[WIP] Enhancing LM Trainer with New Features

[Copilot]

• Explore repository structure and understand current LLM trainer implementation
• Analyze existing training infrastructure and model architecture
• Identify memory and speed optimization opportunities
• Install dependencies and test basic functionality
• Fix missing data module to make base functionality work
• Implement LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning
• Add quantization support (8-bit/4-bit) for memory optimization
• Implement optimized attention mechanisms for speed improvements
• Add advanced optimizers (8-bit AdamW) for memory efficiency
• Create memory optimization utilities (CPU offloading, better checkpointing)
• Update training configuration for new optimization features
• Integrate all optimizations into trainer for 80% VRAM reduction and 2x-5x speedup
• Add documentation and examples for new features
• Validate performance improvements with benchmarks

Current Analysis:
The repository has a solid transformer architecture but is missing the data module. The existing codebase already has good foundations with:

• Mixed precision training (AMP) support
• Gradient checkpointing configuration
• Multiple optimizer support
• Distributed training infrastructure

Planned Optimizations:

1. Memory reduction (80% less VRAM): LoRA, quantization, enhanced gradient checkpointing, CPU offloading
2. Speed improvements (2x-5x faster): Flash attention, 8-bit optimizers, compilation, optimized data loading

Created from VS Code via the GitHub Pull Request extension.

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