Workflow for periodically building top models from huggingFace

[avinashsingh77]

This PR adds the following:

1. Github workflow for:
   1. Selecting top models based on downloads/trending/likes
      1. Allows filtering based on total size, number of models, model family
      2. Parameters to override model selection criteria
   2. Build modctl
   3. Download models selected in step 1, build them using modctl and push to ghcr.io
2. Script to select top models, which is used in workflow
3. README for scripts

Closes #309

[gemini-code-assist]

Summary of Changes

Hello @avinashsingh77, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request establishes an automated pipeline for curating and building popular machine learning models from HuggingFace Hub. By introducing a new GitHub workflow and an accompanying Python script, it ensures that a selection of modctl-compatible models are regularly identified, processed, and made available in ghcr.io, enhancing the accessibility and freshness of pre-built models.

Highlights

• Automated Model Building Workflow: Introduces a new GitHub workflow to periodically select, build, and publish top HuggingFace models to ghcr.io.
• Intelligent Model Selection: The workflow incorporates a Python script to filter models based on criteria such as download count, likes, trending status, size limits, and modctl compatibility (e.g., presence of config.json, supported file formats).
• New Python Script: Adds select-top-models.py to programmatically fetch and filter models from HuggingFace Hub.
• Comprehensive Documentation: Includes top-model-selection.md to detail the model selection script, its usage, options, and integration with the new GitHub workflow.

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Ignored Files

• Ignored by pattern: .github/workflows/** (1)
  • .github/workflows/build-top-models.yml

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[gemini-code-assist]

Code Review

This PR introduces a useful workflow and script for selecting top models from HuggingFace. The Python script is well-structured and includes good features like command-line arguments and fallback for older library versions. My review includes a few suggestions to improve robustness, performance, and correctness. Specifically, I've pointed out places where exception handling can be improved to aid debugging, suggested some performance optimizations for list lookups, and fixed a typo in the documentation. Overall, this is a great addition.