[WIP] Implementation of Specialized Trainers for Efficient Fine-Tuning

[Copilot]

TITLE: Implementation of Specialized Trainers for Efficient Fine-Tuning

USER INTENT: The user aims to implement various specialized trainers (SFTTrainer, DPOTrainer, etc.) in their codebase to enable efficient fine-tuning similar to the Unsloth framework, achieving faster training times and reduced VRAM usage.

TASK DESCRIPTION: The user wants to enhance their existing training framework by integrating multiple trainer types from Hugging Face TRL and Unsloth, focusing on optimizing performance and memory usage during fine-tuning.

EXISTING: The user currently has a single Trainer class located at c:/Users/koula/Desktop/trainer/src/llm_trainer/training/trainer.py, which handles general LLM training but lacks specialized implementations for SFT, DPO, PPO, or Unsloth-style trainers.

PENDING: The user needs to:

1. Create new trainer classes for SFT, DPO, PPO, and Unsloth-style efficient training.
2. Implement quantization and memory/speed optimizations in the Unsloth-style trainer.
3. Allow selection of trainer type from the main training script/config.

CODE STATE:

• Current file: c:/Users/koula/Desktop/trainer/src/llm_trainer/training/trainer.py
• Proposed new file: specialized_trainers.py (to be created based on user preference).

RELEVANT CODE/DOCUMENTATION SNIPPETS:

• Hugging Face TRL Trainers:

  • SFTTrainer: Supervised fine-tuning with prompt formatting and gradient accumulation.
  • DPOTrainer: Direct preference optimization based on human preferences.
  • PPOTrainer: Reinforcement learning for language model optimization.
  • GRPOTrainer: Group preference optimization.

• Unsloth Techniques:

  • Supports 4/8/16-bit quantization and optimized kernels for faster training.
  • Memory-efficient loss and manual autograd optimization.

OTHER NOTES: The assistant has outlined the next steps for implementation and is awaiting the user's preference on whether to create a new file for specialized trainers or to integrate them into the existing trainer file.
Created from VS Code via the GitHub Pull Request extension.

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