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metadata
license: agpl-3.0
language:
  - en

scripts

Personal scripts to automate some tasks.
Will try to keep external module use to a minimum, other than huggingface_hub.
Feel free to send in PRs or use this code however you'd like.
GitHub mirror

For GitHub: Would recommend creating pull requests and discussions on the offical huggingface repo

existing files

work in progress/not tested (unfinished branch)

  • EXL2 Multi Quant local
    • Will replace Local Quant Windows on this readme, and have Linux support. Modified version of Auto EXL2 upload without the upload and deleting part.

other recommended stuff

usage

  • Manage branches

    • Run script and follow prompts. You will be required to be logged in to HF Hub. If you are not logged in, you will need a WRITE token. You can get one in your HuggingFace settings. Colab and Kaggle secret keys are supported.

  • Auto EXL2 upload

    • This script is designed to automate the process of quantizing models to EXL2 and uploading them to the HF Hub as seperate branches. This is both available to run on Windows and Linux.

  • EXL2 Local Quant Windows

    • Easily creates environment to quantize models to exl2 using Windows to your local machine. Replacing soon.

  • Upload folder to repo

    • Uploads user specified folder to specified repo, can create private repos too. Not the same as git commit and push, instead uploads any additional files. This is more to be modified to your needs then used by itself.

  • EXL2 Single Quant

    • Allows you to quantize to exl2 using colab. This version creates a exl2 quant to upload to private repo. Only 7B tested on colab.

  • Download models

    • To use the script, open a terminal and run 'python download-model.py USER/MODEL:BRANCH'. There's also a '--help' flag to show the available arguments. To download from private repositories, make sure to login using 'huggingface-cli login' or (not recommended) HF_TOKEN environment variable.

extras

  • HF login snippet
    • The login method that I wrote to make fetching the token better.
  • HF login snippet kaggle
    • Same as above but for cloud ipynb environments like Colab and Kaggle (Kaggle secret support)