fix fp8

[ZZHbible]

This PR fixes the FP8 + quantization check by using model_args.quantization_bit instead of training_args.quantization_bit, since quantization bits belong to model loading config and may not be present in training_args.

[gemini-code-assist]

Summary of Changes

Hello @ZZHbible, 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 resolves an issue where the compatibility check between FP8 training and model quantization was using an incorrect parameter source. By switching the reference from training_args to model_args for the quantization_bit, the system now accurately validates these configurations, preventing potential misconfigurations or runtime errors related to mixed precision training.

Highlights

• FP8 and Quantization Check Fix: The pull request corrects the logic for checking compatibility between FP8 training and quantization. Previously, the check incorrectly referenced training_args.quantization_bit, which might not always be present or accurate for model-specific quantization settings. This change updates the check to use model_args.quantization_bit, ensuring that the validation correctly identifies when FP8 training is incompatible with a model's quantization configuration.

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[gemini-code-assist]

Code Review

This pull request correctly fixes a bug where the check for conflicting FP8 and quantization settings was using training_args.quantization_bit instead of model_args.quantization_bit. Since quantization settings are part of the model configuration, this change correctly aligns the validation logic with the argument structure. The fix is accurate and improves the code's correctness.

[hiyouga]

nice catch