- implement Mistral model in llm/models/mistral/mistral.py with GroupedQueryAttention, SwiGLU, RoPE, sliding window attention
- add __init__.py for module export
- add config files for mistral training and generation
- update universal experiment runner to support Mistral model
- add notebook for Mistral experiments
- add universal runner run_llm_experiment.py with JSON-config driven LLM training / generation
- add configs for gpt, gpt2, llama (training/generation)
- remove individual train/generate scripts for each model
- update README with simple how-to for experiments block
BREAKING CHANGE: all llm_only experiments now run only through run_llm_experiment.py; legacy scripts removed
- Added LLaMA model architecture with RMSNorm and SwiGLU activation
- Implemented Rotary Positional Embeddings (RoPE) for better positional encoding
- Created training script for LLaMA with BPE tokenizer
- Fixed matplotlib dependency version in uv.lock
- Added LLaMA module initialization
The implementation includes:
- TokenEmbeddings, HeadAttention, MultiHeadAttention with RoPE support
- RMSNorm normalization layer
- SwiGLU feed-forward activation
- Cached decoder implementation for efficient generation
- Add LLM library with GPT model implementation
- Add hf-proxy for HuggingFace integration
- Add experiments for training and generation
- Add comprehensive documentation and examples
- Configure uv workspace with proper dependencies
