license	language	pipeline_tag	library_name
apache-2.0	en zh	text-generation	transformers

Model Card for QWERTY

Go Wider and Deeper (and Faster and Lower and MoE...er?)

QWERTY (QWen Experts in low Rank with Tiny cache and speculative Yeets) is a Qwen-2.5-based Mixture of Experts (MoE) model leveraging speculative decoding, with all linear modules converted to low-rank approximations.

Table of Contents

• Model Card for QWERTY
• Table of Contents
• Table of Contents
• Model Details
  • Model Description
• Uses
  • Direct Use
  • Downstream Use [Optional]
  • Out-of-Scope Use
• Bias, Risks, and Limitations
  • Recommendations
• Training Details
  • Training Data
  • Training Procedure
    • Preprocessing
    • Speeds, Sizes, Times
• Evaluation
  • Testing Data, Factors & Metrics
    • Testing Data
    • Factors
    • Metrics
  • Results
• Model Examination
• Environmental Impact
• Technical Specifications [optional]
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
• Citation
• Glossary [optional]
• More Information [optional]
• Model Card Authors [optional]
• Model Card Contact
• How to Get Started with the Model

Model Details

Model Description

Go Wider and Deeper (and Faster and Lower and MoE...er?)

QWERTY (QWen Experts in low Rank with Tiny cache and speculative Yeets) is a Qwen-2.5-based Mixture of Experts (MoE) model leveraging speculative decoding, with all linear modules converted to low-rank approximations.

• Developed by: 🧪🖌🪄
• Shared by [Optional]: More information needed
• Model type: Language model
• Language(s) (NLP): mul
• License: apache-2.0
• Parent Model: More information needed
• Resources for more information: More information needed
  • GitHub Repo

Uses

Direct Use

Downstream Use [Optional]

Out-of-Scope Use

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

Training Details

Training Data

More information on training data needed

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

More information needed

Metrics

More information needed

Results

More information needed

Model Examination

More information needed

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: More information needed
• Hours used: More information needed
• Cloud Provider: More information needed
• Compute Region: More information needed
• Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed

Citation

BibTeX:

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APA:

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Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

🧪🖌🪄

Model Card Contact

https://ScienceArtMagic.bsky.social

How to Get Started with the Model

Use the code below to get started with the model.

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⚠️ Type of model unknown