autoresearch-mllisted

Autonomous LLM training optimization with GPU support. Runs 5-minute training experiments, measures val_bpb, keeps improvements or reverts — repeat forever. Use this skill when the user asks to "train a model autonomously", "optimize LLM training", "run ML experiments", "autoresearch with GPU", "optimize val_bpb", "autonomous ML training", "LLM pretraining loop", "setup ML autoresearch", "set up ML autoresearch on my GPU", "GPU training experiments", "pretrain from scratch", "speed up training", "lower my loss", "GPU optimization", "CUDA training", or mentions "train.py", "prepare.py", "bits per byte", "val_bpb", "NVIDIA GPU training", "RTX 3090/4090/5090 training", "H100 training", "autonomous model training", "consumer GPU training", "low VRAM training". Always use this skill when the user wants to autonomously optimize any ML training metric.
proyecto26/autoresearch-ai-plugin · ★ 12 · AI & Automation · score 77

Install: claude install-skill proyecto26/autoresearch-ai-plugin

# 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.