Mistral packing, train on completions only, simplifications

[djsaunde]

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[gemini-code-assist]

Summary of Changes

Hello @djsaunde, 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 introduces a minor but significant change to the Mistral model's fast forward implementation. By passing **kwargs through to the core model call, it ensures that any additional, potentially custom, keyword arguments can be utilized, thereby improving the adaptability and future-proofing of the model's inference process.

Highlights

• Model Flexibility: The MistralForCausalLM_fast_forward function now correctly pipes all additional keyword arguments (**kwargs) directly to the underlying model() call. This enhances the flexibility and extensibility of the Mistral model's fast forward pass.

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[gemini-code-assist]

Code Review

This pull request makes a small but important change to MistralForCausalLM_fast_forward by passing **kwargs to the underlying self.model() call. This is a necessary fix to ensure that keyword arguments, which are already accepted by the function, are correctly propagated through the model's forward pass. This enables features that rely on these arguments, such as packed sequence training. The change is correct and well-contained.

[chatgpt-codex-connector]

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[djsaunde]

Simplified this since trl emits this position_ids metadata.