Deduplicate Transformers backend code using inheritance

[hmellor]

Now that VLM support is merged and the model loading has been cleaned up a bit, the Transformers backend classes can be refactored to use inheritance, greatly reducing code duplication.

In summary, this PR:

• Converts TransformersModel to a base class (TransformersBase) which:
  • inherits SupportsQuant, SupportsLoRA and SupportsPP
• TransformersForCausalLM:
  • inherits TransformersBase and adds a language modelling head
• TransformersForMultiModalLM:
  • inherits TransformersForCausalLM and SupportsMultiModal
  • adds multimodal preprocessing methods

[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces a well-designed refactoring of the Transformers backend classes. By leveraging inheritance, it significantly reduces code duplication and improves the overall structure and maintainability of the code. The new base class TransformersBase cleanly encapsulates common functionality, and the specialized classes TransformersForCausalLM and TransformersForMultimodalLM correctly extend it.

The logic for model initialization, forward pass, and weight loading appears to be preserved while being much cleaner. I've reviewed the changes and found no issues. This is a great improvement to the codebase.

[Isotr0py]

LGTM!