autoresearch-ml

# Autoresearch ML: Autonomous LLM Training Optimization An autonomous experiment loop for single-GPU LLM pretraining. Edit `train.py` → commit → run 5-minute training → measure `val_bpb` → keep improvement or revert → **repeat forever**. This skill is self-contained — it includes everything needed to set up and run the loop. ## Setup Phase ### 1. Copy Template Assets Copy the bundled training template to the project directory: ```bash cp ${CLAUDE_SKILL_DIR}/assets/prepare.py . cp ${CLAUDE_SKILL_DIR}/assets/train.py . cp ${CLAUDE_SKILL_DIR}/assets/pyproject.toml . cp ${CLAUDE_SKILL_DIR}/assets/program.md . ``` ### 2. Install and Prepare ```bash uv sync # Install dependencies uv run prepare.py # Download data shards, train tokenizer (~2 min) ``` ### 3. Verify GPU ```bash nvidia-smi python -c "import torch; print(f'CUDA: {torch.cuda.is_available()}, Device: {torch.cuda.get_device_name()}, VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB')" ``` ### 4. Initialize the Experiment Session 1. Create a branch: `git checkout -b autoresearch/<tag>-<date>` — use `$ARGUMENTS` as the run tag if provided, otherwise propose one based on today's date 2. Gitignore the **living session files** — critical: `git revert` fails if `autoresearch.jsonl` is tracked, and if `autoresearch.md` (which you update mid-loop) is tracked, a revert can erase your learnings: ```bash printf '%s\n' autoresearch.jsonl autoresearch.md autoresearch.ideas....