- Added explicit support and usage examples for Mistral and LLaMA architectures in both root and llm/ READMEs
- Updated directory structure and naming (datasets, tokenizers, mistral, hf-proxy)
- Clarified quickstart and experiments usage including config location and CLI
- Documented HuggingFace integration via and marked it as experimental
- Highlighted differences and specifics of all supported architectures
- Improved guide for launching training/generation/experiments
- Made project scope and architecture more transparent for new contributors
- 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
- 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
