| name | openclaw-finetuning |
| description | Use this project skill when Michael asks to fine-tune for OpenClaw, run the pipeline, build training datasets, run GRPO, create LoRA adapters, extract Claude sessions, convert Anthropic to OpenAI format, enrich OpenClaw tools, or work in the OpenClaw pipeline directory.
|
OpenClaw Fine-Tuning
Scope
Use this skill for the local fine-tuning pipeline under ~/openclaw/pipeline/.
Load references/datasets-inventory.md only when dataset variants, dataset
stats, or generated LoRA configs matter.
Pipeline
cd ~/openclaw/pipeline/
python extract_claude_sessions.py
python convert_anthropic_to_openai.py
python enrich_openclaw_tools.py
python build_training_dataset.py
Pipeline shape:
- Extract high-quality Claude Code sessions.
- Convert Anthropic
tool_use / tool_result into OpenAI tool_calls.
- Add OpenClaw tool schemas and synthetic examples.
- Build final train/valid/test splits plus LoRA config.
Training
mlx_lm_lora.train --config pipeline/training-data/openclaw-agentic/lora_config.yaml
Typical local assumptions:
- LoRA rank 16
- learning rate
5e-6
- max sequence length 32768
- batch size 1 on M3 Max constraints
GRPO
cd ~/openclaw/pipeline/
python -m grpo.run --config grpo/config.yaml --dataset prompts.jsonl
Important files:
| File | Role |
|---|
grpo/config.py | GRPOConfig |
grpo/run.py | CLI entry point |
grpo/trainer.py | Training loop |
grpo/rollout.py | Group rollouts |
grpo/rewards.py | Reward functions |
grpo/loss.py | GRPO/PPO-style loss |
Validation
cd pipeline/
pytest test_extract.py -v
pytest test_convert.py -v
pytest test_enrich.py -v
pytest test_build.py -v
