[bugfix] saving model without model.name_or_path

[HDCharles]

Summary:

the way models were saved assumed that model.name_or_path was populated but this is not always the case. I think that if you generate a model and then immediately quantize it, you'll run into this problem.

This PR relaxes this assumption so that the model can be saved.

TEST PLAN:
ran https://gist.github.com/HDCharles/78c55da388076aa046ee893edfbd9df5

previously you'd get:

huggingface_hub.errors.HFValidationError: Repo id must use alphanumeric chars, '-', '_' or '.'. The name cannot start or end with '-' or '.' and the maximum length is 96: ''.

now:

no error

[github-actions]

👋 Hi! Thank you for contributing to llm-compressor. Please add the ready label when the PR is ready for review.

Note: This is required to complete the testing suite, please only add the label once the PR is code complete and local testing has been performed.

[gemini-code-assist]

Summary of Changes

Hello @HDCharles, 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 addresses a critical bug that prevented models from being saved correctly when their configuration's _name_or_path attribute was present but empty. The changes ensure that the model saving process is more robust and reliable. Additionally, the pull request improves the stability of recipe inference by implementing error handling for Hugging Face Hub validation issues, contributing to a more resilient overall system for model and recipe management.

Highlights

• Model Saving Logic Refinement: The pull request modifies the model saving mechanism to ensure that the _name_or_path attribute of a model's configuration is not only present but also contains a non-empty string before being utilized. This resolves a bug where models could not be saved if _name_or_path was an empty string, relaxing a previous assumption.
• Robust Recipe Inference: The infer_recipe_from_model_path function has been enhanced to gracefully handle HFValidationError exceptions that may occur when attempting to load recipes from the Hugging Face Hub cache. This prevents potential crashes and improves the resilience of the recipe inference process.
• Debugging Additions: Several print statements have been introduced in key functions like copy_python_files_from_model_cache and infer_recipe_from_model_path to provide better visibility into configuration values and model path resolution during execution, aiding in future debugging efforts.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request addresses a bug where model saving would fail if model.name_or_path was not populated. The changes correctly relax this assumption by adding checks for an empty path and handling potential errors when resolving recipes from what might be an invalid Hugging Face Hub path. My review focuses on cleaning up some leftover debugging code and improving logging consistency. I've identified a couple of print statements that should be removed and suggested changing a log level from info to debug for an expected error case to avoid unnecessary noise in the logs. The core logic of the fix is sound.