KEP-2170: Design Trainer for the LLM Runtimes

[andreyvelich]

As part of Kubeflow Training V2 work, we should design and implement custom Trainer to fine-tune LLMs that we are planning to support via TrainingRuntimes in Kubeflow upstream.

We should discuss whether we should use native PyTorch APIs or HuggingFace Transformers in the LLM Trainer implementation.

The Trainer should allow users to configure LoRA, QLoRA, FSDP, and other important configurations.

Useful resources:

• LLM Trainer implementation in the Kubeflow Training V1
• Recipes to fine-tune Llama models
• Updated Llama recipes that we use to fine-tune Llama 3.2 - 1B: https://github.com/andreyvelich/llama-recipes/tree/kubeflow-llama

Part of: #2170

Design Doc

Initial design doc from @Electronic-Waste where we can brainstorm ideas: https://docs.google.com/document/d/1a4xWGVWZo43QKv8tIomoK_XHzBMC_byXBnDb0104htQ/edit?tab=t.0

cc @saileshd1402 @deepanker13 @kubeflow/wg-training-leads

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[andreyvelich]

/assign @saileshd1402

We are experimenting with some PyTorch-native and Transformers APIs to design this Trainer.

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In response to this:

/assign @saileshd1402

We are experimenting with some PyTorch-native and Transformers APIs to design this Trainer.

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[saileshd1402]

/assign