Use correct model

[shanjiaz]

SUMMARY:
This test was skipped and when run, was giving the can't copy out of meta tensor error. This is because the model is missing lm_head and embed_token layers and therefore only loaded to meta device. Switching to our model to avoid this issue.

TEST PLAN:
Tested locally and passed.

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @shanjiaz, 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 issue preventing a test from running by updating the model used within the test suite. The change ensures that the test can execute correctly, resolving a problem where the previous model's incomplete structure led to loading errors.

Highlights

• Test Model Update: Replaced the "Xenova/llama2.c-stories110M" model with "nm-testing/llama2.c-stories15M" in "tests/llmcompressor/recipe/test_recipe_parsing.py".
• Error Resolution: This change resolves a "can't copy out of meta tensor" error caused by the previous model lacking necessary layers (lm_head and embed_token), which prevented it from being fully loaded.
• Test Enablement: The updated model allows a previously skipped test to now run and pass successfully.

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[gemini-code-assist]

Code Review

This pull request resolves an issue with a failing test by swapping out a problematic model for one that is better suited for the test environment. The change from Xenova/llama2.c-stories110M to nm-testing/llama2.c-stories15M is well-justified in the description, as the new model is functional and smaller, which should lead to more reliable and faster test execution. The change is straightforward and correct.

[brian-dellabetta]

thank you!