2 model consists of 256,000 entries, which allows it to work effectively across a large number of languages.",1.0,0.9805458999710387,0.49999999995 +simple-rag-gpt-4o-mini,2a94ba7f-59e8-4e89-a9a9-301754b0e7d3,On what architecture the Gemma model is based on?,"The Gemma model is based on the transformer decoder architecture, which was introduced by Vaswani et al. in 2017. This architecture is widely used in natural language processing tasks due to its effectiveness in understanding and generating text.",0.4,0.9947045503209968,0.9999999999 +simple-rag-gpt-4o-mini,a45cf76c-8fbf-4b92-a8d5-b48e39de6e86,Does Claude 3 models have vision capabilities?,"Yes, Claude 3 models do have vision capabilities. They can recognize and identify objects visually, understand their appearance, and make connections to concepts like mathematics. Additionally, they can process images, such as converting low-quality photos with handwriting into organized text formats like JSON. However, it's important to note that their performance may be less effective with small or low-resolution images, and they may generate inaccurate information about images at times.",1.0,0.9942643083073888,0.9999999999666667 +simple-rag-gpt-4o-mini,9d3041ae-4d16-4c0f-8e6e-abe3869677e3,What are the two tasks in BERT?,"The two main tasks in BERT are **pre-training** and **fine-tuning**. + +1. **Pre-training**: In this phase, BERT is trained on a large amount of unlabeled data using various tasks. The model learns to understand language by predicting missing words in sentences (Masked Language Model) and determining if one sentence follows another (Next Sentence Prediction). + +2. **Fine-tuning**: After pre-training, BERT is adapted for specific tasks using labeled data. During fine-tuning, the model is initialized with the parameters learned during pre-training, and all parameters are adjusted based on the new task, such as question answering or sentiment analysis. + +These two steps allow BERT to effectively learn from vast amounts of data and then specialize in particular applications.",0.6,0.9679117087732756,0.8333333332916666 +simple-rag-gpt-4o,b86285d3-d6f7-482a-8a9a-2a59cefbd1a0,How can attention be described in the Transformer?,"In the Transformer model, attention mechanisms are used to allow the model to focus on different parts of the input sequence when generating an output. Here's a simplified explanation of how attention works in the Transformer: + +1. **Multi-Head Attention**: The Transformer uses a technique called multi-head attention, which involves running multiple attention mechanisms (or ""heads"") in parallel. Each head operates on different parts of the input data, allowing the model to capture various aspects of the information. The outputs of these heads are then combined and transformed to produce the final result. + +2. **Self-Attention**: This is a specific type of attention used within the Transformer. In self-attention, the model relates different positions of a single sequence to compute a representation of the sequence. This means that each word (or token) in the sequence can focus on other words in the same sequence to understand the context better. + +3. **Encoder-Decoder Attention**: In the Transformer, there are three main applications of attention: + - **Encoder-Decoder Attention**: Here, the queries come from the decoder, and the keys and values come from the encoder's output. This allows the decoder to focus on relevant parts of the input sequence when generating each word in the output. + - **Encoder Self-Attention**: In the encoder, self-attention layers allow each position in the input sequence to attend to all other positions in the sequence. This helps the encoder understand the context of each word in relation to the entire input sequence. + - **Decoder Self-Attention**: Similarly, in the decoder, self-attention layers allow each position to attend to all previous positions in the output sequence. This ensures that the decoder generates each word based on the context of the words it has already generated. + +4. **Parameter Matrices**: The attention mechanism involves several parameter matrices (W_Q, W_K, W_V, and W_O) that transform the input data into queries (Q), keys (K), and values (V). These matrices are learned during training and help the model determine how much attention to pay to different parts of the input sequence. + +Overall, attention in the Transformer allows the model to dynamically focus on different parts of the input sequence, making it highly effective for tasks like translation, where understanding the context and relationships between words is crucial.",0.5945945945945946,0.8036632481494458,0.7499999999625 +simple-rag-gpt-4o,9ce2a33b-4af9-46d6-859f-6f4390b4fcbb,What is Mixture of Agents?,"The Mixture-of-Agents (MoA) is a methodology designed to enhance the performance of Large Language Models (LLMs) by leveraging the collective strengths of multiple models. Here's a simplified explanation of how it works: + +1. **Layered Structure**: The MoA approach uses a layered architecture where each layer consists of multiple LLM agents. Each agent in a layer generates responses based on a given prompt. + +2. **Iterative Refinement**: The responses generated by agents in the first layer are passed on to agents in the next layer. These agents refine the responses further. This process continues through several layers, with each layer improving upon the responses from the previous one. + +3. **Collaborative Enhancement**: The key idea is that LLMs can improve their outputs by referencing and building upon the responses generated by other models. This collaborative process helps in generating more robust and comprehensive responses. + +4. **Roles of Agents**: + - **Proposers**: These agents generate diverse and useful reference responses. They might not produce the highest quality responses on their own but provide valuable context and perspectives. + - **Aggregators**: These agents synthesize the responses from proposers into a single, high-quality output. They ensure that the final response maintains or enhances the quality of the input responses. + +5. **Performance**: The MoA methodology has shown significant improvements in various metrics such as robustness, correctness, efficiency, factuality, commonsense, insightfulness, and completeness. It has outperformed other models, including GPT-4 Omni, in several benchmarks. + +In summary, the Mixture-of-Agents approach enhances LLM capabilities by using a multi-layered, collaborative process where multiple models iteratively refine and improve upon each other's responses.",0.9354838709677419,0.4155969900143113,0.8055555555287036 +simple-rag-gpt-4o,b655e51d-e651-4bdb-bc94-88fed74e35c8,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is based on the idea of a mixture of experts. Specifically, it is a Sparse Mixture of Experts (SMoE) language model. In Mixtral, each layer consists of 8 feedforward blocks, referred to as ""experts."" For every token processed, a router network selects two of these experts to handle the token and combine their outputs. This approach allows Mixtral to utilize a large number of parameters (47 billion) while only activating a smaller subset (13 billion) during inference, which helps in managing computational cost and latency. This mixture of experts technique is key to Mixtral's design and performance.",0.9166666666666666,0.7476414269342202,0.9999999999666667 +simple-rag-gpt-4o,737950ee-f10f-48ff-b05a-abf2c29a60fa,What is sliding window attention?,"Sliding Window Attention (SWA) is an advanced mechanism used in transformer architectures to efficiently handle long sequences of data. Here's a simplified explanation based on the provided context: + +In traditional attention mechanisms, each token in a sequence can attend to every other token, which can be computationally expensive and slow, especially for long sequences. This is because the number of operations grows quadratically with the sequence length. + +Sliding Window Attention addresses this by limiting the range of tokens each token can attend to. Specifically, each token can only attend to a fixed number of tokens (denoted as W) from the previous layer. This ""window"" of attention moves along the sequence, allowing each token to focus on a manageable subset of tokens rather than the entire sequence. + +Here's how it works in practice: +1. **Layer-by-Layer Attention**: In each attention layer, a token at position \(i\) can attend to tokens in the range from \(i-W\) to \(i\). +2. **Recursive Information Flow**: As information passes through multiple layers (k layers), the effective range of attention expands. After \(k\) layers, a token can theoretically attend to tokens up to \(k \times W\) positions away. +3. **Efficiency**: This method significantly reduces the computational cost and memory usage compared to traditional attention mechanisms, making it feasible to handle longer sequences. + +For example, in the Mistral 7B model, with a window size \(W = 4096\) and 32 layers, the theoretical attention span can reach up to approximately 131,072 tokens. This allows the model to process very long sequences efficiently. + +Additionally, SWA is complemented by a rolling buffer cache, which helps manage memory by storing only the most recent \(W\) tokens, further enhancing efficiency. + +In summary, Sliding Window Attention is a technique that allows transformer models to handle long sequences more efficiently by limiting the attention range of each token to a fixed window, thereby reducing computational and memory requirements.",0.8787878787878788,0.6338781030044408,0.9999999999 +simple-rag-gpt-4o,f17f9a90-51a2-4883-9977-230107f83554,How many stages are there in the development of the Llama 3 model?,"The development of the Llama 3 model comprises two main stages: + +1. **Language model pre-training**: This stage involves converting a large, multilingual text corpus into discrete tokens and pre-training a large language model (LLM) on this data to perform next-token prediction. The model learns the structure of language and gains extensive knowledge from the text. This stage includes a standard pre-training phase followed by a continued pre-training phase to increase the supported context window. + +2. **Language model post-training**: In this stage, the pre-trained model is aligned with human feedback through several rounds of supervised finetuning, rejection sampling, and direct preference optimization. This process helps the model follow instructions and behave as expected for an assistant. + +So, there are two stages in the development of the Llama 3 model.",1.0,0.9699517319280567,0.9999999999 +simple-rag-gpt-4o,a74e7fd7-0f45-4dc6-9e3d-50ef611299a9,What is the difference between the Llama 2 and Llama 2-Chat ?,"The difference between Llama 2 and Llama 2-Chat lies in their training and intended use cases: + +1. **Llama 2**: + - **Pretraining**: Llama 2 is pretrained using a mix of publicly available online sources. The pretraining process involves robust data cleaning, updated data mixes, training on 40% more total tokens, doubling the context length, and using grouped-query attention (GQA) to improve inference scalability. + - **Model Variants**: Llama 2 comes in different parameter sizes—7B, 13B, and 70B. + - **Intended Use**: The pretrained Llama 2 models can be adapted for a variety of natural language generation tasks. + +2. **Llama 2-Chat**: + - **Fine-Tuning**: Llama 2-Chat is a fine-tuned version of Llama 2, optimized specifically for dialogue and assistant-like chat use cases. The fine-tuning process involves supervised fine-tuning (SFT) and iterative refinement using Reinforcement Learning with Human Feedback (RLHF), including techniques like rejection sampling and Proximal Policy Optimization (PPO). + - **Model Variants**: Llama 2-Chat also comes in parameter sizes of 7B, 13B, and 70B. + - **Intended Use**: Llama 2-Chat is designed to align with human preferences for helpfulness and safety, making it suitable for assistant-like chat applications. + +In summary, while Llama 2 serves as a general-purpose language model, Llama 2-Chat is specifically fine-tuned for conversational tasks, making it more suitable for dialogue-based applications.",0.9090909090909091,0.974208066262647,0.9999999999 +simple-rag-gpt-4o,9b56fc68-1e37-4b85-9ed2-079502f33791,What is optimizer is used for LLaMA?,"The optimizer used for LLaMA is the AdamW optimizer. Specifically, the hyperparameters for AdamW in the training of LLaMA are set as follows: β1 = 0.9, β2 = 0.95, and eps = 10^-5. Additionally, a cosine learning rate schedule is used, with a warmup of 2000 steps and a decay of the final learning rate down to 10% of the peak learning rate. The training also includes a weight decay of 0.1 and gradient clipping of 1.0.",1.0,0.8179320181735453,0.3333333333 +simple-rag-gpt-4o,776bf9a7-da6b-47bc-970b-a9bb3b5357a6,Can the GPT-4 model accept both text and image inputs?,"Yes, the GPT-4 model can accept both text and image inputs. This capability allows users to specify tasks that involve both vision and language. For example, GPT-4 can handle documents that include text along with photographs, diagrams, or screenshots, and it exhibits similar capabilities as it does with text-only inputs. This multimodal functionality enables GPT-4 to generate text outputs based on inputs that consist of a combination of text and images.",1.0,1.0,0.8333333332916666 +simple-rag-gpt-4o,cde92d50-7329-4f6f-a29e-6887572db32d,On what architecture the GPT-3 model is based on?,"The GPT-3 model is based on the transformer architecture. This architecture was originally introduced by Vaswani et al. in 2017. GPT-3 leverages various improvements that have been proposed in subsequent models. Specifically, GPT-3 uses pre-normalization, where the input of each transformer sub-layer is normalized instead of the output, and employs the RMSNorm normalizing function. Additionally, GPT-3 incorporates alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer. These modifications help improve training stability and performance.",0.8,0.8329466056919473,0.499999999975 +simple-rag-gpt-4o,3a81972f-efec-474e-a988-83808919db26,How many stages of training are in the GPT model?,"The GPT model is trained in two stages: + +1. **Pre-training**: In this stage, the model is trained on a large dataset of text from the Internet to predict the next word in a sentence. This helps the model learn the structure and patterns of the language. + +2. **Fine-tuning**: After pre-training, the model undergoes fine-tuning using an algorithm called reinforcement learning from human feedback (RLHF). This stage involves using additional data and human feedback to refine the model's outputs to be more aligned with human preferences. + +These two stages help the model develop its language understanding and improve its performance in generating preferred outputs.",0.6923076923076923,0.8796541901899589,0.9999999999 +simple-rag-gpt-4o,90d5a23b-3656-4135-8477-0cc11319e8bd,What tokenizer is used in the Gemma2 model?,"The Gemma 2 model uses a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings. This tokenizer results in a vocabulary with 256,000 entries.",1.0,0.9805458999710387,0.49999999995 +simple-rag-gpt-4o,79ee7c3c-b978-45d6-9a98-dea3713a7c27,On what architecture the Gemma model is based on?,The Gemma model is based on the transformer decoder architecture. This architecture was originally introduced by Vaswani et al. in 2017.,1.0,0.9947045503209968,0.9999999999 +simple-rag-gpt-4o,406184bb-d4e6-48f2-a7e7-757be2d7761e,Does Claude 3 models have vision capabilities?,"Yes, Claude 3 models do have vision capabilities. They can recognize and identify objects visually and understand complex connections between an object's appearance and related concepts, such as mathematics. These models can convert low-quality photos with hard-to-read handwriting into text and organize that text into formats like JSON. However, it's important to note that their performance can be less robust with small or low-resolution images, and they may generate inaccurate information about images, so human validation is recommended for high-precision use cases.",1.0,0.9942643083073888,0.9999999999666667 +simple-rag-gpt-4o,94698f96-eb1a-42e2-a552-ac0028df1a36,What are the two tasks in BERT?,"In BERT (Bidirectional Encoder Representations from Transformers), there are two main tasks involved in its framework: **pre-training** and **fine-tuning**. + +1. **Pre-training**: During this phase, BERT is trained on a large corpus of unlabeled data. The model learns to predict missing words in a sentence (Masked Language Model) and to determine if one sentence follows another (Next Sentence Prediction). This helps the model understand the context and relationships between words and sentences. + +2. **Fine-tuning**: In this phase, BERT is initialized with the pre-trained parameters from the pre-training phase. It is then further trained (fine-tuned) on labeled data specific to a downstream task, such as question answering, sentiment analysis, or named entity recognition. Each downstream task has its own fine-tuned model, even though they all start with the same pre-trained parameters. + +These two tasks allow BERT to be highly versatile and effective across a wide range of natural language processing tasks.",0.5,0.884713892502423,0.8333333332916666 +simple-rag-mixtral-8x7b-instruct,7a1c6789-2612-4e88-8bd4-7d8fe2fa4f6d,How can attention be described in the Transformer?," In the Transformer, attention is used as a mechanism to draw global dependencies between input and output sequences, without relying on recurrence. Instead, it uses a self-attention mechanism that computes a representation of a sequence by relating different positions of the same sequence. This is achieved through the Multi-Head Attention mechanism, which consists of multiple parallel attention layers, or heads. Each head performs its own attention computation, and the results are then concatenated and projected to produce the final output. + +The Transformer employs h = 8 parallel attention heads, with dk = dv = dmodel/h = 64 for each head. This reduces the dimensionality of each head, resulting in a computational cost similar to that of single-head attention with full dimensionality. + +The Transformer uses multi-head attention in three different ways: + +1. Encoder-decoder attention: The queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. +2. Encoder self-attention: All of the keys, values, and queries come from the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder. +3. Decoder self-attention: Each position in the decoder can attend to all positions in the decoder up to and including that position. + +By using attention in this manner, the Transformer can effectively model dependencies between input and output sequences, even when those dependencies are separated by large distances, without relying on recurrence. This allows for significantly more parallelization and can lead to improved translation quality in a shorter training time.",1.0,0.6061674819454508,0.8055555555287036 +simple-rag-mixtral-8x7b-instruct,d2600816-6e5b-4099-9cbf-61825426db47,What is Mixture of Agents?," The Mixture of Agents (MoA) is a methodology introduced to enhance the generation quality of Large Language Models (LLMs). It involves using multiple LLMs in a layered structure, where each layer consists of several agents. These agents generate responses to a given prompt independently, and then the responses are presented to the next layer of agents for further refinement. This iterative process continues until a more robust and comprehensive response is obtained. The MoA approach has been found to significantly improve aspects such as robustness, correctness, efficiency, factuality, commonsense, insightfulness, and completeness compared to a single model score. It outperforms other models in many aspects, except for conciseness, where it tends to produce marginally more verbose outputs.",1.0,0.4480608484378355,0.9166666666361111 +simple-rag-mixtral-8x7b-instruct,a7fa9765-d182-435e-bf82-c8bd65b9374c,Is Mixtral based on the idea of a mixture of experts?," Yes, Mixtral is indeed based on the idea of a mixture of experts. In the Mixtral model, each layer is composed of 8 feedforward blocks, or ""experts"". For every token, a router network selects two experts to process the current state and combine their outputs. This allows each token to have access to 47 billion parameters, but only use 13 billion active parameters during inference. The mixture of experts approach allows Mixtral to outperform other models on various benchmarks while controlling cost and latency.",1.0,0.8528536603556107,0.999999999975 +simple-rag-mixtral-8x7b-instruct,4d1058d1-1a99-4c8b-8110-30b46ad7a459,What is sliding window attention?," Sliding Window Attention (SWA) is an attention mechanism used in the Mistral 7B model to balance the goals of high performance and efficiency in large language models. In SWA, each token can attend to a fixed number (W) of tokens from the previous layer, which helps to reduce the computational costs and inference latency that are typically quadratic in the sequence length in vanilla attention. This mechanism allows for more parallelization and can reach a new state of the art in translation quality after being trained for a relatively short period of time. Additionally, SWA enables the model to handle longer sequences more effectively at a reduced computational cost, making it a valuable approach for real-world applications.",0.5384615384615384,0.651867730804378,0.9999999999 +simple-rag-mixtral-8x7b-instruct,b619c608-35ec-4107-9dab-f0c08b8f2955,How many stages are there in the development of the Llama 3 model?," The development of the Llama 3 model comprises two main stages: + +1. Language model pre-training: This involves converting a large, multilingual text corpus to discrete tokens and pre-training a large language model (LLM) on the resulting data to perform next-token prediction. The model learns the structure of language and obtains knowledge about the world from the text it is ""reading"". Pre-training is performed at a massive scale, involving a model with 405B parameters and 15.6T tokens. + +2. Language model post-training: The pre-trained language model undergoes alignment with human feedback in several rounds, which involves supervised finetuning, rejection sampling, and direct preference optimization. This stage aims to ensure that the model follows instructions and behaves as an assistant should. + +These two stages are essential for developing the Llama 3 model, as they help create a model that performs well on various language understanding tasks and interacts with users in a helpful and harmless manner.",0.7894736842105263,0.9385616026326954,0.9999999999 +simple-rag-mixtral-8x7b-instruct,75f36278-5fde-4b51-826d-a65563ea7c9c,What is the difference between the Llama 2 and Llama 2-Chat ?," The Llama 2 is a pretrained language model that is part of a new family of models with improved performance over the previous Llama 1 models. It is an auto-regressive language model that uses an optimized transformer architecture and has been trained on a new mix of publicly available data. The Llama 2 comes in a range of parameter sizes (7B, 13B, and 70B) and has both pretrained and fine-tuned variations. + +On the other hand, Llama 2-Chat is a fine-tuned version of Llama 2 that is specifically optimized for dialogue use cases. It is designed to perform well in chat applications and is released in variations with 7B, 13B, and 70B parameters. + +In summary, while Llama 2 is a general-purpose pretrained language model, Llama 2-Chat is a fine-tuned version of Llama 2 that is optimized for dialogue-based applications.",0.875,0.9860150863486945,0.8333333332916666 +simple-rag-mixtral-8x7b-instruct,d1a0dd9e-2302-418c-92c3-429bff4452f3,What is optimizer is used for LLaMA?," The Llama models are trained using the AdamW optimizer, which is a variant of the Adam optimizer with decoupled weight decay. This optimizer is commonly used in machine learning to update the model parameters during training. The specific hyperparameters used for the AdamW optimizer in Llama include β1 = 0.9, β2 = 0.95, eps = 10−5, a cosine learning rate schedule with warmup of 2000 steps, decaying the final learning rate down to 10% of the peak learning rate, a weight decay of 0.1, and gradient clipping of 1.0.",0.6923076923076923,0.8345781572965595,0.3333333333 +simple-rag-mixtral-8x7b-instruct,7333d957-9db8-4e30-9b6a-4b47400bacd6,Can the GPT-4 model accept both text and image inputs?," Yes, the GPT-4 model can accept both text and image inputs. This is mentioned in the provided context: ""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task. Specifically, the model generates text outputs given inputs consisting of arbitrarily interlaced text and images.""",1.0,1.0,0.8333333332916666 +simple-rag-mixtral-8x7b-instruct,abd57ad5-2dac-4a23-b9be-0be66f0e40bf,On what architecture the GPT-3 model is based on?," The GPT-3 model is based on the transformer architecture, with several improvements inspired by models such as PaLM and GPTNeo. These improvements include the use of pre-normalization and the SwiGLU activation function, as well as the removal of absolute positional embeddings in favor of rotary embeddings. The specific transformer architecture used in GPT-3 is not explicitly stated in the provided context, but it is mentioned that the model uses alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer.",0.5,0.0,0.499999999975 +simple-rag-mixtral-8x7b-instruct,d920ec74-169e-4a58-bc05-02e9a0d0302b,How many stages of training are in the GPT model?, The GPT model is trained in two stages. The first stage involves training the model to predict the next word using a large dataset of text from the Internet. The second stage is fine-tuning the model with additional data using a process called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers.,1.0,0.6807735367657081,0.9999999999 +simple-rag-mixtral-8x7b-instruct,e04ab448-6bee-4faa-ba12-fc41c88c931d,What tokenizer is used in the Gemma2 model?," The Gemma 2 model uses a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings. This is the same tokenizer that was used in the Gemma 1 model and the Gemini model.",1.0,0.9805458999710387,0.49999999995 +simple-rag-mixtral-8x7b-instruct,feb5e406-ed2e-4ecc-9bf8-de237daee954,On what architecture the Gemma model is based on?," The Gemma model is based on the transformer decoder architecture, as described in the paper ""Attention is All You Need"" by Vaswani et al. (2017). This architecture is also used in the Gemini models, which Gemma is built upon. The transformer decoder architecture is a type of neural network architecture that is commonly used in natural language processing tasks. It is known for its ability to handle sequential data, such as text, and its use of self-attention mechanisms to weigh the importance of different words or tokens in the input sequence.",0.3,0.9947045503209968,0.9999999999 +simple-rag-mixtral-8x7b-instruct,6e29f6f4-b5b0-4342-b58b-eb97c46ba7f1,Does Claude 3 models have vision capabilities?," Yes, based on the provided context, Claude 3 models do have vision capabilities. They can recognize and identify objects visually, and they can process images uploaded along with text prompts for richer context and expanded use cases. However, it's important to note that the performance of these vision capabilities can sometimes be lower for small or low-resolution images, and the model may generate inaccurate information or descriptions about images. Therefore, it's recommended that Claude 3 models should not be used for consequential use cases that require high precision and accuracy without human validation.",1.0,0.9942643083073888,0.9999999999666667 +simple-rag-mixtral-8x7b-instruct,670ea6ed-6b02-45ec-a3c4-925e8be4de5f,What are the two tasks in BERT?," The two tasks in BERT are pre-training and fine-tuning. During pre-training, the model is trained on unlabeled data over different pre-training tasks. For fine-tuning, the BERT model is initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters.",1.0,0.9679117087732756,0.9999999999 +simple-rag-llama-3.1-405b-instruct,267957b3-adb2-45ca-9c5d-942082b4c681,How can attention be described in the Transformer?," + +In the Transformer, attention can be described as a mechanism that allows the model to focus on specific parts of the input sequence when generating the output sequence. This is achieved through a process called self-attention, where the model computes a weighted sum of the input elements based on their relevance to the current output element being generated. + +The Transformer uses a multi-head attention mechanism, which allows the model to jointly attend to information from different representation subspaces at different positions. This is done by applying multiple attention mechanisms in parallel, each with a different set of weights, and then concatenating the results. + +The attention mechanism in the Transformer can be described as follows: + +1. The input sequence is first embedded into a vector space, where each element is represented as a vector. +2. The embedded input sequence is then used to compute a set of attention weights, which represent the relevance of each input element to the current output element being generated. +3. The attention weights are computed using a dot-product attention mechanism, where",0.4117647058823529,0.7616315551800569,0.7499999999625 +simple-rag-llama-3.1-405b-instruct,e9f95145-c541-48a8-b22f-02fbed7e3ef5,What is Mixture of Agents?," + +Mixture-of-Agents (MoA) is a methodology that leverages multiple large language models (LLMs) to iteratively enhance the generation quality of responses. It involves constructing a layered architecture where each layer comprises multiple LLM agents. Each agent takes all the outputs from agents in the previous layer as auxiliary information in generating its response. This process continues for several cycles until a more robust and comprehensive response is obtained. The MoA approach has been shown to achieve state-of-the-art performance on various benchmarks, surpassing individual LLMs.",0.9230769230769231,0.5114405716120649,0.8055555555287036 +simple-rag-llama-3.1-405b-instruct,1081078d-453b-4362-a2d2-ceb6c3c13d34,Is Mixtral based on the idea of a mixture of experts?," + +Yes, Mixtral is based on the idea of a mixture of experts. According to the text, Mixtral is a sparse mixture-of-experts network, where each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. This technique increases the number of parameters of a model while controlling cost and latency, as the model only uses a fraction of the total set of parameters per token.",1.0,0.7634894767055279,0.9999999999666667 +simple-rag-llama-3.1-405b-instruct,32b161e9-8c36-4ddc-9348-308849fa6b9b,What is sliding window attention?," + +Sliding Window Attention (SWA) is a technique used in the Mistral 7B model to improve the efficiency of attention mechanisms in transformer architectures. It allows the model to attend to a fixed-size window of tokens, rather than the entire input sequence, which reduces the computational cost and memory requirements. + +In SWA, each token can attend to at most W tokens from the previous layer, where W is the window size. This means that the model can only attend to a limited number of tokens in the input sequence, rather than the entire sequence. This reduces the number of operations required for attention, making it more efficient. + +The key idea behind SWA is that the model can still capture long-range dependencies in the input sequence by recursively applying the attention mechanism across multiple layers. At each layer, the model can attend to a different set of tokens within the window, allowing it to capture dependencies at different scales. + +In the case of Mistral 7B, the window size W is set",0.8125,0.651867730804378,0.9999999999 +simple-rag-llama-3.1-405b-instruct,f7c93347-c06a-4def-82e1-548951c70429,How many stages are there in the development of the Llama 3 model?," + +There are two main stages in the development of the Llama 3 model: + +1. Language model pre-training: This stage involves pre-training a large language model on a large, multilingual text corpus to perform next-token prediction. +2. Language model post-training: This stage involves aligning the pre-trained language model with human feedback through supervised fine-tuning, rejection sampling, and direct preference optimization.",1.0,0.9385616026326954,0.9999999999 +simple-rag-llama-3.1-405b-instruct,3a8ae902-36db-4522-a260-9ea8a7f6a80b,What is the difference between the Llama 2 and Llama 2-Chat ?," + +Llama 2 and Llama 2-Chat are both large language models developed by Meta AI, but they differ in their training objectives and intended use cases. + +Llama 2 is a general-purpose language model that has been trained on a large corpus of text data to generate human-like language. It is intended for a variety of natural language generation tasks, such as text summarization, language translation, and text generation. + +Llama 2-Chat, on the other hand, is a fine-tuned version of Llama 2 that has been specifically optimized for dialogue use cases. It has been trained using supervised fine-tuning and reinforcement learning with human feedback to align with human preferences for helpfulness and safety. Llama 2-Chat is intended for assistant-like chat applications, where the model is expected to engage in conversation with a user and provide helpful and informative responses. + +In summary, the main difference between Llama 2 and Llama 2-Chat is that Llama",0.7,0.9815519471495716,0.8333333332916666 +simple-rag-llama-3.1-405b-instruct,07f3b5df-5d10-4790-9bd7-b138eb046dea,What is optimizer is used for LLaMA?," + +The optimizer used for LLaMA is AdamW.",1.0,0.8986013185128363,0.3333333333 +simple-rag-llama-3.1-405b-instruct,360ad3fc-f514-4709-87eb-26850eb1e5c0,Can the GPT-4 model accept both text and image inputs?," + +Yes, the GPT-4 model can accept both text and image inputs. According to the context, GPT-4 accepts prompts consisting of both images and text, which allows the user to specify any vision or language task. The model generates text outputs given inputs consisting of arbitrarily interlaced text and images. This capability is demonstrated in various domains, including documents with text and photographs, diagrams, or screenshots.",1.0,0.7444278589409986,0.8333333332916666 +simple-rag-llama-3.1-405b-instruct,c3903cec-cd6b-473d-8786-a5a4467dea58,On what architecture the GPT-3 model is based on?," + +The GPT-3 model is based on the transformer architecture.",1.0,0.9935035488418335,0.249999999975 +simple-rag-llama-3.1-405b-instruct,c14ae752-6b3b-4f41-ab43-2f4a6ce435e0,How many stages of training are in the GPT model?," + +The GPT model is trained in two stages. The first stage involves training the model on a large dataset of text from the Internet to predict the next word. The second stage involves fine-tuning the model with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers.",1.0,0.6807735367657081,0.9999999999 +simple-rag-llama-3.1-405b-instruct,3ff82bb7-9646-4c17-8f56-d80a9363d46c,What tokenizer is used in the Gemma2 model?," + +The tokenizer used in the Gemma 2 model is a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings (Kudo and Richardson, 2018). The resulting vocabulary has 256k entries.",0.8333333333333334,0.9815825225561667,0.49999999995 +simple-rag-llama-3.1-405b-instruct,4f64eb9b-18a6-4394-b610-6915d149a2af,On what architecture the Gemma model is based on?," + +The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017).",1.0,0.980834227715684,0.9999999999 +simple-rag-llama-3.1-405b-instruct,a0a3537a-77fe-4edf-bccd-b5e761d44813,Does Claude 3 models have vision capabilities?," + +Yes, Claude 3 models have vision capabilities. They can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics. Additionally, they can convert low-quality photos with hard-to-read handwriting into text and organize the text into a JSON format. However, it's noted that their performance may be lower for small or low-resolution images, and they may generate inaccurate information and descriptions about images at times.",1.0,0.9136744356048091,0.9999999999666667 +simple-rag-llama-3.1-405b-instruct,266cde88-5fee-4811-9a24-65dc9e7078bf,What are the two tasks in BERT?," + +The two tasks in BERT are: + +1. Pre-training: The model is trained on unlabeled data over different pre-training tasks. +2. Fine-tuning: The BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks.",1.0,0.972429714219599,0.9999999999 +simple-rag-llama-3.1-70b-instruct,24390fdf-9558-4834-a2be-8b1d8b778ee9,How can attention be described in the Transformer?," + +According to the provided context, attention in the Transformer can be described as a mechanism that allows the model to draw global dependencies between input and output sequences without regard to their distance. It is an integral part of the Transformer architecture, which relies entirely on self-attention to compute representations of its input and output. + +In the Transformer, attention is used in three different ways: + +1. Encoder-decoder attention: This allows every position in the decoder to attend over all positions in the input sequence. +2. Self-attention in the encoder: This allows each position in the encoder to attend to all positions in the previous layer of the encoder. +3. Self-attention in the decoder: This allows each position in the decoder to attend to all positions in the decoder up to and including that position. + +The Transformer uses multi-head attention, which is a variant of attention that allows the model to jointly attend to information from different representation subspaces at different positions. This is achieved by linearly projecting the queries, keys, and values, and then applying attention to each projected representation. The outputs from each attention head are then concatenated and linearly transformed to produce the final output.",0.75,0.8649486707196153,0.8055555555287036 +simple-rag-llama-3.1-70b-instruct,4ca88ef8-2035-4fa7-b1c1-8878c5c1fac1,What is Mixture of Agents?," + +Mixture of Agents (MoA) is a methodology that leverages multiple Large Language Models (LLMs) to iteratively enhance the generation quality of responses. It involves constructing a layered architecture where each layer comprises multiple LLM agents. Each agent takes all the outputs from agents in the previous layer as auxiliary information in generating its response. This approach allows the models to collaborate and build upon each other's strengths, ultimately producing more robust and comprehensive responses.",0.6923076923076923,0.5329962282622026,0.8055555555287036 +simple-rag-llama-3.1-70b-instruct,bf8e0908-d397-4a4f-bacd-82d1a14d32be,Is Mixtral based on the idea of a mixture of experts?," + +Yes, Mixtral is based on the idea of a mixture of experts. Specifically, it is a sparse mixture-of-experts (SMoE) network, where each layer is composed of 8 feedforward blocks (i.e. experts). For every token, a router network selects two experts to process the current state and combine their outputs. This technique allows Mixtral to increase the number of parameters of a model while controlling cost and latency, as the model only uses a fraction of the total set of parameters per token.",1.0,0.6958164930001116,0.9999999999666667 +simple-rag-llama-3.1-70b-instruct,0a77638e-5f71-47a7-88aa-20481852fd13,What is sliding window attention?," + +Sliding Window Attention (SWA) is a type of attention mechanism used in transformer-based language models, such as Mistral 7B. It allows the model to attend to information beyond the window size W, by exploiting the stacked layers of the transformer. + +In traditional attention mechanisms, each token can only attend to tokens within a fixed window size. However, in SWA, each token can attend to tokens from the previous layer, which can be outside the current window size. This allows the model to capture longer-range dependencies and relationships between tokens. + +The way it works is that each token in the current layer attends to all tokens from the previous layer within a certain window size. This window size is typically smaller than the sequence length, but can be larger than the traditional attention window size. + +By recursively applying this process across multiple layers, the model can effectively attend to tokens that are far away from each other in the input sequence. This allows the model to capture complex relationships and dependencies between tokens, which can be useful for tasks such as language translation, text summarization, and question answering. + +In the case of Mistral 7B, the sliding window attention mechanism is designed to handle longer sequences more effectively at a reduced computational cost. This is achieved by limiting the attention span to a fixed size, which allows for more efficient computation and memory usage. + +Overall, sliding window attention is a powerful attention mechanism that can help improve the performance of transformer-based language models, especially for tasks that require capturing long-range dependencies and relationships between tokens.",0.4814814814814815,0.5446090459513437,0.9999999999 +simple-rag-llama-3.1-70b-instruct,4bd3397a-070f-4ffb-9249-c2b3d5c8925a,How many stages are there in the development of the Llama 3 model?," + +There are two main stages in the development of the Llama 3 model: + +1. Language model pre-training: This stage involves pre-training a large language model on a large, multilingual text corpus to perform next-token prediction. +2. Language model post-training: This stage involves aligning the pre-trained language model with human feedback through several rounds of supervised fine-tuning, rejection sampling, and direct preference optimization.",1.0,0.9385616026326954,0.9999999999 +simple-rag-llama-3.1-70b-instruct,43be3113-264c-4ff0-851a-f37283d2c3d2,What is the difference between the Llama 2 and Llama 2-Chat ?," + +The Llama 2 and Llama 2-Chat are two different models developed by Meta AI. The main difference between them is their purpose and training. + +Llama 2 is a general-purpose language model that is trained on a large corpus of text data to generate human-like text. It is available in different sizes, including 7B, 13B, and 70B parameters. Llama 2 is intended for commercial and research use in English and can be adapted for a variety of natural language generation tasks. + +On the other hand, Llama 2-Chat is a fine-tuned version of Llama 2 that is specifically designed for dialogue use cases. It is trained using reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. Llama 2-Chat is also available in different sizes, including 7B, 13B, and 70B parameters. + +In summary, the main difference between Llama 2 and Llama 2-Chat is that Llama 2 is a general-purpose language model, while Llama 2-Chat is a specialized model designed for chat and dialogue applications.",0.92,0.9815519471495716,0.8333333332916666 +simple-rag-llama-3.1-70b-instruct,cac378a7-430f-438c-a7bc-69c79fbfea54,What is optimizer is used for LLaMA?," + +The optimizer used for LLaMA is AdamW.",1.0,0.9039968374174632,0.3333333333 +simple-rag-llama-3.1-70b-instruct,a7d84169-5c32-406e-b277-9525c2ced5fc,Can the GPT-4 model accept both text and image inputs?," + +Yes, the GPT-4 model can accept both text and image inputs. According to the provided context, GPT-4 accepts prompts consisting of both images and text, which allows the user to specify any vision or language task. The model can generate text outputs given inputs consisting of arbitrarily interlaced text and images, and it exhibits similar capabilities on a range of domains, including documents with text and photographs, diagrams, or screenshots.",1.0,0.7447086516158969,0.8333333332916666 +simple-rag-llama-3.1-70b-instruct,04d8128f-f1ad-4a32-b7ce-3ab3a3749b2e,On what architecture the GPT-3 model is based on?," + +The GPT-3 model is based on the transformer architecture, specifically the Sparse Transformer. It uses alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer.",0.8,0.9935035488418335,0.49999999995 +simple-rag-llama-3.1-70b-instruct,afc00b79-78a7-4a04-8a61-82eb6b15778f,How many stages of training are in the GPT model?," + +The GPT model is trained in two stages. First, it is trained to predict the next word using a large dataset of text from the Internet. Then, it is fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers.",1.0,0.6807735367657081,0.9999999999 +simple-rag-llama-3.1-70b-instruct,07fce096-2868-4412-acba-ea0605f15eb1,What tokenizer is used in the Gemma2 model?," + +The Gemma 2 model uses a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings.",1.0,0.9805458999710387,0.49999999995 +simple-rag-llama-3.1-70b-instruct,ac8dfb07-b29c-43e1-937f-6dfb7e3c745e,On what architecture the Gemma model is based on?," + +The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017).",1.0,0.980834227715684,0.9999999999 +simple-rag-llama-3.1-70b-instruct,b8522a46-5e91-4571-80f8-0bdafa5863f5,Does Claude 3 models have vision capabilities?," + +Yes, Claude 3 models have vision capabilities. According to the provided context, Claude 3 models can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics. Additionally, the models can convert low-quality photos with hard-to-read handwriting into text and organize the text into a JSON format. They can also process images, including tables, graphs, and photos, along with text prompts for richer context and expanded use cases.",1.0,0.9136744356048091,0.8333333332916666 +simple-rag-llama-3.1-70b-instruct,4776db54-7919-4dd3-8f0f-bd4a3f646016,What are the two tasks in BERT?," + +The two tasks in BERT are: + +1. Pre-training: During pre-training, the model is trained on unlabeled data over different pre-training tasks. +2. Fine-tuning: For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks.",1.0,0.9999999999999996,0.8333333332916666 +simple-rag-llama-3.1-8b,85e53ad2-034c-4d58-be75-2936484bb19b,How can attention be described in the Transformer?," + +In the Transformer model, attention is described as a mechanism that allows the model to focus on different parts of the input sequence when computing the representation of a particular position. This is achieved through the use of self-attention and multi-head attention. + +Self-attention is an attention mechanism that allows a position in the input sequence to attend to all other positions in the same sequence. In the Transformer, self-attention is used in the encoder and decoder stacks to allow each position to attend to all other positions in the input sequence. + +Multi-head attention is a variant of self-attention that allows the model to jointly attend to information from different representation subspaces at different positions. In the Transformer, multi-head attention is used in the encoder and decoder stacks to allow the model to attend to different parts of the input sequence in parallel. + +The Transformer uses multi-head attention in three different ways: + +1. In ""encoder-decoder attention"" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. +2. The encoder contains self-attention layers. In a self-attention layer, all of the keys, values, and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder. +3. Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. + +The Transformer's attention mechanism is described as a way to draw global dependencies between input and output sequences, allowing the model to capture long-range dependencies without the need for recurrent neural networks (RNNs) or convolutional neural networks (CNNs).",0.8,0.7575874974984704,0.8055555555287036 +simple-rag-llama-3.1-8b,ad57a8bb-bd76-4907-a4d4-8ec736edea46,What is Mixture of Agents?," + +Mixture-of-Agents (MoA) is a methodology that leverages multiple large language models (LLMs) to achieve boosted performance in natural language understanding and generation tasks. It is a layered architecture where each layer comprises multiple LLM agents, and each agent takes the outputs from the previous layer as auxiliary information to generate its response. + +In the MoA approach, the models are categorized into two distinct roles: + +1. Proposers: These models excel at generating useful reference responses for use by other models. They may not produce high-scoring responses on their own, but they provide context and diverse perspectives that contribute to better final responses when used by an aggregator. +2. Aggregators: These models are proficient in synthesizing responses from other models into a single, high-quality output. An effective aggregator maintains or enhances output quality even when using responses from other models. + +The MoA methodology has been shown to significantly outperform LLM rankers and achieve state-of-the-art performance on various benchmarks, including AlpacaEval 2.0, MT-Bench, and FLASK. It has also been demonstrated to surpass the performance of GPT-4 Omni in several key aspects, such as correctness, factuality, insightfulness, and completeness.",1.0,0.6236265068677259,0.8055555555287036 +simple-rag-llama-3.1-8b,3e052d83-2c4e-4a40-8913-353e2557234d,Is Mixtral based on the idea of a mixture of experts?," + +Yes, Mixtral is based on the idea of a mixture of experts. According to the paper, Mixtral is a sparse mixture-of-experts network, where each layer is composed of 8 feedforward blocks (experts) that are selected by a router network to process the input and combine their outputs. This allows Mixtral to use a subset of its parameters for every token, while still achieving state-of-the-art performance.",0.7777777777777778,0.4853798518381936,0.999999999975 +simple-rag-llama-3.1-8b,474fbef7-d9c2-4c00-9d22-83584795c7fc,What is sliding window attention?," + +Sliding Window Attention (SWA) is a technique used in the Mistral 7B model to alleviate the limitations of traditional attention mechanisms. It allows each token to attend to at most W tokens from the previous layer, where W is a fixed window size. This means that instead of considering all tokens in the sequence, the model can focus on a smaller window of tokens, reducing the computational costs and memory requirements. + +In the context of the Mistral 7B model, the window size W is set to 4096, which allows the model to attend to up to 4096 tokens from the previous layer. This is illustrated in Figure 1, where the model can attend to tokens from the previous layer within the window size W. + +The use of SWA has several benefits, including: + +* Reduced computational costs: By limiting the number of tokens that need to be considered, SWA reduces the computational costs of attention mechanisms. +* Improved memory efficiency: SWA reduces the memory requirements during decoding, allowing for higher batch sizes and higher throughput. +* Enhanced performance: SWA is designed to handle longer sequences more effectively at a reduced computational cost, which can lead to improved performance in certain tasks. + +Overall, Sliding Window Attention is a technique that allows the Mistral 7B model to efficiently process longer sequences while maintaining high performance.",0.7,0.651867730804378,0.7499999999625 +simple-rag-llama-3.1-8b,ea626f5d-f44e-4e68-8974-b0bb2330f574,How many stages are there in the development of the Llama 3 model?," + +According to the provided context, the development of the Llama 3 language models comprises two main stages: + +1. Language model pre-training: This stage involves converting a large, multilingual text corpus to discrete tokens and pre-training a large language model (LLM) on the resulting data to perform next-token prediction. +2. Language model post-training: This stage involves aligning the pre-trained language model with human feedback in several rounds, each of which involves supervised fine-tuning with integrating multimodal capabilities into Llama 3. + +So, there are 2 stages in the development of the Llama 3 model.",0.9,0.8601381129012596,0.9999999999 +simple-rag-llama-3.1-8b,f455235b-768b-4bff-b3bb-b14036aecd3e,What is the difference between the Llama 2 and Llama 2-Chat ?," + +According to the provided context, Llama 2 and Llama 2-Chat are two different models developed by Meta AI. + +Llama 2 is a general-purpose language model that is trained on a large corpus of text data and can be used for a variety of natural language generation tasks. It is an auto-regressive language model that uses an optimized transformer architecture and has been trained on a new mix of publicly available data, with a 40% increase in the size of the pretraining corpus, doubled context length, and grouped-query attention. + +Llama 2-Chat, on the other hand, is a fine-tuned version of Llama 2 that is specifically designed for dialogue use cases. It has been optimized for assistant-like chat and is intended for commercial and research use in English. The main difference between Llama 2 and Llama 2-Chat is that Llama 2-Chat has been fine-tuned for dialogue tasks, whereas Llama 2 is a more general-purpose language model. + +In other words, Llama 2 is a more general-purpose language model that can be used for a wide range of tasks, while Llama 2-Chat is a specialized model that is specifically designed for dialogue tasks and is optimized for assistant-like chat.",0.9,0.9815519471495716,0.8333333332916666 +simple-rag-llama-3.1-8b,c79cbfbb-dcf3-44b4-b0de-cd35cb5eea30,What is optimizer is used for LLaMA?," + +According to the text, the optimizer used for LLaMA is AdamW, which is used with the following hyperparameters: + +* β1 = 0.9 +* β2 = 0.95 +* eps = 10^(-5) +* Weight decay of 0.1 +* Gradient clipping of 1.0 + +The AdamW optimizer is used with a cosine learning rate schedule, with a warmup of 2000 steps and a decay final learning rate down to 10% of the peak learning rate.",1.0,0.8353384665946649,0.3333333333 +simple-rag-llama-3.1-8b,7e78af9c-1194-47ad-ad95-98e3d29733b7,Can the GPT-4 model accept both text and image inputs?," + +Yes, the GPT-4 model can accept both text and image inputs. According to the text, GPT-4 exhibits similar capabilities as it does on text-only inputs when given documents with text and photographs, diagrams, or screenshots. This means that the model can generate text outputs given inputs consisting of arbitrarily interlaced text and images.",1.0,0.7452702369656934,0.8333333332916666 +simple-rag-llama-3.1-8b,b85cae86-ee84-4d85-9d0f-84a40a5286c7,On what architecture the GPT-3 model is based on?," + +The GPT-3 model is based on the transformer architecture, which was first introduced by Vaswani et al. in 2017. However, the GPT-3 model also incorporates several improvements and modifications to the original transformer architecture, including: + +* Pre-normalization, which normalizes the input of each transformer sub-layer instead of the output, using the RMSNorm normalizing function. +* SwiGLU activation function, which replaces the ReLU non-linearity to improve performance. +* Rotary Embeddings, which remove the absolute positional embeddings and instead add relative positional embeddings. + +These modifications were inspired by recent work on large language models, such as PaLM, and were found to improve the performance of the GPT-3 model.",0.8333333333333334,0.62241531757762,0.249999999975 +simple-rag-llama-3.1-8b,a39a694b-fd93-4fc8-a28b-b09df7650dd1,How many stages of training are in the GPT model?," + +According to the provided context, the GPT model is trained in two stages. First, they are trained on a large dataset of text from the Internet to predict the next word. Then, they are fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers.",1.0,0.6703254856570627,0.9999999999 +simple-rag-llama-3.1-8b,e40637bb-0bc0-420a-9eee-1540cd6c2357,What tokenizer is used in the Gemma2 model?," +The tokenizer used in the Gemma2 model is a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings, as described in Kudo and Richardson (2018). This tokenizer has a vocabulary of 256k entries.",1.0,0.9916950262807455,0.49999999995 +simple-rag-llama-3.1-8b,cfe75fc5-feef-45c7-8539-933069fa8261,On what architecture the Gemma model is based on?," +The Gemma model architecture is based on the transformer decoder, specifically the Vaswani et al. (2017) architecture.",1.0,0.980834227715684,0.9999999999 +simple-rag-llama-3.1-8b,88195490-7bc9-4a2d-99c4-5c9ce771f14e,Does Claude 3 models have vision capabilities?," + +Yes, Claude 3 models have vision capabilities. According to the provided context, Claude 3 models can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics. Additionally, the models can process images, including tables, graphs, and photos, and can even identify objects within those images.",1.0,0.9942643083073888,0.8333333332916666 +simple-rag-llama-3.1-8b,22da4dd9-eb7b-4823-b6bb-1c37f91b1003,What are the two tasks in BERT?," + +According to the provided context, the two tasks in BERT are: + +1. Pre-training: During pre-training, the model is trained on unlabeled data over different pre-training tasks. +2. Fine-tuning: During fine-tuning, the pre-trained model is fine-tuned using labeled data from the downstream tasks.",1.0,0.893674110975772,0.9999999999 +simple-rag-llama-3-70b,e49b5201-8cb8-4ffb-be97-5f97f7ec5d33,How can attention be described in the Transformer?," + +In the Transformer model, attention can be described as a mechanism that allows the model to attend to different parts of the input sequence simultaneously and weigh their importance. This is achieved through the use of multi-head attention, which is a key component of the Transformer architecture. + +Specifically, attention in the Transformer is described as a function that takes in three inputs: queries (Q), keys (K), and values (V). These inputs are first linearly transformed into three vectors using learned parameter matrices WQ, WK, and WV. Then, the attention weights are computed by taking the dot product of the queries and keys, and applying a softmax function. + +The attention weights are then used to compute a weighted sum of the values, which produces the final output. This process is repeated multiple times in parallel, with different learned linear projections of the queries, keys, and values. This is known as multi-head attention, and it allows the model to jointly attend to information from different representation subspaces at different positions. + +In the Transformer, attention is used in three different ways: + +1. Encoder-decoder attention: This allows the decoder to attend to all positions in the input sequence. +2. Self-attention in the encoder: This allows each position in the encoder to attend to all positions in the previous layer of the encoder. +3. Self-attention in the decoder: This allows each position in the decoder to attend to all positions in the decoder up to and including that position. + +Overall, attention in the Transformer allows the model to capture long-range dependencies and relationships between different parts of the input sequence, which is particularly useful for tasks such as machine translation.",0.7037037037037037,0.8036632481494458,0.7499999999625 +simple-rag-llama-3-70b,7838da43-330a-4b8a-b565-b3edcbcce297,What is Mixture of Agents?," + +Mixture of Agents (MoA) is a methodology that leverages multiple Large Language Models (LLMs) to iteratively enhance the generation quality. It's a layered architecture where each layer consists of multiple LLM agents. Each agent takes all the outputs from agents in the previous layer as auxiliary information in generating its response. + +In other words, MoA is a collaborative approach where multiple LLMs work together to generate a more robust and comprehensive response. The process starts with a prompt, and the first layer of agents generates responses independently. These responses are then presented to the next layer of agents for further refinement, and this process continues for several cycles. + +The MoA approach is designed to harness the collective strengths of multiple LLMs, allowing them to learn from each other and improve their responses. This methodology has been shown to achieve state-of-the-art performance on various benchmarks, surpassing even highly optimized single models like GPT-4 Omni.",0.8636363636363636,0.6081442820517281,0.8055555555287036 +simple-rag-llama-3-70b,8752dce2-c4bd-4122-a8bf-9e7e65a111e1,Is Mixtral based on the idea of a mixture of experts?," + +Yes, Mixtral is based on the idea of a mixture of experts. In fact, it is a Sparse Mixture of Experts (SMoE) language model. This means that each layer in the model is composed of multiple experts (in this case, 8 feedforward blocks), and a router network selects a subset of these experts (2 in this case) to process each token. The outputs of the selected experts are then combined to produce the final output. This approach allows Mixtral to have a large number of parameters (47B) while only using a fraction of them (13B) during inference, making it more efficient and scalable.",0.75,0.7300998694731305,0.999999999975 +simple-rag-llama-3-70b,c0aacfbe-3ba8-4a9b-8c6b-9febb7b756dd,What is sliding window attention?," + +Sliding Window Attention (SWA) is a technique used in transformer-based models, like Mistral 7B, to attend to information beyond the window size W. Here's how it works: + +In a traditional transformer architecture, each token in a layer attends to all tokens in the previous layer. However, with SWA, each token in position i of layer k attends to all hidden states from the previous layer with positions between i - W and i. This means that the token can access tokens from the input layer at a distance of up to W × k tokens. + +To illustrate this, let's consider an example. Suppose we have a window size W = 3, and we're at layer k = 2. The token at position i in layer 2 can attend to tokens at positions i - 3, i - 2, i - 1, i, i + 1, and i + 2 in the previous layer. This allows the token to access information from a larger context than just the immediate neighboring tokens. + +By recursively applying this process, the token can access tokens from the input layer at a distance of up to W × k tokens. In the case of Mistral 7B, with a window size of W = 4096, the theoretical attention span is approximately 131K tokens. + +SWA is particularly useful for handling longer sequences, as it reduces the computational cost and memory requirements compared to traditional attention mechanisms. This makes it more efficient and scalable for real-world applications.",0.4,0.5474125891504549,0.9999999999 +simple-rag-llama-3-70b,1e924508-4a88-4fe7-9e42-35a500d36288,How many stages are there in the development of the Llama 3 model?," + +According to the provided context, there are two main stages in the development of the Llama 3 model: + +1. **Language model pre-training**: In this stage, a large language model (LLM) is pre-trained on a large, multilingual text corpus to perform next-token prediction. This stage is further divided into two sub-stages: a standard pre-training stage and a continued pre-training stage that increases the supported context window. + +2. **Language model post-training**: In this stage, the pre-trained language model is aligned with human feedback through several rounds of supervised fine-tuning, rejection sampling, and direct preference optimization. This stage is aimed at making the model follow instructions and behave like an assistant.",1.0,0.9385616026326954,0.9999999999 +simple-rag-llama-3-70b,97f553c6-adfb-4eda-9ce9-4db079be07bb,What is the difference between the Llama 2 and Llama 2-Chat ?," + +The main difference between Llama 2 and Llama 2-Chat is their fine-tuning and intended use cases. + +Llama 2 is a pre-trained language model that has been trained on a large corpus of publicly available text data. It's an auto-regressive language model that uses an optimized transformer architecture. The pre-trained Llama 2 models can be adapted for various natural language generation tasks. + +On the other hand, Llama 2-Chat is a fine-tuned version of Llama 2, specifically optimized for dialogue use cases, such as chatbots or conversational AI. The fine-tuning process involves supervised fine-tuning and reinforcement learning with human feedback (RLHF) to align the model with human preferences for helpfulness and safety. + +In other words, Llama 2 is a more general-purpose language model, while Llama 2-Chat is a specialized version of the model that's tailored for chat-like conversations. The fine-tuning process for Llama 2-Chat involves human feedback and evaluation, which helps the model learn to generate more helpful and safe responses in a conversational setting. + +Both Llama 2 and Llama 2-Chat come in different parameter sizes, including 7B, 13B, and 70B, but the key difference lies in their fine-tuning and intended use cases.",0.7727272727272727,0.9815519471495716,0.8333333332916666 +simple-rag-llama-3-70b,26c2c40f-51bf-4b16-9e42-a9f71dbb0de2,What is optimizer is used for LLaMA?," + +According to the provided context, the optimizer used for LLaMA is AdamW. This is mentioned in the ""Training Details"" section, specifically in the ""Hyperparameters"" subsection. The exact quote is: + +""We trained using the AdamW optimizer (Loshchilov and Hutter, 2017), with β1 = 0.9, β2 = 0.95, eps = 10−5.""",1.0,0.914787875226717,0.3333333333 +simple-rag-llama-3-70b,9f3f0626-50de-46c8-a799-646f20edaaf9,Can the GPT-4 model accept both text and image inputs?," + +According to the provided context, the answer is yes, the GPT-4 model can accept both text and image inputs. In fact, it is mentioned that ""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task."" This means that the model can process and generate text outputs given inputs that consist of arbitrarily interlaced text and images, such as documents with text and photographs, diagrams, or screenshots.",1.0,1.0,0.8333333332916666 +simple-rag-llama-3-70b,a576229a-713c-43df-9702-e4cdec578f29,On what architecture the GPT-3 model is based on?," + +The GPT-3 model is based on the transformer architecture, specifically a modified version of it. The modifications include pre-normalization, SwiGLU activation function, and rotary embeddings, which are inspired by other models such as GPT-2, PaLM, and GPTNeo.",0.3333333333333333,0.9935035488418335,0.249999999975 +simple-rag-llama-3-70b,2c18e543-79f1-4668-96e9-c99ae08a98b0,How many stages of training are in the GPT model?," + +According to the provided context, GPT models are trained in two stages: + +1. **Pre-training**: The model is trained using a large dataset of text from the Internet to predict the next word. +2. **Fine-tuning**: The model is fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers.",1.0,0.6703254856570627,0.9999999999 +simple-rag-llama-3-70b,714f80f3-b988-4a66-ad1e-2a0de82753cb,What tokenizer is used in the Gemma2 model?," + +According to the provided context, the tokenizer used in the Gemma 2 model is a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings (Kudo and Richardson, 2018). This tokenizer is the same as the one used in Gemma 1 and Gemini, and it results in a vocabulary of 256k entries.",0.875,0.9815825225561667,0.49999999995 +simple-rag-llama-3-70b,c496da6d-ca87-4958-a650-96eeb9f019f3,On what architecture the Gemma model is based on?," + +The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017).",1.0,0.980834227715684,0.9999999999 +simple-rag-llama-3-70b,46ab95f8-adf7-4e22-8356-537d59010518,Does Claude 3 models have vision capabilities?," + +According to the provided context, yes, Claude 3 models do have vision capabilities. In fact, they can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics. Additionally, they can process multimodal input, which means users can upload images (e.g., tables, graphs, photos) along with text prompts to provide richer context and expanded use cases.",1.0,0.9942643083073888,0.8333333332916666 +simple-rag-llama-3-70b,8cc4071d-9a93-498d-b3c6-a6b863c8f4e5,What are the two tasks in BERT?," + +According to the provided context, the two tasks in BERT are: + +1. **Pre-training**: During pre-training, the model is trained on unlabeled data over different pre-training tasks. This involves training the model on a large corpus of text, such as the BooksCorpus and English Wikipedia, to learn general language representations. + +2. **Fine-tuning**: For fine-tuning, the BERT model is first initialized with the pre-trained parameters, and all of the parameters are fine-tuned using labeled data from the downstream tasks. This involves adapting the pre-trained model to a specific task, such as question-answering, sentiment analysis, or named entity recognition, by training it on a smaller dataset specific to that task.",0.75,0.9679117087732756,0.8333333332916666 +simple-rag-llama-3-8b,1d322d13-ff9a-4f5e-955f-a302afe62048,How can attention be described in the Transformer?," + +According to the provided context, attention in the Transformer can be described as follows: + +The Transformer uses multi-head attention in three different ways: + +1. In ""encoder-decoder attention"" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. +2. The encoder contains self-attention layers. In a self-attention layer, all of the keys, values, and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder. +3. Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. + +The multi-head attention mechanism is defined as: + +MultiHead(Q, K, V) = Concat(head1, ..., headh)WO + +where headi = Attention(QWQi, KWKi, VWi) + +The attention mechanism is used to compute a weighted sum of the values (V) based on the attention weights computed from the queries (Q) and keys (K). The attention weights are computed using a softmax function. + +In the Transformer, the attention mechanism is used to draw global dependencies between input and output sequences, allowing the model to model long-range dependencies without relying on recurrent neural networks (RNNs) or convolutional neural networks (CNNs).",0.6666666666666666,0.7017209050242159,0.9999999999 +simple-rag-llama-3-8b,4759918e-09d8-4fcd-ac17-b826010f4625,What is Mixture of Agents?," + +According to the provided context, Mixture-of-Agents (MoA) is a methodology that leverages multiple Large Language Models (LLMs) to iteratively enhance the generation quality of responses. The structure of MoA is illustrated in Figure 2, which shows a layered architecture with multiple LLM agents in each layer. Each agent takes the outputs from agents in the previous layer as auxiliary information to generate its response. + +In the MoA methodology, the process begins with LLMs in the first layer, denoted as agents A1,1, ..., A1,n, independently generating responses to a given prompt. These responses are then presented to agents in the next layer, A2,1, ..., A2,n, for further refinement. This iterative refinement process continues for several cycles until obtaining a more robust and comprehensive response. + +The MoA approach has been shown to significantly outperform a single model alone, achieving state-of-the-art performance on various benchmarks, including AlpacaEval 2.0, MT-Bench, and FLASK.",0.8571428571428571,0.5638222676158946,0.7499999999625 +simple-rag-llama-3-8b,94798547-5075-462e-a49d-c594720a99d4,Is Mixtral based on the idea of a mixture of experts?," + +Yes, Mixtral is based on the idea of a Mixture of Experts (MoE) architecture. Specifically, it is a Sparse Mixture of Experts (SMoE) model, which is a type of neural network that combines the outputs of multiple experts to produce the final output. In Mixtral, each layer is composed of 8 feedforward blocks (experts), and a router network selects two experts to process the current state and combine their outputs. This allows the model to leverage the strengths of multiple experts while controlling the number of active parameters and reducing computational costs.",0.6666666666666666,0.6315072616697112,0.9999999999666667 +simple-rag-llama-3-8b,c42c68b6-af7a-4f3f-ad65-5312db64d43c,What is sliding window attention?," + +According to the provided context, Sliding Window Attention (SWA) is a mechanism used in the Mistral 7B model architecture. It allows each token to attend to information beyond the window size W by exploiting the stacked layers of a transformer. Specifically, the hidden state in position i of the layer k, hi, attends to all hidden states from the previous layer with positions between i-W and i. This means that hi can access tokens from the input layer at a distance of up to W × k tokens. + +In other words, SWA enables the model to attend to information that is outside the traditional attention window, which is typically limited to a fixed number of tokens. This is achieved by recursively applying the attention mechanism across multiple layers, allowing the model to access information from a larger context. + +In the case of Mistral 7B, the window size W is set to 4096, which means that the model can attend to information up to 131K tokens in theory, and 16K tokens in practice. This is achieved through a combination of the sliding window attention mechanism and the rolling buffer cache, which limits the cache size to W and stores keys and values for each timestep in a fixed position in the cache.",0.6875,0.5814366521676471,0.9999999999 +simple-rag-llama-3-8b,fead8486-a8ca-4bd9-8199-7bcd30b527df,How many stages are there in the development of the Llama 3 model?," + +According to the provided context, the development of the Llama 3 language models comprises two main stages: + +1. Language model pre-training: This stage involves converting a large, multilingual text corpus to discrete tokens and pre-training a large language model (LLM) on the resulting data to perform next-token prediction. +2. Language model post-training: This stage involves aligning the pre-trained language model with human feedback in several rounds, each of which involves supervised fine-tuning on supervised finetuning (SFT), rejection sampling (RS), and direct preference optimization (DPO). + +These two stages are the primary components of the Llama 3 model development process.",1.0,0.8209282447089518,0.9999999999 +simple-rag-llama-3-8b,91ad6f4c-deed-4b09-b309-092e493bf288,What is the difference between the Llama 2 and Llama 2-Chat ?," + +According to the provided context, Llama 2 is a pre-trained language model that uses an optimized transformer architecture, whereas Llama 2-Chat is a fine-tuned version of Llama 2 that is specifically optimized for dialogue use cases. + +The main difference between the two is that Llama 2 is a general-purpose language model, whereas Llama 2-Chat is a specialized model designed for conversational AI applications, such as chatbots and virtual assistants. + +Llama 2-Chat has undergone additional training using Reinforcement Learning with Human Feedback (RLHF) methodologies, specifically through rejection sampling and Proximal Policy Optimization (PPO), to align the model's responses with human preferences for helpfulness and safety. This fine-tuning process has resulted in a model that is better suited for generating human-like conversations. + +In contrast, Llama 2 is a pre-trained model that can be used for a variety of natural language generation tasks, but it has not been specifically optimized for dialogue use cases. + +In summary, Llama 2 is a general-purpose language model, while Llama 2-Chat is a specialized model designed for conversational AI applications, with additional training and fine-tuning for dialogue use cases.",0.7333333333333333,0.9815519471495716,0.8333333332916666 +simple-rag-llama-3-8b,b3815f8c-8e0e-45ba-9e38-925a6b979bb9,What is optimizer is used for LLaMA?," + +According to the provided context, the optimizer used for LLaMA is AdamW optimizer with the following hyperparameters: + +* β1 = 0.9 +* β2 = 0.95 +* eps = 10^(-5) + +These hyperparameters are used in combination with a cosine learning rate schedule, with warmup of 2000 steps, and decay final learning rate down to 10% of the peak learning rate. Additionally, a weight decay of 0.1 and gradient clipping of 1.0 are also used.",1.0,0.8351883505708505,0.3333333333 +simple-rag-llama-3-8b,dcfe704f-9ede-43cd-aab5-21f84bcffa91,Can the GPT-4 model accept both text and image inputs?," + +According to the provided context, yes, the GPT-4 model can accept both text and image inputs. In fact, it is mentioned that GPT-4 ""accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task."" This means that the model can generate text outputs given inputs consisting of arbitrarily interlaced text and images, and it can perform various tasks across different domains, including documents with text and photographs, diagrams, or screenshots.",1.0,1.0,0.8333333332916666 +simple-rag-llama-3-8b,06a75aff-7e25-4925-a664-1e730a97d3c5,On what architecture the GPT-3 model is based on?," + +According to the text, the GPT-3 model is based on the transformer architecture, which was introduced by Vaswani et al. in 2017. The authors of GPT-3 leveraged various improvements that were subsequently proposed and used in different models, such as PaLM. The main differences between the original architecture and the GPT-3 architecture are: + +* Pre-normalization: Instead of normalizing the output of each transformer sub-layer, GPT-3 normalizes the input of each sub-layer using the RMSNorm normalizing function, introduced by Zhang and Sennrich (2019). +* SwiGLU activation function: GPT-3 replaces the ReLU non-linearity with the SwiGLU activation function, introduced by Shazeer (2020), to improve performance. +* Rotary Embeddings: GPT-3 removes absolute positional embeddings and instead adds rotary embeddings, which were introduced in GPT-Neo. + +These modifications were found to improve the performance of the model.",0.9333333333333333,0.6450537488543274,0.249999999975 +simple-rag-llama-3-8b,df723477-9606-4a2f-bdec-e837120b055a,How many stages of training are in the GPT model?," + +According to the provided context, the GPT models are trained in two stages: + +1. The first stage involves training the model to predict the next word using a large dataset of text from the Internet. +2. The second stage involves fine-tuning the model with additional data using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers. + +These two stages are mentioned in the text as follows: + +""GPT models are often trained in two stages. First, they are trained, using a large dataset of text from the Internet, to predict the next word. The models are then fine-tuned with additional data, using an algorithm called reinforcement learning from human feedback (RLHF), to produce outputs that are preferred by human labelers.""",1.0,0.6703254856570627,0.9999999999 +simple-rag-llama-3-8b,1cf7a927-bde7-4ddc-9877-72ef0b9be676,What tokenizer is used in the Gemma2 model?," + +According to the provided context, the tokenizer used in the Gemma2 model is the SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings (Kudo and Richardson, 2018). This is the same tokenizer used in Gemma 1 and Gemini. The resulting vocabulary has 256k entries.",0.875,1.0,0.49999999995 +simple-rag-llama-3-8b,b0238136-2bd2-4433-a122-078f56829c0a,On what architecture the Gemma model is based on?," + +According to the provided context, the Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017).",1.0,0.980834227715684,0.9999999999 +simple-rag-llama-3-8b,125a2e82-39ea-48c5-9305-36429ddd2ceb,Does Claude 3 models have vision capabilities?," + +According to the provided context, yes, Claude 3 models have vision capabilities. In fact, it is mentioned that Claude 3 models can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics. Additionally, the models can also process images and convert them into text, as demonstrated in Figure 25, where a low-quality photo with hard-to-read handwriting is converted into text.",1.0,0.9942643083073888,0.8333333332916666 +simple-rag-llama-3-8b,365ff399-2b4b-4040-93b8-ce8f3c3ee2bb,What are the two tasks in BERT?," + +According to the provided context, the two tasks in BERT are: + +1. **Pre-training**: During pre-training, the model is trained on unlabeled data over different pre-training tasks. The pre-training procedure largely follows the existing literature on language model pre-training, using the BooksCorpus (800M words) and English Wikipedia (2,500M words) as the pre-training corpus. +2. **Fine-tuning**: Fine-tuning is the process of initializing the pre-trained model with the pre-trained parameters and then fine-tuning all the parameters using labeled data from the downstream tasks. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters.",1.0,0.9184199769082732,0.9999999999 +simple-rag-claude-3.5-sonnet,09d6f73f-afd4-499b-898d-afa2e2dcc5c2,How can attention be described in the Transformer?,"Based on the provided context, attention in the Transformer model can be described in several key ways: + +1. Multi-Head Attention: The Transformer uses multi-head attention, which allows the model to jointly attend to information from different representation subspaces at different positions. This is calculated as: + + MultiHead(Q, K, V) = Concat(head1, ..., headh)W^O + + Where each head is an attention function: + headi = Attention(QW^Q_i, KW^K_i, VW^V_i) + +2. Three Types of Attention: + a) Encoder-Decoder Attention: Queries come from the previous decoder layer, while keys and values come from the encoder output. This allows each position in the decoder to attend to all positions in the input sequence. + + b) Encoder Self-Attention: All keys, values, and queries come from the output of the previous encoder layer. Each position in the encoder can attend to all positions in the previous layer. + + c) Decoder Self-Attention: Similar to encoder self-attention, but each position in the decoder can only attend to earlier positions in the decoder to prevent leftward information flow. + +3. Parallelization: Unlike recurrent models, the Transformer's attention mechanism allows for significant parallelization, as it doesn't rely on sequential computation. + +4. Global Dependencies: The attention mechanism in the Transformer allows it to model dependencies between input and output without regard to their distance in the sequence. + +5. Self-Attention: The Transformer heavily relies on self-attention, which relates different positions of a single sequence to compute a representation of the sequence. + +6. Constant Computational Cost: The number of operations required to relate signals from two arbitrary input or output positions is reduced to a constant, unlike some other models where it grows with distance. + +In summary, attention in the Transformer is a versatile and powerful mechanism that enables the model to process input sequences in parallel, capture long-range dependencies efficiently, and generate output by focusing on relevant parts of the input at each step.",0.8333333333333334,0.7616315551800569,0.9999999999 +simple-rag-claude-3.5-sonnet,52dddd6a-ba45-4c71-8c76-d6d6494db1b8,What is Mixture of Agents?,"Mixture of Agents (MoA) is a methodology that leverages multiple Large Language Models (LLMs) to iteratively enhance the quality of generated responses. Here are the key points about MoA: + +1. Structure: MoA uses a layered architecture where each layer consists of multiple LLM agents. The structure is illustrated in Figure 2 of the context. + +2. Process: + - Agents in the first layer independently generate responses to a given prompt. + - These responses are then presented to agents in the next layer for further refinement. + - This iterative refinement process continues for several cycles. + +3. Purpose: The goal is to produce a more robust and comprehensive response by combining the strengths of multiple models. + +4. Roles of LLMs: + - Proposers: Generate useful reference responses for other models. + - Aggregators: Synthesize responses from other models into a high-quality output. + +5. Performance: MoA has achieved state-of-the-art performance on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK, even surpassing GPT-4 Omni in some aspects. + +6. Advantages: MoA significantly outperforms individual LLMs and simple LLM-based rankers. It shows improvements in various aspects such as robustness, correctness, efficiency, factuality, and insightfulness. + +The key insight behind MoA is leveraging the collaborativeness of LLMs - their ability to improve their responses when provided with outputs from other models. This approach allows for harnessing the collective expertise of multiple LLMs to produce higher quality outputs.",0.9090909090909091,0.7036913399983197,0.999999999975 +simple-rag-claude-3.5-sonnet,861f6f9d-1401-45ab-9d8d-67839ea69d57,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is indeed based on the idea of a mixture of experts. Specifically, Mixtral 8x7B is described as a Sparse Mixture of Experts (SMoE) language model. Here are the key points from the context that confirm this: + +1. The abstract states: ""We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model."" + +2. The introduction elaborates: ""Mixtral is a sparse mixture-of-experts network."" + +3. The model's architecture is explained as follows: ""It is a decoder-only model where the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the ""experts"") to process the token and combine their output additively."" + +4. Figure 1 in the paper visually illustrates the Mixture of Experts Layer, showing how each input vector is assigned to 2 of the 8 experts by a router. + +This mixture of experts approach allows Mixtral to have a large number of parameters (47B in total) while only using a subset (13B active parameters) for processing each token. This design enables the model to achieve high performance while maintaining efficiency in terms of computation and inference speed.",1.0,1.0,0.9999999999666667 +simple-rag-claude-3.5-sonnet,dd23f10e-b2b3-45c5-b489-542cc6207ca5,What is sliding window attention?,"Sliding Window Attention (SWA) is an attention mechanism used in Mistral 7B, a large language model, to efficiently handle longer sequences while reducing computational costs. Here's a breakdown of how SWA works: + +1. Limited attention span: In SWA, each token attends to a fixed number of tokens (W) from the previous layer, rather than attending to all tokens in the sequence. + +2. Moving window: The attention window ""slides"" along the sequence, allowing each token to focus on its nearby context. + +3. Layered information propagation: Although each layer has a limited window size, information can propagate further through the network's multiple layers. After k attention layers, information can move forward by up to k × W tokens. + +4. Efficient computation: SWA reduces the number of operations compared to vanilla attention, which is quadratic in sequence length. This leads to improved speed and memory efficiency. + +5. Extended reach: Despite the limited window size, tokens outside the immediate window can still influence predictions through the layered structure of the model. + +6. Practical benefits: For Mistral 7B, using a window size of 4096 allows for a theoretical attention span of approximately 131K tokens at the last layer. + +7. Performance improvement: The context mentions that for a sequence length of 16K and window size of 4096, SWA yields a 2x speed improvement over vanilla attention. + +By using SWA, Mistral 7B can effectively process longer sequences while maintaining efficiency, which is crucial for real-world applications of large language models.",0.875,0.56767737031783,0.7499999999625 +simple-rag-claude-3.5-sonnet,23e99460-ffdc-4f9d-b061-42d500382460,How many stages are there in the development of the Llama 3 model?,"Based on the provided context, there are two main stages in the development of the Llama 3 language models: + +1. Language model pre-training: This stage involves converting a large, multilingual text corpus into discrete tokens and pre-training a large language model on this data to perform next-token prediction. This stage helps the model learn the structure of language and acquire knowledge about the world from the text it processes. It includes: + - Standard pre-training on 15.6T tokens using a context window of 8K tokens + - Continued pre-training to increase the supported context window to 128K tokens + +2. Language model post-training: In this stage, the pre-trained model is aligned with human feedback through several rounds. This process involves: + - Supervised fine-tuning (SFT) + - Rejection sampling (RS) + - Direct preference optimization (DPO) + +These stages aim to transform the pre-trained model, which has a rich understanding of language, into an assistant that can follow instructions and behave appropriately. The post-training stage is designed to be more stable and scalable compared to complex reinforcement learning algorithms used in some other models.",0.75,0.8254964353972353,0.9999999999 +simple-rag-claude-3.5-sonnet,53c762ae-20cf-4da6-8e85-7c8d8a5068dc,What is the difference between the Llama 2 and Llama 2-Chat ?,"Based on the provided context, the key differences between Llama 2 and Llama 2-Chat are: + +1. Purpose: + - Llama 2 is a pretrained language model that can be adapted for various natural language generation tasks. + - Llama 2-Chat is a fine-tuned version of Llama 2 specifically optimized for dialogue use cases and assistant-like chat interactions. + +2. Training process: + - Llama 2 is the base model created through pretraining on a large corpus of publicly available data. + - Llama 2-Chat undergoes additional training steps after the initial pretraining: + a) Supervised fine-tuning (SFT) + b) Reinforcement Learning with Human Feedback (RLHF) + +3. Optimization: + - Llama 2-Chat is further refined to align with human preferences for helpfulness and safety in conversational contexts. + +4. Intended use: + - Llama 2 is more versatile and can be adapted for various natural language tasks. + - Llama 2-Chat is specifically designed for assistant-like chat applications. + +5. Safety considerations: + - Llama 2-Chat has undergone additional safety evaluations and optimizations to make it more suitable for direct interactions with users. + +Both Llama 2 and Llama 2-Chat are available in multiple sizes (7B, 13B, and 70B parameters), but Llama 2-Chat is the version that has been specifically tailored for conversational AI applications.",0.9,0.9714733580630797,0.9999999999 +simple-rag-claude-3.5-sonnet,4111b3a7-e225-4938-941e-d7e590290b2b,What is optimizer is used for LLaMA?,"Based on the provided context, the optimizer used for training LLaMA 2 (and by extension, likely for LLaMA in general) is AdamW. Specifically, the context states: + +""We trained using the AdamW optimizer (Loshchilov and Hutter, 2017), with β1 = 0.9, β2 = 0.95, eps = 10−5."" + +AdamW is a variant of the Adam optimizer that incorporates weight decay regularization. The hyperparameters used with AdamW for training LLaMA 2 are also provided: + +1. β1 (beta1) = 0.9 +2. β2 (beta2) = 0.95 +3. eps (epsilon) = 10^-5 + +Additionally, they mention using a cosine learning rate schedule with a warmup period of 2000 steps, weight decay of 0.1, and gradient clipping of 1.0. + +This information pertains specifically to LLaMA 2, but it's likely that similar optimization techniques were used for the original LLaMA model as well, given that the context mentions they adopted most of the pretraining settings from LLaMA 1.",0.6363636363636364,0.8544933964212964,0.3333333333 +simple-rag-claude-3.5-sonnet,04c7d61b-5407-4d73-96ee-246c7b5a0154,Can the GPT-4 model accept both text and image inputs?,"Yes, GPT-4 can accept both text and image inputs. According to the provided context: + +1. GPT-4 is described as a ""large multimodal model,"" which implies it can work with multiple types of input. + +2. The context explicitly states: ""GPT-4 accepts prompts consisting of both images and text."" + +3. It further elaborates that the model ""generates text outputs given inputs consisting of arbitrarily interlaced text and images."" + +4. The model's capabilities extend to various domains, including ""documents with text and photographs, diagrams, or screenshots."" + +5. GPT-4 is said to exhibit similar capabilities on visual inputs as it does on text-only inputs. + +This multimodal ability allows users to specify a wide range of vision or language tasks by combining text and images in their prompts. The model can then process this combined input and generate text outputs based on both the textual and visual information provided. + +It's worth noting that while the context mentions these visual capabilities, it also indicates that more detailed information about GPT-4's visual abilities will be released in follow-up work.",0.8666666666666667,0.9692922226109744,0.8333333332916666 +simple-rag-claude-3.5-sonnet,27c5cd28-04fb-4f26-8d89-c1a3be705527,On what architecture the GPT-3 model is based on?,"Based on the provided context, the GPT-3 model is based on the transformer architecture. Specifically, the context states: + +""We use the same model and architecture as GPT-2 [RWC+19], including the modified initialization, pre-normalization, and reversible tokenization described therein, with the exception that we use alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer [CGRS19]."" + +The transformer architecture, originally introduced by Vaswani et al. in 2017, is the foundation for GPT-3. However, GPT-3 incorporates several modifications and improvements to this base architecture: + +1. It uses the same modifications as GPT-2, including changes to initialization, pre-normalization, and tokenization. + +2. It employs alternating dense and locally banded sparse attention patterns in the transformer layers, similar to the Sparse Transformer. + +3. It uses pre-normalization, where the input of each transformer sub-layer is normalized instead of the output, to improve training stability. + +4. While not explicitly stated for GPT-3, the context mentions that later models in this line of research (like PaLM) incorporated further improvements such as the SwiGLU activation function and rotary embeddings. + +These modifications build upon the basic transformer architecture to create the large-scale language model that is GPT-3.",0.6111111111111112,0.951779266171675,0.499999999975 +simple-rag-claude-3.5-sonnet,89fa855b-65fd-46d8-83ff-1058b19e83cc,How many stages of training are in the GPT model?,"Based on the provided context, GPT models are typically trained in two stages: + +1. First stage: The models are trained on a large dataset of text from the Internet to predict the next word. This is often referred to as pre-training. + +2. Second stage: The models are then fine-tuned with additional data using an algorithm called reinforcement learning from human feedback (RLHF). In this stage, the model is trained to produce outputs that are preferred by human labelers. + +The context specifically states: ""GPT models are often trained in two stages."" This two-stage approach allows the model to first learn general language patterns and knowledge from a broad dataset, and then refine its outputs based on human preferences and specific tasks.",0.6666666666666666,0.7370863750719033,0.9999999999 +simple-rag-claude-3.5-sonnet,e43c0fa8-e6c0-4a78-ab1f-3f659da735b7,What tokenizer is used in the Gemma2 model?,"According to the context provided, the Gemma 2 model uses the same tokenizer as Gemma 1 and Gemini. Specifically, it uses a SentencePiece tokenizer with the following characteristics: + +1. Split digits +2. Preserved whitespace +3. Byte-level encodings + +The tokenizer is based on the SentencePiece algorithm developed by Kudo and Richardson in 2018. + +An important feature of this tokenizer is its large vocabulary size. The resulting vocabulary has 256,000 entries, which is designed to work with a large number of languages. This large vocabulary contributes to the higher number of embedding parameters in the Gemma models compared to models limited to one or a few languages. + +The use of this tokenizer allows Gemma 2 to effectively process and understand a wide range of text inputs, making it versatile for various language tasks.",0.6666666666666666,0.9805458999710387,0.49999999995 +simple-rag-claude-3.5-sonnet,bef93275-f3b1-482e-9204-19109275b35b,On what architecture the Gemma model is based on?,"Based on the provided context, the Gemma model architecture is based on the transformer decoder. Specifically, the context states: + +""The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017)."" + +The transformer architecture, introduced by Vaswani et al. in 2017, has become a fundamental building block for many modern language models. By using this architecture, Gemma leverages the powerful attention mechanisms and other features that have made transformers so successful in natural language processing tasks. + +The context also mentions some specific architectural details for Gemma, including: + +1. The use of RoPE (Rotary Position Embedding) for positional encoding. +2. An approximated version of the GeGLU activation function. +3. RMSNorm (Root Mean Square Layer Normalization) to stabilize training. + +These architectural choices contribute to Gemma's performance across various language understanding and generation tasks.",0.5714285714285714,0.9824451804350853,0.9999999999 +simple-rag-claude-3.5-sonnet,97e9688c-20b8-4200-bfe0-0e589b350f9f,Does Claude 3 models have vision capabilities?,"Yes, Claude 3 models do have vision capabilities. Based on the provided context, here are the key points about Claude 3's vision capabilities: + +1. The Claude 3 family includes multimodal input capabilities, allowing users to upload images (such as tables, graphs, and photos) along with text prompts. + +2. All models in the Claude 3 family, including Opus, Sonnet, and Haiku, have vision capabilities. + +3. These models can recognize and identify objects visually, and they can think in complex ways about visual information, such as understanding both an object's appearance and its connections to concepts like mathematics. + +4. The models can perform tasks like converting low-quality photos with hard-to-read handwriting into text and organizing information from images into structured formats like JSON. + +5. Claude 3 models can handle various image formats including JPEG, PNG, GIF, and WebP, with file size limits of up to 10MB and dimensions up to 8000x8000 pixels. + +However, it's important to note that while these vision capabilities are powerful, they do have some limitations: + +1. The models may sometimes generate inaccurate information and descriptions about images. +2. Performance can be lower for small or low-resolution images. +3. These capabilities should not be used for consequential use cases that require high precision and accuracy without human validation. + +Anthropic is actively working on improving Claude's performance in these areas.",0.9545454545454546,0.9942643083073888,0.5833333333041666 +simple-rag-claude-3.5-sonnet,5bfa9e21-294d-43ce-b4d6-654124458794,What are the two tasks in BERT?,"Based on the provided context, the two main tasks in BERT (Bidirectional Encoder Representations from Transformers) are: + +1. Pre-training: During this phase, the model is trained on unlabeled data using different pre-training tasks. The pre-training is done using a large corpus of text, including the BooksCorpus and English Wikipedia. + +2. Fine-tuning: In this phase, the BERT model is initialized with the pre-trained parameters, and then all parameters are fine-tuned using labeled data from specific downstream tasks. Each downstream task has its own separately fine-tuned model, even though they all start with the same pre-trained parameters. + +The context emphasizes that a distinctive feature of BERT is its unified architecture across different tasks, with minimal differences between the pre-trained architecture and the final downstream architecture. This allows BERT to be easily adapted to various natural language processing tasks by simply swapping out the appropriate inputs and outputs during the fine-tuning phase.",0.9333333333333333,0.8890085053043496,0.3333333333 +simple-rag-claude-3-sonnet,2a29c9fe-b5d6-427d-bb6c-d0d7cf11f2e4,How can attention be described in the Transformer?,"In the Transformer model, attention is used in several key ways: + +1. Multi-Head Attention: The core attention mechanism used is multi-head attention. This allows the model to jointly attend to information from different representation subspaces at different positions. Specifically, the queries (Q), keys (K), and values (V) are projected using different linear transformations to create multiple ""heads"". The attention is calculated in parallel across these heads and then concatenated. + +2. Encoder Self-Attention: In the encoder, self-attention layers allow each position to attend to all other positions in the input sequence. This allows capturing long-range dependencies in the input. + +3. Decoder Self-Attention: In the decoder, self-attention is also used within the decoder stack, but in a masked way. Each position is allowed to only attend to earlier positions in the output sequence. This prevents illegal peeking at future output positions. + +4. Encoder-Decoder Attention: The decoder additionally has an attention layer that attends to the outputs from the final encoder layer. This allows focusing the generation on relevant parts of the input sequence. + +The key innovation is doing away with recurrence entirely and instead relying on attention to draw relationships between elements regardless of their position. This enables much more parallelization than recurrent models. The multi-head attention mechanism allows attending to information from different representation subspaces jointly.",0.8,0.781363542893511,0.7499999999625 +simple-rag-claude-3-sonnet,2473dfb9-ecec-48ba-957b-eade925ab918,What is Mixture of Agents?,"Mixture-of-Agents (MoA) is a methodology proposed in this paper that leverages the collective strengths of multiple large language models (LLMs) to generate enhanced responses. + +The key idea is to construct a layered architecture where each layer comprises multiple LLM agents. The agents in the first layer independently generate initial responses to a given prompt. Then, the agents in the next layer take all the outputs from the previous layer as auxiliary information to refine and improve upon those initial responses. This iterative process continues through multiple layers, with each subsequent layer synthesizing and enhancing the responses from the previous layer. + +The MoA approach aims to exploit the collaborativeness phenomenon observed in LLMs - that their response quality can improve when provided with outputs from other models as additional context. By combining the diverse perspectives and strengths of multiple LLMs through this layered refinement process, MoA can produce more robust, comprehensive and higher-quality responses than what a single model could achieve alone. + +The paper demonstrates that MoA models using this methodology achieve state-of-the-art performance on several language model evaluation benchmarks like AlpacaEval 2.0, MT-Bench and FLASK, even outperforming models like GPT-4 in certain metrics. The iterative refinement process leveraging multiple agents allows MoA to push the boundaries of what is possible with current LLMs.",0.8620689655172413,0.6236265068677259,0.8055555555287036 +simple-rag-claude-3-sonnet,ad4548c1-1aca-496d-ade1-b37aab6a4dd0,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is based on the idea of a Sparse Mixture of Experts (SMoE) architecture. The key points about Mixtral's architecture from the provided context are: + +- Mixtral has the same architecture as the previous Mistral 7B model, but each layer is composed of 8 feedforward blocks called ""experts"". + +- For every token at each layer, a router network selects two out of the 8 experts to process the current state and combine their outputs. + +- Even though each token only sees two experts, the selected experts can be different at each time step. + +- As a result, each token has access to 47B parameters in total, but only uses 13B active parameters during inference. + +- This mixture of experts approach increases the model's parameter count while controlling computational cost and latency, as only a subset of parameters is used per token. + +So in summary, Mixtral leverages the mixture of experts idea, where different subsets of model parameters (experts) process each input token, allowing for an increased overall parameter count while keeping computational requirements manageable. This sparse mixture of experts (SMoE) architecture is a key design principle behind Mixtral.",1.0,0.7094256786034553,0.9999999999666667 +simple-rag-claude-3-sonnet,72743bfe-111f-4f58-a0b2-af37ca1ccd8e,What is sliding window attention?,"Sliding window attention (SWA) is an attention mechanism used in large language models like Mistral 7B that helps handle longer input sequences more efficiently. Here's how it works: + +In a standard transformer architecture, each token attends to all other tokens in the sequence during the self-attention calculation. This becomes computationally expensive for very long sequences. + +With sliding window attention, each token only attends to a window of W recent tokens from the previous layer, rather than the entire sequence. For example, if W=4096, a token at position i only attends to tokens between positions i-4096 and i from the previous layer. + +By stacking multiple attention layers, information can still flow across the entire sequence, but in a more efficient way. After k layers, a token can integrate information from up to k*W tokens away, without having to attend to the full sequence length at every layer. + +So SWA allows capturing long-range dependencies in a computationally efficient manner by limiting the receptive field at each layer, while stacking layers to expand the total receptive field. This makes it possible to handle very long sequences of tens of thousands of tokens on models like Mistral 7B.",0.7222222222222222,0.596707451787069,0.9999999999 +simple-rag-claude-3-sonnet,2aeec2ee-0cd8-4b39-a661-371c6b5a7601,How many stages are there in the development of the Llama 3 model?,"According to the context provided, the development of the Llama 3 language models comprises two main stages: + +1. Language model pre-training: This involves converting a large multilingual text corpus into tokens and pre-training a large language model on this data to perform next-token prediction. This stage allows the model to learn the structure of language and acquire knowledge from the text data. + +2. Language model post-training: After the initial pre-training, the model is further aligned with human feedback through several rounds of supervised fine-tuning on instructional data. This helps the model learn to follow instructions and behave like an AI assistant. + +So in summary, there are two key stages in developing the Llama 3 models - an initial large-scale pre-training stage on text data, followed by a post-training stage involving supervised fine-tuning to make the model behave in a more instructional and assistant-like manner.",1.0,0.8075911450963744,0.9999999999 +simple-rag-claude-3-sonnet,543a1e07-7a07-4bd2-ac9f-3eb8fd262510,What is the difference between the Llama 2 and Llama 2-Chat ?,"Based on the provided context, the key difference between Llama 2 and Llama 2-Chat is: + +Llama 2 is the base pretrained language model, trained on a large corpus of publicly available online data using an optimized autoregressive transformer architecture. It comes in different parameter sizes like 7B, 13B, 70B etc. + +Llama 2-Chat is a fine-tuned version of Llama 2, specifically optimized for dialogue/conversational use cases. The fine-tuning process involved: + +1) Initial supervised fine-tuning on a dataset to create an initial Llama 2-Chat version. + +2) Further iterative refinement using Reinforcement Learning with Human Feedback (RLHF) techniques like rejection sampling and Proximal Policy Optimization (PPO). + +So in summary, Llama 2 is the base pretrained model, while Llama 2-Chat is a tuned version of it tailored for open-ended dialogue through supervised fine-tuning and reinforcement learning techniques using human feedback. The context mentions Llama 2-Chat models in 7B, 13B, 70B parameter sizes corresponding to the base Llama 2 sizes.",0.9473684210526315,0.9773636296964231,0.8333333332916666 +simple-rag-claude-3-sonnet,7c4fb0f2-dacd-4e66-be2f-03b67ea3bff4,What is optimizer is used for LLaMA?,"According to the provided context, the LLaMA (Llama 2) model was trained using the AdamW optimizer with the following hyperparameters: + +- β1 = 0.9 +- β2 = 0.95 +- eps = 10^-5 +- Cosine learning rate schedule with 2000 step warmup +- Weight decay of 0.1 +- Gradient clipping of 1.0 + +The context specifically mentions: ""We trained using the AdamW optimizer (Loshchilov and Hutter, 2017), with β1 = 0.9, β2 = 0.95, eps = 10−5. We use a cosine learning rate schedule, with warmup of 2000 steps, and decay final learning rate down to 10% of the peak learning rate. We use a weight decay of 0.1 and gradient clipping of 1.0."" + +So in summary, the AdamW variant of the Adam optimizer was used with the listed hyperparameter settings for training the LLaMA (Llama 2) large language model.",0.6666666666666666,0.7766249527198191,0.3333333333 +simple-rag-claude-3-sonnet,40d0dce0-ca24-4eaf-8e39-2db0213e0fe4,Can the GPT-4 model accept both text and image inputs?,"Yes, according to the provided context, GPT-4 can accept inputs consisting of both text and images. The context states: + +""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task. Specifically, the model generates text outputs given inputs consisting of arbitrarily interlaced text and images. Over a range of domains—including documents with text and photographs, diagrams, or screenshots—GPT-4 exhibits similar capabilities as it does on text-only inputs."" + +So GPT-4 has multimodal capabilities, allowing it to process and generate outputs based on inputs containing both textual and visual information like images, diagrams, or screenshots interlaced with text. This multimodal input ability expands GPT-4's capabilities beyond just processing text, enabling it to handle tasks involving both language and vision modalities.",0.6666666666666666,0.9549543063545066,0.8333333332916666 +simple-rag-claude-3-sonnet,0fe9c435-7113-4b81-9eb0-bdf7afc8b5ab,On what architecture the GPT-3 model is based on?,"According to the provided context, the GPT-3 model is based on the transformer architecture, with several improvements and modifications: + +1. It uses pre-normalization, where the input to each transformer sub-layer is normalized instead of normalizing the output. This improves training stability. + +2. It uses the SwiGLU activation function instead of ReLU, which was introduced in the PaLM model to improve performance. + +3. It uses rotary embeddings instead of absolute positional embeddings. + +4. It uses alternating dense and locally banded sparse attention patterns in the transformer layers, similar to the Sparse Transformer architecture. + +5. It follows the same model architecture as GPT-2, including the modified initialization, pre-normalization, and reversible tokenization described in the GPT-2 paper. + +So in summary, while being based on the core transformer architecture, GPT-3 incorporates several architectural improvements and modifications from other large language models like PaLM, GPT-Neo, and the Sparse Transformer. The transformer architecture with these enhancements allows GPT-3 to achieve strong performance on various NLP tasks at a very large scale of 175 billion parameters.",0.5217391304347826,0.682846808318871,0.499999999975 +simple-rag-claude-3-sonnet,385d49d2-5dc4-4af9-a6e2-050417dd21d2,How many stages of training are in the GPT model?,"According to the provided context, GPT models like GPT-4 are often trained in two main stages: + +1. First stage: The model is trained on a large dataset of text from the internet to predict the next word in a sequence. This allows the model to learn patterns and gain general language understanding from the massive text data. + +2. Second stage: The model is then fine-tuned using an algorithm called reinforcement learning from human feedback (RLHF). In this stage, additional data is used to train the model to produce outputs that are preferred by human labelers or raters. This helps make the model's outputs more aligned with human preferences. + +So in summary, there are two key training stages for GPT models: +1) Pretraining on large text data to build general language abilities +2) Fine-tuning with human feedback to make outputs more aligned with human preferences + +This two-stage training process involving both unsupervised pretraining on text data and supervised fine-tuning with human feedback is what allows GPT models to achieve their strong language generation capabilities.",0.4615384615384616,0.8313300675648327,0.9999999999 +simple-rag-claude-3-sonnet,e29e7dd1-e8a5-4f7a-8b30-4ab3e197dda3,What tokenizer is used in the Gemma2 model?,"According to the context provided, the Gemma 2 models use the same tokenizer as the previous Gemma 1 and Gemini models. Specifically, it uses a SentencePiece tokenizer with the following properties: + +- Split digits +- Preserved whitespace +- Byte-level encodings + +The resulting vocabulary has 256k entries. + +The context states: + +""We use the same tokenizer as Gemma 1 and Gemini: a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings (Kudo and Richardson, 2018). The resulting vocabulary has 256k entries."" + +So in summary, the Gemma 2 model uses a SentencePiece tokenizer with a 256k vocabulary, inheriting the tokenizer from the previous Gemma and Gemini models.",1.0,0.9805458999710387,0.49999999995 +simple-rag-claude-3-sonnet,b85a15cb-c3f3-411e-9764-016dd32de8b6,On what architecture the Gemma model is based on?,"According to the context provided, the Gemma model architecture is based on the transformer decoder architecture (Vaswani et al., 2017). Specifically, it states: + +""The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017)."" + +The transformer decoder is a type of neural network architecture that was introduced in the original Transformer paper by Vaswani et al. in 2017. It is commonly used in large language models for text generation tasks, where the model generates text output in an autoregressive manner, predicting one token at a time based on the previous tokens.",0.375,0.981908196195285,0.9999999999 +simple-rag-claude-3-sonnet,c1d62ac0-9bb9-4c4b-abad-a70b9592eed6,Does Claude 3 models have vision capabilities?,"Yes, according to the provided context, the Claude 3 models do have vision capabilities. Specifically, it mentions that ""A key enhancement in the Claude 3 family is multimodal input capabilities with text output, allowing users to upload images (e.g., tables, graphs, photos) along with text prompts for richer context and expanded use cases."" + +It further states that ""Claude 3 models can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object's appearance and its connections to concepts like mathematics."" + +Additionally, it notes that ""Claude 3 Haiku is the fastest and most affordable option on the market for its intelligence category, while also including vision capabilities."" + +So in summary, yes the context clearly indicates that the Claude 3 models have vision capabilities that allow them to process and understand images in addition to text inputs.",1.0,0.9830251010857803,0.3333333333 +simple-rag-claude-3-sonnet,e7f5d7d0-0a11-4389-985e-83ddf760721f,What are the two tasks in BERT?,"According to the provided context, BERT has two main tasks: + +1. Pre-training: In this step, the BERT model is trained on a large corpus of unlabeled text data over different pre-training tasks like Masked Language Modeling and Next Sentence Prediction. + +2. Fine-tuning: After the pre-training step, the BERT model parameters are used to initialize models for various downstream tasks. The pre-trained BERT model is then fine-tuned on labeled data from the specific downstream task, with all parameters being updated during this fine-tuning process. Each downstream task has its own separate fine-tuned model, even though they start from the same pre-trained parameters. + +So in summary, the two key tasks are pre-training on unlabeled data to learn general language representations, followed by fine-tuning on a specific labeled dataset for the target downstream task. The pre-training and fine-tuning approach allows BERT to effectively transfer its knowledge to different natural language understanding tasks.",0.8666666666666667,0.89357426574327,0.8333333332916666 +simple-rag-claude-3-opus,911ae964-f85a-4663-bb9f-22dbc7afd16c,How can attention be described in the Transformer?,"Based on the provided context, attention in the Transformer model can be described in the following ways: + +1. The Transformer uses multi-head attention in three different ways: + - In ""encoder-decoder attention"" layers, where the queries come from the previous decoder layer, and the keys and values come from the encoder output. This allows the decoder to attend to all positions in the input sequence. + - In self-attention layers in the encoder, where all keys, values and queries come from the output of the previous encoder layer. This allows each position in the encoder to attend to all positions in the previous layer. + - In self-attention layers in the decoder, which allow each decoder position to attend to all positions up to and including that position. + +2. Self-attention relates different positions of a single sequence to compute a representation of that sequence. It has been used successfully in various tasks like reading comprehension, summarization, entailment, etc. + +3. The Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output, without using sequence-aligned RNNs or convolution. + +4. Attention allows modeling dependencies regardless of their distance in the input or output sequences. + +So in summary, the Transformer extensively uses self-attention and multi-head attention mechanisms to draw global dependencies between input and output, enabling more parallelization compared to recurrent models.",0.9,0.7699838650237495,0.7499999999625 +simple-rag-claude-3-opus,a23ebbef-394f-4c61-be32-8d335eddc10e,What is Mixture of Agents?,"Based on the provided context, Mixture-of-Agents (MoA) is a methodology that leverages multiple large language models (LLMs) to iteratively enhance the quality of generated responses. + +The key aspects of MoA are: + +1. It uses a layered architecture, where each layer contains multiple LLM agents. + +2. The agents in the first layer independently generate responses to a given prompt. + +3. The responses from the previous layer are then provided as auxiliary information to the agents in the next layer to refine and improve upon. + +4. This iterative refinement process continues for several cycles across the layers until a more robust and comprehensive final response is obtained. + +5. The LLMs in MoA can play two key roles - Proposers that are good at generating useful reference responses for other models to build on, and Aggregators that are skilled at synthesizing the responses from other models into a single high-quality output. + +So in summary, MoA aims to harness the collective strengths and expertise of multiple LLMs in a collaborative manner to boost the overall performance and capabilities compared to using any single LLM independently. The layered architecture enables iterative refinement of the generated responses.",0.95,0.6236265068677259,0.8055555555287036 +simple-rag-claude-3-opus,51e78c28-549a-48b1-bd2d-491d4a6f49dd,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is based on the idea of a mixture of experts (MoE). The key points from the context are: + +- Mixtral is a sparse mixture of experts (SMoE) language model. It has the same architecture as Mistral 7B, but each layer is composed of 8 feedforward blocks (experts). + +- For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. + +- Even though each token only sees two experts, the selected experts can be different at each timestep. This allows each token to access 47B parameters in total, while only using 13B active parameters during inference. + +So in summary, Mixtral leverages the mixture of experts approach, where a subset of expert modules are dynamically selected to process each token. This enables scaling up the model capacity while keeping computation efficient.",0.875,0.8855289025127228,0.9999999999666667 +simple-rag-claude-3-opus,d1ecef7a-cd9b-48f8-99e4-9c537851ad09,What is sliding window attention?,"Sliding Window Attention (SWA) is a technique used in the Mistral 7B model to efficiently handle long input sequences while reducing computational costs. In SWA, each token in a given layer can attend to a limited number of tokens (denoted as W) from the previous layer, instead of attending to all tokens in the sequence. + +For example, with a window size of W=3, a token at position i in layer k can only attend to tokens at positions i-3 to i in the previous layer k-1. This allows information to propagate forward through the layers, so a token in the final layer can still be influenced by tokens beyond the window size from the input sequence. + +The key benefits of SWA are: + +1. It reduces the computational complexity, as the attention operations are limited to the window size rather than the entire sequence length. + +2. It enables the model to handle longer sequences more efficiently by reducing memory requirements during decoding. + +3. Despite the limited attention span in each layer, information can still flow across the entire sequence as it moves through the stacked layers of the transformer. In Mistral 7B, with 32 layers and a window size of 4096, the theoretical attention span at the last layer is approximately 131K tokens.",0.7777777777777778,0.5980433477267649,0.9999999999 +simple-rag-claude-3-opus,902f4126-2436-4712-950b-90fcadce521a,How many stages are there in the development of the Llama 3 model?,"According to the provided context, the development of the Llama 3 language models comprises two main stages: + +1. Language model pre-training: The model is pre-trained on a large, multilingual text corpus to learn the structure of language and obtain knowledge about the world. This is done at a massive scale, with the largest model having 405B parameters and being pre-trained on 15.6T tokens. + +2. Language model post-training: After pre-training, the model is aligned with human feedback through several rounds of supervised fine-tuning, rejection sampling, and direct preference optimization. This stage helps the model follow instructions and behave like an assistant. + +So in summary, there are two key stages - pre-training to learn language and knowledge, followed by post-training to align the model to be helpful and safe.",0.9411764705882353,0.8209282447089518,0.9999999999 +simple-rag-claude-3-opus,9a2686a4-15e6-4d8f-a13a-1895dc143111,What is the difference between the Llama 2 and Llama 2-Chat ?,"Based on the provided context, the main differences between Llama 2 and Llama 2-Chat are: + +1. Training: Llama 2 is a pretrained language model, trained on a new mix of publicly available data with 40% more tokens, doubled context length, and grouped-query attention compared to Llama 1. In contrast, Llama 2-Chat is a fine-tuned version of Llama 2 that has been further optimized for dialogue use cases through supervised fine-tuning and reinforcement learning with human feedback (RLHF). + +2. Intended Use: Llama 2 pretrained models can be adapted for a variety of natural language generation tasks, while Llama 2-Chat tuned models are specifically intended for assistant-like chat interactions. + +3. Safety: Llama 2-Chat models have undergone additional safety evaluations and improvements through the RLHF process to align them with human preferences for helpfulness and safety in conversational settings. + +In summary, Llama 2 serves as the base pretrained model, while Llama 2-Chat is a specialized version fine-tuned and optimized for safe and engaging conversational AI applications.",0.95,0.974208066262647,0.8333333332916666 +simple-rag-claude-3-opus,09a816b0-1b93-46e3-b084-c19dbc7b9f2b,What is optimizer is used for LLaMA?,"Based on the provided context, Llama 2 uses the AdamW optimizer for training. Specifically, the context states: + +""We trained using the AdamW optimizer (Loshchilov and Hutter, 2017), with β1 = 0.9, β2 = 0.95, eps = 10−5. We use a cosine learning rate schedule, with warmup of 2000 steps, and decay final learning rate down to 10% of the peak learning rate. We use a weight decay of 0.1 and gradient clipping of 1.0."" + +So in summary, the AdamW optimizer is used to train Llama 2, with specific hyperparameters detailed for the learning rate schedule, weight decay, and gradient clipping.",1.0,0.7935489579862396,0.3333333333 +simple-rag-claude-3-opus,a60d4006-711c-465b-b7b4-ff4f1fe18674,Can the GPT-4 model accept both text and image inputs?,"Yes, according to the provided context, GPT-4 can accept prompts consisting of both images and text. Specifically, the model can generate text outputs given inputs that arbitrarily combine text and images. + +The context states: + +""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task. Specifically, the model generates text outputs given inputs consisting of arbitrarily interlaced text and images."" + +It further mentions that over various domains, including ""documents with text and photographs, diagrams, or screenshots,"" GPT-4 demonstrates similar capabilities as it does with text-only inputs.",1.0,0.8449986059565022,0.8333333332916666 +simple-rag-claude-3-opus,f3e5805f-fe4b-4847-8c41-5a7ee6dc2a39,On what architecture the GPT-3 model is based on?,"Based on the provided context, the GPT-3 model architecture is based on the transformer architecture originally proposed by Vaswani et al. in 2017. The context mentions that GPT-3 uses the same model and architecture as GPT-2, with a few modifications: + +- It uses alternating dense and locally banded sparse attention patterns in the transformer layers, similar to the Sparse Transformer. + +- It incorporates some architectural improvements used in other large language models, such as: + - Pre-normalization of the input to each transformer sub-layer (instead of normalizing the output), using the RMSNorm function, to improve training stability. This technique was used in GPT-3. + - Replacing the ReLU activation function with the SwiGLU activation, which was introduced in the PaLM model to improve performance. + - Using rotary position embeddings instead of absolute positional embeddings, a technique used in GPT-Neo. + +So in summary, while building upon the core transformer architecture, GPT-3 incorporates several architectural modifications and techniques used in other transformer-based large language models to improve its performance and training stability.",0.3684210526315789,0.9811289221468537,0.249999999975 +simple-rag-claude-3-opus,2a855f0d-79b5-4b33-9b56-ba425d4bf379,How many stages of training are in the GPT model?,"According to the provided context, GPT models are often trained in two stages: + +1. First, they are trained on a large dataset of text from the Internet to predict the next word. + +2. Then, the models are fine-tuned with additional data, using an algorithm called reinforcement learning from human feedback (RLHF), to produce outputs that are preferred by human labelers. + +So in summary, the two main stages of training for GPT models are: + +1. Pre-training on large text datasets +2. Fine-tuning using RLHF to align outputs with human preferences",1.0,0.8625917778433476,0.9999999999 +simple-rag-claude-3-opus,39f47452-f03c-43e8-8405-5d833240f932,What tokenizer is used in the Gemma2 model?,"Based on the provided context, the Gemma 2 models use the same tokenizer as Gemma 1 and Gemini: a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings. The resulting vocabulary has 256k entries.",1.0,0.9668872845590216,0.49999999995 +simple-rag-claude-3-opus,3e670007-67e7-4b04-8d06-b3f95a52f5be,On what architecture the Gemma model is based on?,"Based on the provided context, the Gemma model architecture is based on the transformer decoder architecture, as described in this excerpt: + +""The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017)."" + +The transformer decoder was originally introduced in the influential ""Attention Is All You Need"" paper by Vaswani et al. in 2017. This architecture has become the foundation for many state-of-the-art language models in recent years.",0.4285714285714285,0.980834227715684,0.9999999999 +simple-rag-claude-3-opus,22aa7319-4996-493b-b76d-1f405ce6e6c0,Does Claude 3 models have vision capabilities?,"Yes, the Claude 3 models have vision capabilities. According to the provided context: + +""A key enhancement in the Claude 3 family is multimodal input capabilities with text output, allowing users to upload images (e.g., tables, graphs, photos) along with text prompts for richer context and expanded use cases."" + +The context also mentions that the Claude 3 models can recognize and identify objects visually, and think in complex ways about an object's appearance and its connections to concepts. + +However, it notes that the models can sometimes generate inaccurate information and descriptions about images, so they should not be used for consequential use cases requiring high precision without human validation. Performance may also be lower for small or low resolution images.",1.0,0.9830251010857803,0.3333333333 +simple-rag-claude-3-opus,e9cbb7fd-1f02-4e8d-8acd-c79518802347,What are the two tasks in BERT?,"Based on the provided context, the two main tasks in BERT are: + +1. Pre-training: During pre-training, the BERT model is trained on unlabeled data over different pre-training tasks. The two pre-training tasks mentioned are: + - Masked Language Model (Mask LM): Some of the input tokens are masked and the model tries to predict the original vocabulary ID of the masked word based on its context. + - Next Sentence Prediction (NSP): The model receives pairs of sentences as input and learns to predict if the second sentence in the pair is the subsequent sentence in the original document. + +2. Fine-tuning: After pre-training, the BERT model is initialized with the pre-trained parameters and all of the parameters are fine-tuned using labeled data from downstream tasks. Each downstream task has separate fine-tuned models, even though they are initialized with the same pre-trained parameters. + +So in summary, the two main tasks are the unsupervised pre-training tasks (Mask LM and NSP) followed by supervised fine-tuning of the model for specific downstream applications.",0.5263157894736842,0.9679117087732756,0.4166666666458333 +simple-rag-claude-3-haiku,f8345de2-7f9d-413e-8eec-ca7318e085e5,How can attention be described in the Transformer?,"The Transformer model uses multi-head attention in three different ways: + +1. Encoder-Decoder Attention: + - In the encoder-decoder attention layers, the queries come from the previous decoder layer. + - The memory keys and values come from the output of the encoder. + - This allows every position in the decoder to attend over all positions in the input sequence. + - This is similar to the typical encoder-decoder attention mechanisms in sequence-to-sequence models. + +2. Encoder Self-Attention: + - The encoder contains self-attention layers. + - In these self-attention layers, all of the keys, values, and queries come from the same place - the output of the previous encoder layer. + - Each position in the encoder can attend to all positions in the previous layer of the encoder. + +3. Decoder Self-Attention: + - The self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. + - This prevents leftward information flow, ensuring the predictions for position i can depend only on the known outputs at positions less than i. + +The Transformer uses multi-head attention, where it employs 8 parallel attention layers or ""heads"". This allows the model to jointly attend to information from different representation subspaces at different positions. The reduced dimension of each head (64) keeps the total computational cost similar to that of single-head attention with full dimensionality.",0.8666666666666667,0.6855380198678489,0.7499999999625 +simple-rag-claude-3-haiku,dcde9a32-4186-46fb-a2e7-15b612bc7e99,What is Mixture of Agents?,"Mixture of Agents (MoA) is a methodology introduced in this paper to leverage the collective expertise of multiple large language models (LLMs) to enhance their capabilities. The key ideas are: + +1. Collaborativeness of LLMs: The paper shows that LLMs can improve their responses by referencing outputs from other models, even if the auxiliary responses are of lower quality. This indicates a collaborative capability among LLMs. + +2. MoA Architecture: MoA constructs a layered architecture where each layer consists of multiple LLM ""agents"". Each agent in a layer takes the outputs from all agents in the previous layer as auxiliary information to generate its own response. This iterative refinement process continues across multiple layers. + +3. Roles of Agents: The paper categorizes the LLM agents into two roles: + - Proposers: LLMs that excel at generating useful reference responses for other models to leverage. + - Aggregators: LLMs that are proficient at synthesizing responses from other models into a single high-quality output. + +4. Performance Improvements: Experiments show that the MoA methodology significantly outperforms using a single LLM or an LLM-based ranker that selects from the proposer outputs. MoA achieves state-of-the-art results on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK, surpassing even GPT-4 Omni. + +In summary, Mixture of Agents is a novel approach that harnesses the collective strengths of multiple LLMs through an iterative refinement process, leading to substantial improvements in language model capabilities.",1.0,0.6027928736139344,0.8055555555287036 +simple-rag-claude-3-haiku,7d502eea-3258-4a6e-a997-0e7943646358,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is based on the idea of a Mixture of Experts (MoE) architecture. The key points are: + +1. Mixtral has a Sparse Mixture of Experts (SMoE) architecture, where each layer is composed of 8 feedforward blocks (experts). + +2. For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. This means that each token only uses a subset of the total 47B parameters, specifically 13B active parameters during inference. + +3. The selection of the two experts to use for each token is dynamic, meaning the selected experts can be different at each timestep. This allows the model to adapt the computation to the specific input. + +4. This sparse MoE approach allows Mixtral to outperform or match larger models like Llama 2 70B and GPT-3.5 across various benchmarks, especially in areas like mathematics, code generation, and multilingual tasks. + +In summary, the core innovation of Mixtral is its Sparse Mixture of Experts architecture, which selectively activates a subset of the model's parameters for each input token, leading to improved performance and efficiency compared to traditional monolithic language models.",0.7894736842105263,0.5558841842085167,0.9999999999666667 +simple-rag-claude-3-haiku,af8556ae-6673-4f48-9195-c549c6e5c30d,What is sliding window attention?,"Sliding Window Attention (SWA) is a technique used in the Mistral 7B language model to improve the efficiency and performance of the attention mechanism. Here's a breakdown of how it works: + +1. Attention Span Limitation: In a standard transformer model, the attention mechanism allows each token to attend to all other tokens in the input sequence. This results in quadratic computational complexity and memory usage, which can be a bottleneck for long input sequences. + +2. Sliding Window Approach: To address this issue, SWA introduces a sliding window approach. Instead of attending to all tokens, each token in the current layer can only attend to a fixed-size window of tokens from the previous layer. This window size is denoted as W. + +3. Recursive Attention: By stacking multiple attention layers, the effective attention span can be increased. Specifically, after k attention layers, each token can attend to tokens within a window of size k * W. This allows the model to capture long-range dependencies without the full quadratic complexity. + +4. Practical Implementation: In the case of Mistral 7B, the window size W is set to 4096 tokens. This means that at the last layer, the model can effectively attend to tokens within a window of approximately 131K tokens (32 layers * 4096 window size), even though the input sequence length is limited to 16K tokens. + +The key benefits of Sliding Window Attention are: +1. Improved Efficiency: By limiting the attention span, SWA reduces the computational complexity and memory usage of the attention mechanism, leading to faster inference and lower memory requirements. +2. Handling Longer Sequences: The recursive attention mechanism allows Mistral 7B to effectively capture long-range dependencies, even for long input sequences, without sacrificing performance. + +Overall, Sliding Window Attention is a crucial component of the Mistral 7B architecture that helps balance the goals of high performance and efficient inference, making the model suitable for real-world applications.",0.4,0.56767737031783,0.7499999999625 +simple-rag-claude-3-haiku,8fef30b5-92d9-4446-a720-17b94d866aad,How many stages are there in the development of the Llama 3 model?,"According to the context provided, the development of the Llama 3 language models comprises two main stages: + +1. Language model pre-training: + - The model is pre-trained on a large, multilingual text corpus to perform next-token prediction. This allows the model to learn the structure of language and obtain knowledge about the world from the text. + - The pre-training is performed at a massive scale, with a 405B parameter model trained on 15.6T tokens using a context window of 8K tokens. This is followed by a continued pre-training stage that increases the supported context window to 128K tokens. + +2. Language model post-training: + - The pre-trained language model is then aligned with human feedback through several rounds of supervised finetuning, rejection sampling, and direct preference optimization. + - This post-training stage helps the model to follow instructions and behave in the way expected of an assistant, rather than just having a rich understanding of language. + +So in summary, the development of the Llama 3 model involves two main stages: language model pre-training and language model post-training.",1.0,0.8254964353972353,0.9999999999 +simple-rag-claude-3-haiku,980699f6-cc26-428c-8b5a-61a27d317cc5,What is the difference between the Llama 2 and Llama 2-Chat ?,"The key differences between Llama 2 and Llama 2-Chat are: + +1. Purpose: +- Llama 2 is the base pretrained language model, intended for a variety of natural language generation tasks. +- Llama 2-Chat is a fine-tuned version of Llama 2, specifically optimized for dialogue and assistant-like use cases. + +2. Training Process: +- Llama 2 was trained through pretraining on a large corpus of publicly available data. +- Llama 2-Chat was created by taking the Llama 2 model and further fine-tuning it using supervised learning and reinforcement learning with human feedback (RLHF). This process aligned the model to be more helpful and safe for conversational interactions. + +3. Model Sizes: +- Both Llama 2 and Llama 2-Chat are available in multiple sizes, including 7B, 13B, and 70B parameter versions. + +4. Safety and Performance: +- The Llama 2-Chat models have been shown to outperform open-source models in safety evaluations across both single-turn and multi-turn prompts. The fine-tuning process has made them more aligned with human preferences for helpfulness and safety. + +In summary, Llama 2 is the base language model, while Llama 2-Chat is the fine-tuned version optimized specifically for conversational and assistant-like use cases, with improved safety and performance compared to other open-source models.",0.9565217391304348,0.974208066262647,0.8333333332916666 +simple-rag-claude-3-haiku,d0efabfd-ccae-464b-b3b0-cea3bb3b2794,What is optimizer is used for LLaMA?,"Based on the context provided, the optimizer used for training the LLaMA models is AdamW. Specifically, the passage states: + +""We trained using the AdamW optimizer (Loshchilov and Hutter, 2017), with β1 = 0.9, β2 = 0.95, eps = 10−5."" + +AdamW is a variant of the popular Adam optimizer that incorporates weight decay. The use of AdamW as the optimizer, along with the specified hyperparameter values, is a key detail of the training process for the LLaMA models described in the context.",0.75,0.9065396506662374,0.3333333333 +simple-rag-claude-3-haiku,e2010869-3c34-46d0-9595-9cc6aa46165d,Can the GPT-4 model accept both text and image inputs?,"Yes, according to the context provided, the GPT-4 model can accept both text and image inputs. The context states: + +""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task. Specifically, the model generates text outputs given inputs consisting of arbitrarily interlaced text and images. Over a range of domains—including documents with text and photographs, diagrams, or screenshots—GPT-4 exhibits similar capabilities as it does on text-only inputs."" + +So the key points are: + +1. GPT-4 can accept prompts that include both images and text. +2. This allows the user to specify a wide range of vision or language tasks. +3. GPT-4 can generate text outputs given inputs that mix text and images. +4. GPT-4 demonstrates similar capabilities on these multimodal inputs as it does on text-only inputs. + +Therefore, the context clearly indicates that the GPT-4 model is capable of accepting and processing both text and image inputs.",0.7142857142857143,1.0,0.8333333332916666 +simple-rag-claude-3-haiku,c3df9e4a-94a6-4996-9abe-19a7bf5ea0a4,On what architecture the GPT-3 model is based on?,"Based on the provided context, the GPT-3 model is based on the Transformer architecture, with some key modifications: + +1. Pre-normalization: The model uses pre-normalization, where the input to each transformer sub-layer is normalized, instead of normalizing the output. This was introduced in the original GPT-3 paper to improve training stability. + +2. SwiGLU activation function: The model uses the SwiGLU activation function, which was introduced in the PaLM model to improve performance. It replaces the standard ReLU non-linearity. + +3. Rotary Embeddings: The model uses rotary embeddings instead of absolute positional embeddings. This was introduced in the GPT-Neo model. + +4. Alternating dense and locally banded sparse attention patterns: The model uses a combination of dense and sparse attention patterns in the transformer layers, similar to the Sparse Transformer model. + +So in summary, the GPT-3 model is built on the core Transformer architecture, but incorporates several recent advancements and modifications to improve its performance and training stability, drawing inspiration from models like GPT-2, PaLM, and GPT-Neo.",0.6842105263157895,0.6897303494384182,0.499999999975 +simple-rag-claude-3-haiku,b115b653-4fda-43ed-8102-fb413d40c9df,How many stages of training are in the GPT model?,"Based on the context provided, the GPT models are often trained in two stages: + +1. First, the models are trained using a large dataset of text from the Internet to predict the next word. This is referred to as the pre-training stage. + +2. Then, the models are fine-tuned with additional data, using an algorithm called reinforcement learning from human feedback (RLHF). This fine-tuning stage helps produce outputs that are preferred by human labelers. + +So in summary, the GPT models go through two main stages of training - pre-training on a large text dataset, followed by fine-tuning using reinforcement learning from human feedback. This two-stage training process helps the models develop a broad base of knowledge and then refine their outputs to be more aligned with human preferences.",0.7272727272727273,0.7985030137812528,0.9999999999 +simple-rag-claude-3-haiku,8d58e6e2-5aaf-404c-8e9e-bf02dc7d207f,What tokenizer is used in the Gemma2 model?,"According to the context provided, the Gemma 2 models use the same tokenizer as the Gemma 1 and Gemini models. Specifically, the context states: + +""Tokenizer. We use the same tokenizer as Gemma 1 and Gemini: a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings (Kudo and Richardson, 2018). The resulting vocabulary has 256k entries."" + +So the Gemma 2 models use a SentencePiece tokenizer with a vocabulary of 256,000 entries.",1.0,0.9663442990105505,0.49999999995 +simple-rag-claude-3-haiku,566048a5-9dd7-430b-8d05-da2b30a14b4b,On what architecture the Gemma model is based on?,"According to the context provided, the Gemma model architecture is based on the transformer decoder architecture, as stated in the following excerpt: + +""The Gemma model architecture is based on the transformer decoder (Vaswani et al., 2017)."" + +The transformer decoder is a type of neural network architecture that has been widely used in large language models and other natural language processing tasks. It consists of a series of transformer layers, which use attention mechanisms to capture dependencies between different parts of the input sequence. + +So in summary, the Gemma model is built upon the transformer decoder architecture, which has been shown to be effective for a variety of language understanding and generation tasks.",0.2222222222222222,0.9824451804350853,0.9999999999 +simple-rag-claude-3-haiku,953523ce-4bf7-4afa-b686-5e103db4c963,Does Claude 3 models have vision capabilities?,"Based on the provided context, the Claude 3 models do have vision capabilities. The context states: + +""Figure 26 +Claude 3 models can recognize and identify objects visually, and they can think in complex +ways, such as understanding both an object's appearance and its connections to concepts like mathematics."" + +This indicates that the Claude 3 models are capable of visual recognition and understanding, allowing them to process and analyze images. The context also mentions that the models can handle multimodal inputs, including images, which can be used alongside text prompts. + +However, the context also notes that the models' performance on visual tasks can sometimes be less accurate, especially for small or low-resolution images. It states that the models ""should not be used for consequential use cases that require high precision and accuracy without human validation."" + +So in summary, the Claude 3 models do have vision capabilities, but their performance on visual tasks may not be perfect and should be validated by humans, especially for critical applications. The models can process images, but their visual understanding is not infallible.",1.0,0.9830251010857803,0.9999999999666667 +simple-rag-claude-3-haiku,b207c66c-e048-46e7-bdd6-a1bf499b511b,What are the two tasks in BERT?,"Based on the provided context, the two main tasks in BERT are: + +1. Pre-training: + - During the pre-training stage, the BERT model is trained on unlabeled data over different pre-training tasks. This allows the model to learn general language representations that can be leveraged for downstream tasks. + +2. Fine-tuning: + - In the fine-tuning stage, the pre-trained BERT model is initialized with the pre-trained parameters. Then, all the parameters are fine-tuned using labeled data from the specific downstream tasks. + - The fine-tuning process allows the pre-trained BERT model to be adapted to perform well on various downstream tasks, such as question answering, text classification, named entity recognition, etc. + +The key idea behind BERT is to leverage the power of pre-training on large unlabeled datasets, and then efficiently fine-tune the pre-trained model on specific downstream tasks. This two-stage approach allows BERT to achieve strong performance across a wide range of natural language processing tasks.",0.3636363636363636,0.9679117087732756,0.3333333333 +simple-rag-mistral-7b-instruct,2a5ec2f6-7d87-4615-9e4d-0b1ff49e706b,How can attention be described in the Transformer?," In the Transformer model, attention is a mechanism that allows different positions of a sequence to relate to each other and compute a representation of the sequence. Self-attention, also called intra-attention, is the type of attention used in the Transformer. It allows each position in the encoder or decoder to attend to all positions in the same layer, up to and including that position. This is different from traditional recurrent models, which factor computation along the symbol positions of the input and output sequences and generate a sequence of hidden states as a function of the previous hidden state and the input for that position. The use of self-attention in the Transformer allows for more parallelization and can lead to significant improvements in computational efficiency and model performance. The Transformer is the first transduction model to rely entirely on self-attention for computing representations of its input and output without using sequence-aligned RNNs or convolution.",0.8888888888888888,0.4632475915482617,0.8055555555287036 +simple-rag-mistral-7b-instruct,a367bbac-3481-417e-b744-6e10cc746ff8,What is Mixture of Agents?," Mixture of Agents (MoA) is a methodology that leverages multiple large language models (LLMs) to collaborate and enhance the generation quality of responses. In this approach, each layer of the architecture consists of multiple LLM agents. Each agent generates a response using the outputs from agents in the previous layer as auxiliary information. The MoA architecture has achieved state-of-the-art performance on various benchmarks, such as AlpacaEval 2.0, MT-Bench, and FLASK, surpassing the performance of models like GPT-4 Omni. The collaborative nature of LLMs allows them to generate higher quality responses when they can reference outputs from other models. The MoA methodology significantly outperforms LLM rankers and has shown improvements in robustness, correctness, efficiency, factuality, commonsense, insightfulness, and completeness.",1.0,0.5357192533118421,0.9166666666361111 +simple-rag-mistral-7b-instruct,028c5eb0-9209-4465-ac03-99e974f7ba23,Is Mixtral based on the idea of a mixture of experts?," Yes, Mixtral is indeed based on the idea of a Mixture of Experts (MoE). In Mixtral, each token interacts with a small number of ""experts"" - in this case, two at each timestep - which are selected by a router network. The outputs of these selected experts are then combined to produce the final output for that token. This approach allows Mixtral to access a large number of parameters while only actively using a subset during inference, which can lead to faster inference speeds and higher throughput.",0.9,0.8764177588434284,0.999999999975 +simple-rag-mistral-7b-instruct,dbb76d8f-bd23-4a01-a641-2ebd70faa5a6,What is sliding window attention?," Sliding Window Attention is a type of attention mechanism used in transformer models, such as Mistral 7B, to address the computational and memory challenges that come with traditional attention mechanisms. Instead of allowing each token to attend to all tokens in the sequence, sliding window attention restricts each token's attention to a fixed window of tokens from the previous layer. This allows for more parallelization and reduces the memory requirements, leading to faster inference and improved efficiency. The size of the window determines the number of tokens each token can attend to, and in the case of Mistral 7B, a window size of 4096 allows for an approximate attention span of 131,000 tokens.",0.8666666666666667,0.7051723668384134,0.9999999999 +simple-rag-mistral-7b-instruct,57916eea-36c5-454e-a617-ce1bdffc0a56,How many stages are there in the development of the Llama 3 model?," The development of the Llama 3 model consists of two main stages: + +1. Language model pre-training: In this stage, a large language model is pre-trained on a massive text corpus to learn the structure of language and obtain knowledge about the world. This is done by converting the text corpus to discrete tokens and pre-training the model on the resulting data to perform next-token prediction. Pre-training is performed at a large scale, with a model of 405B parameters and a context window of 128K tokens. +2. Language model post-training: After pre-training, the model is aligned with human feedback through several rounds of supervised finetuning, rejection sampling, and direct preference optimization. This stage integrates multimodal capabilities into Llama 3 and brings the model closer to behaving like a human assistant. The models are still under active development and not yet ready for release.",0.9473684210526315,0.9385616026326954,0.9999999999 +simple-rag-mistral-7b-instruct,9aaf4320-190d-4cdb-a944-a57b6dd509b1,What is the difference between the Llama 2 and Llama 2-Chat ?," Llama 2 and Llama 2-Chat are both models developed by Meta AI as part of the Llama family, but they serve different purposes. + +Llama 2 is a pretrained language model that uses an optimized transformer architecture and is trained on a larger dataset than its predecessor, Llama 1. The pretraining data includes a new mix of publicly available sources, and the model is available in various parameter sizes, including 7B, 13B, and 70B. Llama 2 is intended for commercial and research use in English and can be adapted for a variety of natural language generation tasks. + +Llama 2-Chat, on the other hand, is a fine-tuned version of Llama 2 that is optimized for dialogue use cases. It undergoes additional training using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. Llama 2-Chat is also available in 7B, 13B, and 70B parameter sizes. + +The main difference between the two models is that Llama 2 is a pretrained model, while Llama 2-Chat is a fine-tuned model specifically designed for chat applications. The fine-tuning process helps Llama 2-Chat to generate more contextually appropriate and human-like responses in a conversational setting. According to the provided context, Llama 2-Chat outperforms other open-source models on both single turn and multi-turn prompts.",0.9090909090909091,0.9815519471495716,0.8333333332916666 +simple-rag-mistral-7b-instruct,7da73b26-3d25-4042-b887-43c4abd7643d,What is optimizer is used for LLaMA?," The optimizer used for LLaMA is AdamW. AdamW is a variant of the Adam optimizer, which is commonly used for training machine learning models. It stands for Adaptive Moment Estimation with Weight Decay. The AdamW optimizer adjusts the learning rate for each parameter based on the gradient information, and it also includes a weight decay term to help prevent the model from overfitting. In the context provided, it was used to train LLaMA with a learning rate schedule, a cosine decay, and a weight decay of 0.1.",0.4,0.914787875226717,0.3333333333 +simple-rag-mistral-7b-instruct,788acfd2-0717-4de3-9ff3-cc893911c106,Can the GPT-4 model accept both text and image inputs?," Yes, according to the context provided, the GPT-4 model can accept both text and image inputs. The model generates text outputs given inputs consisting of arbitrarily interlaced text and images. This capability allows users to specify any vision or language task. Examples of visual input for GPT-4 can be found in the context.",0.8571428571428571,1.0,0.8333333332916666 +simple-rag-mistral-7b-instruct,91ba9ad6-d7de-4331-b550-8320b204504e,On what architecture the GPT-3 model is based on?," The GPT-3 model is based on the transformer architecture, which was introduced by Vaswani et al. in 2017. However, it incorporates several improvements that have been proposed since then, such as pre-normalization, SwiGLU activation function, and rotary embeddings. Pre-normalization involves normalizing the input of each transformer sub-layer instead of the output, SwiGLU is a new activation function introduced to improve performance, and rotary embeddings replace absolute positional embeddings with new ones that incorporate sin and cos functions.",0.25,0.6380521118475492,0.249999999975 +simple-rag-mistral-7b-instruct,9516b186-c424-4f00-a801-a5fd8cdcc7c4,How many stages of training are in the GPT model?, The GPT model undergoes two main stages of training: the first stage involves training the model to predict the next word using a large dataset of text from the internet. The second stage is fine-tuning the model using an algorithm called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human labelers.,1.0,0.867624571401988,0.9999999999 +simple-rag-mistral-7b-instruct,85961982-bdac-4729-a904-a274b33f0516,What tokenizer is used in the Gemma2 model?," The Gemma2 model uses the same tokenizer as Gemma1 and Gemini: a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings. The resulting vocabulary has 256k entries.",1.0,0.9892151178644935,0.49999999995 +simple-rag-mistral-7b-instruct,56d10b9a-b8fe-4c4a-b6c9-167b96480e0a,On what architecture the Gemma model is based on?," The Gemma model is based on the transformer decoder architecture, specifically the one introduced in the paper ""Attention is All You Need"" by Vaswani et al. (2017). This architecture is widely used in many large language models due to its effectiveness in handling long-range dependencies in sequences and its ability to achieve strong performance in text domains.",0.3333333333333333,0.9947045503209968,0.9999999999 +simple-rag-mistral-7b-instruct,cc6d102b-b05c-4783-b10a-831f2a425f0d,Does Claude 3 models have vision capabilities?," Yes, according to the context provided, Claude 3 models do have vision capabilities. They can recognize and identify objects visually, and users can upload images along with text prompts for richer context and expanded use cases. However, the performance of the model in handling small or low resolution images may be lower, and the accuracy of image descriptions and information generation may not be as high as human validation requires for consequential use cases.",1.0,0.9942643083073888,0.9999999999666667 +simple-rag-mistral-7b-instruct,997f32d3-b04e-4567-8b13-c5ebcaf3462e,What are the two tasks in BERT?," The two main tasks in BERT are pre-training and fine-tuning. During pre-training, the model is trained on large amounts of unlabeled data using various pre-training tasks. In the fine-tuning stage, the pre-trained BERT model is initialized with the pre-trained parameters and then further trained on labeled data from specific downstream tasks. The goal is to adapt the model to the particular task, such as sentiment analysis, question answering, or named entity recognition.",0.8333333333333334,0.9679117087732756,0.9999999999 +simple-rag-gemma-7b-it,4fd35654-ce7f-468d-bb10-85d4865ca835,How can attention be described in the Transformer?,"**Attention in the Transformer is described as follows:** + +- The Transformer employs **multi-head attention**, which involves parallel attention layers (heads) to capture different aspects of the input. + + +- Each head performs **self-attention**, meaning it allows each position in the sequence to attend to all positions in the sequence, capturing long-range dependencies. + + +- The attention mechanism in the Transformer is **parallel**, which means it can compute the representation of an input sequence simultaneously, rather than sequentially like traditional recurrent models. + + +- The Transformer uses **multiple heads** to increase the effective number of attention heads, enhancing the model's ability to capture complex dependencies in the input sequence.",0.5333333333333333,0.7600955551127712,0.7499999999625 +simple-rag-gemma-7b-it,a6e734ff-2786-4ed7-9191-29172648a9c0,What is Mixture of Agents?,"**Mixture of Agents (MoA)** is a methodology that leverages multiple Large Language Models (LLMs) to iteratively enhance the quality of their responses. It works by: + +- **Multiple LLMs in layers:** The process starts with multiple LLMs in the first layer generating responses to a given prompt. +- **Iterative refinement:** These responses are then presented to subsequent layers of LLMs, which refine them further based on the previous layer's outputs. +- **Collaborative enhancement:** This iterative refinement process continues for several cycles, leading to more robust and comprehensive responses. + +**How it works:** + +- MoA exploits the collaborative capabilities of LLMs, where each model can provide unique perspectives and context. +- By leveraging the strengths of multiple models, MoA generates more comprehensive and accurate responses than any individual LLM. + +**Benefits of MoA:** + +- Improved robustness, correctness, efficiency, factuality, commonsense, insightfulness, and completeness. +- Outperforms GPT-4 Omni in multiple evaluation metrics. +- Achieves state-of-the-art performance on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK.",0.8518518518518519,0.6200701012934485,0.7499999999625 +simple-rag-gemma-7b-it,daeed559-360a-4e19-a967-4ac9cb901fca,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is based on the idea of a **Mixture of Experts (MoE)**. It utilizes a Sparse Mixture of Experts (SMoE) architecture, which involves combining the outputs of multiple experts (feedforward blocks) to enhance model performance.",0.75,0.6500947281985868,0.999999999975 +simple-rag-gemma-7b-it,db6f0b08-a210-4463-95de-6dd60e000c26,What is sliding window attention?,"**Sliding window attention** is a technique used in transformer-based language models to address the computational limitations of vanilla attention, which is quadratic in the sequence length. + +**How it works:** + +- In vanilla attention, each token in the sequence can attend to all other tokens in the sequence, leading to a quadratic time complexity. +- Sliding window attention limits the attention range to a fixed window size W, allowing only a subset of tokens within the window to be attended to by each token. +- This reduces the computational complexity and improves efficiency, especially for longer sequences. + +**In the context of Mistral 7B:** + +- The sliding window size is set to 4096, meaning that each token can attend to a window of 4096 previous tokens. +- This allows for a theoretical attention span of approximately 131K tokens, despite the window size being relatively small. + +**Benefits of sliding window attention:** + +- Reduced computational complexity. +- Improved efficiency for longer sequences. +- Allows for parallelization during inference. + +**Applications:** + +- Language translation +- Text summarization +- Text generation +- Code generation",0.625,0.8029022626195147,0.9999999999 +simple-rag-gemma-7b-it,5a88e7e6-88b1-4aa4-b270-fa845d8fa33f,How many stages are there in the development of the Llama 3 model?,"The development of the Llama 3 model comprises two main stages: + +1. Language model pre-training. +2. Language model post-training.",1.0,0.9385616026326954,0.9999999999 +simple-rag-gemma-7b-it,31f61570-9d23-4852-b047-457f26db57af,What is the difference between the Llama 2 and Llama 2-Chat ?,"**Llama 2** is a large language model that is trained on a new mix of publicly available data, with improvements in data cleaning, context length, and the use of grouped-query attention. + +**Llama 2-Chat** is a fine-tuned version of Llama 2 specifically optimized for dialogue use cases. It is designed to be more helpful and safe in conversational settings.",0.7777777777777778,0.9714733580630797,0.8333333332916666 +simple-rag-gemma-7b-it,18b00f75-9c55-40a1-aef4-52d4198fa0f2,What is optimizer is used for LLaMA?,"The optimizer used for Llama is the **AdamW optimizer**, with a learning rate schedule and hyperparameters including a cosine learning rate decay and weight decay.",1.0,0.8161103929576455,0.3333333333 +simple-rag-gemma-7b-it,87cd2895-c162-4c43-b1a9-69a1d3e5c6e9,Can the GPT-4 model accept both text and image inputs?,"Yes, the GPT-4 model can accept both text and image inputs. It can process documents with text and photographs, diagrams, or screenshots. The model generates text outputs given inputs consisting of arbitrarily interlaced text and images.",1.0,0.8296185726273325,0.8333333332916666 +simple-rag-gemma-7b-it,459e858d-c23c-4712-90c8-02bbba5715d4,On what architecture the GPT-3 model is based on?,"The GPT-3 model is based on the **transformer architecture**, with the following improvements: + +- **Pre-normalization:** Normalizes the input of each transformer sub-layer using the RMSNorm normalization function. +- **SwiGLU activation function:** Replaces the ReLU non-linearity with the SwiGLU activation function to improve performance. +- **Rotary Embeddings:** Removes the absolute positional embeddings and adds rotational embeddings instead.",0.8333333333333334,0.6701487983445623,0.249999999975 +simple-rag-gemma-7b-it,3aa6ee89-b9b2-456e-a776-65a1a887a6db,How many stages of training are in the GPT model?,"The GPT model is trained in two stages: + +1. **Pre-training:** Trained on a massive dataset of text from the Internet to predict the next word. + + +2. **Fine-tuning:** Trained with additional data using reinforcement learning from human feedback to produce outputs preferred by human labelers.",0.8571428571428571,0.6807735367657081,0.9999999999 +simple-rag-gemma-7b-it,1295406f-4a1b-4e59-b10e-9ef9829ccafb,What tokenizer is used in the Gemma2 model?,"The Gemma2 model uses a SentencePiece tokenizer with split digits, preserved whitespace, and byte-level encodings.",1.0,0.9892151178644935,0.49999999995 +simple-rag-gemma-7b-it,b030dc4c-9874-4954-a622-5c76ed91ab65,On what architecture the Gemma model is based on?,"The Gemma model is based on the **transformer decoder architecture** (Vaswani et al., 2017).",1.0,0.9947045503209968,0.9999999999 +simple-rag-gemma-7b-it,fef14de5-fe9f-4edc-b614-c88967de0971,Does Claude 3 models have vision capabilities?,"Yes, Claude 3 models have vision capabilities. They can recognize and identify objects visually, and can even think in complex ways, understanding both an object's appearance and its connections to concepts like mathematics.",1.0,0.9942643083073888,0.9999999999666667 +simple-rag-gemma-7b-it,38141a28-b99d-4961-986d-d214b38daffb,What are the two tasks in BERT?,"The two tasks in BERT are: + +**1. Pre-training:** Training the model on unlabeled data using various pre-training tasks such as NSP (Next Sentence Prediction) and MLM (Masked Language Modeling). + +**2. Fine-tuning:** Adapting the pre-trained model to specific downstream tasks by adding appropriate input and output layers and training it on labeled data from those tasks.",1.0,0.9679117087732756,0.8333333332916666 +simple-rag-gemma2-9b-it,54f2296a-5f0d-449f-953a-be32f30a86b4,How can attention be described in the Transformer?,"The Transformer uses attention in three main ways: + +* **Encoder-Decoder Attention:** This allows the decoder to attend to all positions in the input sequence. Think of it like the decoder looking back at the entire input to understand the context of what it's trying to generate. + +* **Self-Attention in the Encoder:** Each position in the encoder attends to all other positions in the same layer. This helps the encoder understand relationships and dependencies between different parts of the input. + +* **Self-Attention in the Decoder:** Similar to the encoder, each position in the decoder attends to all positions in the decoder up to that point. This allows the decoder to build a representation of the generated sequence as it progresses. + + +Essentially, attention in the Transformer lets different parts of the input and output sequences ""talk"" to each other, regardless of their distance, allowing the model to capture complex relationships and dependencies. +",0.5384615384615384,0.8369018122584321,0.7499999999625 +simple-rag-gemma2-9b-it,1f1a2b4f-dd95-4965-b9a3-fd98691f5700,What is Mixture of Agents?,"Mixture-of-Agents (MoA) is a new approach to leveraging the power of multiple Large Language Models (LLMs). + +Imagine it like a team of experts working together. + +* **The Team:** MoA creates a layered structure with each layer containing several LLMs, called ""agents."" +* **Sharing Knowledge:** Each agent takes the outputs from all the agents in the previous layer as input when generating its own response. This allows them to build upon each other's ideas and improve the overall quality of the final answer. + +Think of it like a brainstorming session where each person contributes their thoughts, and the group refines and improves upon them collectively. + +This collaborative approach has been shown to significantly outperform single LLMs, leading to better and more comprehensive responses. +",0.4166666666666667,0.6236265068677259,0.8055555555287036 +simple-rag-gemma2-9b-it,2306a0b2-6f59-4694-ac98-963d786c44b8,Is Mixtral based on the idea of a mixture of experts?,"Yes, Mixtral is explicitly designed as a **Sparse Mixture of Experts (SMoE)** model. + +The text states: ""We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model."" + + +Let me know if you have any other questions about Mixtral! +",1.0,0.6950071324552871,0.9999999999666667 +simple-rag-gemma2-9b-it,9d5632fd-d3a8-4fc5-a03c-a707a8123ddd,What is sliding window attention?,"Sliding window attention is a technique used in large language models (LLMs) to allow them to attend to information from a larger context than would be possible with traditional attention mechanisms. + +Here's how it works: + +* **Limited Window:** Instead of attending to the entire sequence at once, sliding window attention focuses on a fixed-size window of previous tokens. +* **Shifting Window:** As the model processes each token, this window ""slides"" forward, allowing the model to access information from progressively earlier parts of the sequence. +* **Recursive Access:** Because the model has multiple layers, each layer's window can access information from a larger context. Think of it like a stack of windows, each layer looking back a bit further than the one before. + +**Benefits:** + +* **Reduced Computational Cost:** By focusing on smaller windows, sliding window attention reduces the number of attention calculations needed, making the model more efficient. +* **Improved Memory Efficiency:** It also limits the amount of memory required to store past information, allowing for handling longer sequences. + +**Example:** + +Imagine a window size of 4 tokens. + +* The first layer attends to the previous 4 tokens. +* The second layer attends to the previous 8 tokens (4 from the previous layer + 4 from the input). +* And so on... + +This allows the model to build a representation of the entire sequence, even though it's only directly attending to a smaller window at each step. + + +Let me know if you have any other questions! +",0.4074074074074074,0.7658352370558137,0.9999999999 +simple-rag-gemma2-9b-it,ee07563c-854a-44e9-a500-99e1cda87a0a,How many stages are there in the development of the Llama 3 model?,"The development of Llama 3 has two main stages: + +1. **Language model pre-training:** This stage involves training the model on a massive text dataset to understand language structure and world knowledge. +2. **Language model post-training:** This stage aligns the pre-trained model with human feedback through techniques like supervised fine-tuning, rejection sampling, and direct preference optimization to make it more helpful and harmless. + + +",0.8181818181818182,0.8916191702652686,0.9999999999 +simple-rag-gemma2-9b-it,fb41a04d-2a19-4015-9129-0f4c3fd39fa9,What is the difference between the Llama 2 and Llama 2-Chat ?,"Llama 2 and Llama 2-Chat are both large language models developed by Meta AI, but they have different focuses: + +* **Llama 2:** This is the foundational model, trained on a massive dataset of publicly available text. It's designed for a wide range of natural language processing tasks, like text generation, translation, and summarization. Think of it as the general-purpose language expert. + +* **Llama 2-Chat:** This is a specialized version of Llama 2 that's been fine-tuned specifically for conversational interactions. It's better at understanding context, generating human-like dialogue, and engaging in back-and-forth conversations. Imagine it as the chatty and conversational sibling of Llama 2. + + +Essentially, Llama 2 is the broader, more versatile model, while Llama 2-Chat is tailored for creating chatbots and similar applications. +",0.5,0.9766560265582885,0.8333333332916666 +simple-rag-gemma2-9b-it,24cb73a3-f097-4b6a-bbff-abd6384fcde4,What is optimizer is used for LLaMA?,"The text states that Llama 2 was trained using the **AdamW optimizer**. +",1.0,0.7935489579862396,0.3333333333 +simple-rag-gemma2-9b-it,5e7e939b-ca3e-431e-9cc5-35d0027acfb1,Can the GPT-4 model accept both text and image inputs?,"Yes, GPT-4 can accept both text and image inputs. + +The text states: ""GPT-4 accepts prompts consisting of both images and text, which—parallel to the text-only setting—lets the user specify any vision or language task."" +",1.0,0.9692922226109744,0.8333333332916666 +simple-rag-gemma2-9b-it,65eb488f-cbc2-4cd7-925e-21e6acc0ac41,On what architecture the GPT-3 model is based on?,"The GPT-3 model is based on the **transformer architecture**. + +The text states: ""Following recent work on large language models, our network is based on the transformer architecture (Vaswani et al., 2017)."" +",1.0,0.9935035488418335,0.249999999975 +simple-rag-gemma2-9b-it,67b8d955-40bd-4b77-a365-3cbb9f5b2a28,How many stages of training are in the GPT model?,"The GPT model is trained in **two** stages. + +First, it is trained on a massive dataset of text from the internet to predict the next word. + +Second, it is fine-tuned using a technique called reinforcement learning from human feedback (RLHF) to produce outputs that are preferred by human reviewers. +",1.0,0.9730765519325724,0.9999999999 +simple-rag-gemma2-9b-it,2860f8d9-371c-4fa0-8835-a111f8c2c07e,What tokenizer is used in the Gemma2 model?,"The Gemma 2 models use the same tokenizer as Gemma 1 and Gemini: a SentencePiece tokenizer. +",1.0,0.9668872845590216,0.49999999995 +simple-rag-gemma2-9b-it,b4b11963-7694-41aa-aefb-35481b87946b,On what architecture the Gemma model is based on?,"The Gemma model is based on the **transformer decoder** architecture. +",1.0,0.9947045503209968,0.9999999999 +simple-rag-gemma2-9b-it,80d86ae0-1438-4011-845c-9ebecc60f6c4,Does Claude 3 models have vision capabilities?,"Yes, Claude 3 models have vision capabilities. + +The text states: ""Claude 3 models can recognize and identify objects visually, and they can think in complex ways, such as understanding both an object’s appearance and its connections to concepts like mathematics."" + +It also mentions that Claude 3 Haiku, one of the models in the family, includes vision capabilities. +",1.0,0.9942643083073888,0.8333333332916666 +simple-rag-gemma2-9b-it,db3fbde4-e291-42cc-ac8e-f979391b2255,What are the two tasks in BERT?,"The two main tasks in BERT are: + +1. **Pre-training:** This involves training the model on a massive amount of unlabeled text data using various techniques like masked language modeling (MLM) and next sentence prediction (NSP). + +2. **Fine-tuning:** This step takes the pre-trained BERT model and further trains it on a smaller, labeled dataset specific to a downstream task, such as question answering, sentiment analysis, or text classification. + + +Let me know if you have any other questions about BERT! +",0.7272727272727273,0.9679117087732756,0.8333333332916666 diff --git a/src/configuration/configuration_model.py b/src/configuration/configuration_model.py index c00034c..580f7a4 100644 --- a/src/configuration/configuration_model.py +++ b/src/configuration/configuration_model.py @@ -1,4 +1,5 @@ """Module for defining the structure of the configs for the RAG models.""" + import warnings from typing import Dict, List, Optional, Union @@ -6,10 +7,11 @@ warnings.filterwarnings("ignore") + class ModelKwargsBedrock(BaseModel): """Pydantic model for the model kwargs for the Bedrock model.""" - max_tokens: Optional[conint(ge=256, le=8192)] = Field(None, alias='max_gen_len') + max_tokens: Optional[conint(ge=256, le=8192)] = Field(None, alias="max_gen_len") temperature: Optional[confloat(ge=0.0, le=1.0)] = None top_k: Optional[conint(ge=0, le=500)] = None top_p: Optional[confloat(ge=0.0, le=500.0)] = None @@ -17,6 +19,7 @@ class ModelKwargsBedrock(BaseModel): class Config: """Pydantic config for the model kwargs for the Bedrock model.""" + allow_population_by_field_name = True diff --git a/src/constants/evaluation_config.py b/src/constants/evaluation_config.py new file mode 100644 index 0000000..ee2d327 --- /dev/null +++ b/src/constants/evaluation_config.py @@ -0,0 +1,23 @@ +"""Constants connected to evaluation of the model.""" + +from ragas.metrics import answer_relevancy, context_utilization, faithfulness + +LLM_MODEL_ID = "anthropic.claude-3-5-sonnet-20240620-v1:0" + +LLM_MODEL_KWARGS = {"max_tokens": 4096, "temperature": 0.1} + +EMBEDDER_MODEL_ID = "amazon.titan-embed-text-v2:0" + +EMBEDDER_MODEL_KWARGS = {"dimensions": 512, "normalize": True} + +METRICS = [faithfulness, answer_relevancy, context_utilization] + +RESULT_COLUMNS = [ + "experiment_name", + "trace_id", + "question", + "answer", + "faithfulness", + "answer_relevancy", + "context_utilization", +] diff --git a/src/constants/prompts.py b/src/constants/prompts.py index 9bf6b16..beebd75 100644 --- a/src/constants/prompts.py +++ b/src/constants/prompts.py @@ -178,4 +178,4 @@ {{output_3}} Assistant: -""" \ No newline at end of file +""" diff --git a/src/constants/questions.py b/src/constants/questions.py index 15f1bc2..7cfb08c 100644 --- a/src/constants/questions.py +++ b/src/constants/questions.py @@ -14,5 +14,5 @@ "What is sliding window attention?", "Is Mixtral based on the idea of a mixture of experts?", "What is Mixture of Agents?", - "How can attention be described in the Transformer?" + "How can attention be described in the Transformer?", ] diff --git a/src/models/simple_rag.py b/src/models/simple_rag.py index 73d0494..46e4916 100644 --- a/src/models/simple_rag.py +++ b/src/models/simple_rag.py @@ -52,7 +52,7 @@ def initialize_llm(self): credentials_profile_name=os.environ["BEDROCK_CREDENTIALS_PROFILE_NAME"], model_id=self.config.llm.model_spec.model_id, model_kwargs=self.config.llm.model_spec.model_kwargs, - beta_use_converse_api=True + beta_use_converse_api=True, ) logger.info("Bedrock LLM initialized") elif self.config.llm.provider == "groq": diff --git a/src/pipelines/mixture_rag_pipeline.py b/src/pipelines/mixture_rag_pipeline.py index 7607fa3..f3474a0 100644 --- a/src/pipelines/mixture_rag_pipeline.py +++ b/src/pipelines/mixture_rag_pipeline.py @@ -8,9 +8,9 @@ from langfuse import Langfuse from langfuse.callback import CallbackHandler -from ragas.metrics import answer_relevancy, context_utilization, faithfulness from src.configuration.configuration_model import MixtureRAGConfig +from src.constants import evaluation_config from src.models.mixture_rag import MixtureRAG from src.utils.evaluation import init_llm_n_metrics, score_output @@ -19,40 +19,45 @@ warnings.filterwarnings("ignore") -METRICS = [faithfulness, answer_relevancy, context_utilization] +PUBLIC_KEY = os.environ.get("LANGFUSE_PUBLIC_KEY") +SECRET_KEY = os.environ.get("LANGFUSE_SECRET_KEY") +HOST = os.environ.get("LANGFUSE_HOST") +METRICS = evaluation_config.METRICS def mixture_rag_pipeline_execution( config: MixtureRAGConfig, rag_prompts: list, aggregator_prompt: str, questions: list ): """ - Executes the SimpleRAG pipeline. + Executes the Mixture RAG pipeline. Args: - config (SimpleRAGConfig): The configuration for SimpleRAG. + config (MixtureRAGConfig): The configuration for Mixture RAG. prompt (str): The prompt to be used. questions (list): A list of questions to be processed. Returns: None """ + logger.info("Starting Mixture RAG pipeline execution") logger.info("Creating Langfuse client") langfuse = Langfuse( - secret_key=os.environ["LANGFUSE_SECRET_KEY"], - public_key=os.environ["LANGFUSE_PUBLIC_KEY"], - host=os.environ["LANGFUSE_HOST"], + secret_key=SECRET_KEY, + public_key=PUBLIC_KEY, + host=HOST, ) langfuse_handler = CallbackHandler( - secret_key=os.environ["LANGFUSE_SECRET_KEY"], - public_key=os.environ["LANGFUSE_PUBLIC_KEY"], - host=os.environ["LANGFUSE_HOST"], + secret_key=SECRET_KEY, + public_key=PUBLIC_KEY, + host=HOST, session_id=config.experiment_name, ) logger.info("Initializing LLM and metrics for evaluation") init_llm_n_metrics(METRICS) + logger.info("Initializing Mixture RAG") mixture_rag = MixtureRAG(config) mixture_rag.initialize_base() @@ -81,4 +86,4 @@ def mixture_rag_pipeline_execution( score_output(langfuse, trace_id, METRICS, question, contexts, answer) print(question, answer, contexts) - logger.info("MixtureRAG pipeline execution complete") + logger.info("Mixture RAG pipeline execution complete") diff --git a/src/pipelines/simple_rag_pipeline.py b/src/pipelines/simple_rag_pipeline.py index 4b0792c..f1d71f2 100644 --- a/src/pipelines/simple_rag_pipeline.py +++ b/src/pipelines/simple_rag_pipeline.py @@ -1,5 +1,5 @@ """ -This module defines the pipeline execution for SimpleRAG. +This module defines the pipeline execution for Simple RAG. """ import logging @@ -8,9 +8,9 @@ from langfuse import Langfuse from langfuse.callback import CallbackHandler -from ragas.metrics import answer_relevancy, context_utilization, faithfulness from src.configuration.configuration_model import SimpleRAGConfig +from src.constants import evaluation_config from src.models.simple_rag import SimpleRAG from src.utils.evaluation import init_llm_n_metrics, score_output @@ -19,41 +19,45 @@ warnings.filterwarnings("ignore") -METRICS = [faithfulness, answer_relevancy, context_utilization] +PUBLIC_KEY = os.environ.get("LANGFUSE_PUBLIC_KEY") +SECRET_KEY = os.environ.get("LANGFUSE_SECRET_KEY") +HOST = os.environ.get("LANGFUSE_HOST") +METRICS = evaluation_config.METRICS def simple_rag_pipeline_execution( config: SimpleRAGConfig, prompt: list, questions: list ): """ - Executes the SimpleRAG pipeline. + Executes the Simple RAG pipeline. Args: - config (SimpleRAGConfig): The configuration for SimpleRAG. + config (SimpleRAGConfig): The configuration for Simple RAG. prompt (str): The prompt to be used. questions (list): A list of questions to be processed. Returns: None """ + logger.info("Starting Simple RAG pipeline execution") logger.info("Creating Langfuse client") langfuse = Langfuse( - secret_key=os.environ["LANGFUSE_SECRET_KEY"], - public_key=os.environ["LANGFUSE_PUBLIC_KEY"], - host=os.environ["LANGFUSE_HOST"], + secret_key=SECRET_KEY, + public_key=PUBLIC_KEY, + host=HOST, ) langfuse_handler = CallbackHandler( - secret_key=os.environ["LANGFUSE_SECRET_KEY"], - public_key=os.environ["LANGFUSE_PUBLIC_KEY"], - host=os.environ["LANGFUSE_HOST"], + secret_key=SECRET_KEY, + public_key=PUBLIC_KEY, + host=HOST, session_id=config.experiment_name, ) logger.info("Initializing LLM and metrics for evaluation") init_llm_n_metrics(METRICS) - logger.info("Initializing SimpleRAG") + logger.info("Initializing Simple RAG") simple_rag = SimpleRAG(config) simple_rag.initialize_base() @@ -80,4 +84,4 @@ def simple_rag_pipeline_execution( score_output(langfuse, trace_id, METRICS, question, contexts, answer) print(question, answer, contexts) - logger.info("SimpleRAG pipeline execution complete") + logger.info("Simple RAG pipeline execution complete") diff --git a/src/utils/evaluation.py b/src/utils/evaluation.py index 2bb9619..e4ed23f 100644 --- a/src/utils/evaluation.py +++ b/src/utils/evaluation.py @@ -12,15 +12,17 @@ from ragas.metrics.base import MetricWithEmbeddings, MetricWithLLM from ragas.run_config import RunConfig +from src.constants import evaluation_config + logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) warnings.filterwarnings("ignore") -LLM_MODEL_ID = "anthropic.claude-3-5-sonnet-20240620-v1:0" -LLM_MODEL_KWARGS = {"max_tokens": 4096, "temperature": 0.1} -EMBEDDER_MODEL_ID = "amazon.titan-embed-text-v2:0" -EMBEDDER_MODEL_KWARGS = {"dimensions": 512, "normalize": True} +LLM_MODEL_ID = evaluation_config.LLM_MODEL_ID +LLM_MODEL_KWARGS = evaluation_config.LLM_MODEL_KWARGS +EMBEDDER_MODEL_ID = evaluation_config.EMBEDDER_MODEL_ID +EMBEDDER_MODEL_KWARGS = evaluation_config.EMBEDDER_MODEL_KWARGS def _wrap_model(