|
Download docs/scripts.md from jdsb06/meta-r2: direct link, hf CLI and curl.
- Browser
- Download file 2.61 kB
-
https://huggingface.co/spaces/jdsb06/meta-r2/resolve/main/docs/scripts.md
- Command line
-
hf download hf://spaces/jdsb06/meta-r2/docs/scripts.md
-
curl -L -o scripts.md https://huggingface.co/spaces/jdsb06/meta-r2/resolve/main/docs/scripts.md
2.61 kB
| # Scripts reference | |
| **Directory:** `scripts/` | |
| --- | |
| ## `scripts/train_trl.py` β main training | |
| Full GRPO training β single-step curriculum and episodic curriculum. See [train_trl.md](train_trl.md) for complete reference. | |
| ```bash | |
| LIFESTACK_NO_UNSLOTH=1 python scripts/train_trl.py # full 5-stage curriculum | |
| python scripts/train_trl.py --dry-run # pipeline check, CPU OK | |
| python scripts/train_trl.py --resume # resume from checkpoint | |
| python scripts/train_trl.py --episode-train # episodic mode | |
| python scripts/train_trl.py --push-to-hub --hub-model-id ... # upload after training | |
| ``` | |
| --- | |
| ## `scripts/eval.py` β random baseline | |
| Runs a uniform random policy for N episodes and reports mean/std reward. No trained model, no GPU, no API key needed. Use this to establish a reward floor before GRPO runs or to verify env correctness after code changes. | |
| ```bash | |
| python scripts/eval.py # 10 episodes, all domains | |
| python scripts/eval.py --episodes 20 --domain flight_crisis | |
| python scripts/eval.py --episodes 5 --verbose # per-step output | |
| ``` | |
| --- | |
| ## `scripts/plot_training.py` β plot generation | |
| Parses training logs (`train_run_v*.log`) and generates matplotlib plots: reward curve, loss curve, per-component reward breakdown, and 4-panel summary. Supports multiple log formats. | |
| ```bash | |
| python scripts/plot_training.py --log train_run_v4.log --output-dir plots/ | |
| ``` | |
| --- | |
| ## `scripts/smoke_test.py` | |
| Fast pipeline check: import validation + one `reset()` + one `step()`. No GPU, no downloads. | |
| ```bash | |
| python scripts/smoke_test.py | |
| ``` | |
| Also called by `setup.sh` at the end of the install process. | |
| --- | |
| ## `scripts/upload_hf_model_cards.py` | |
| Uploads model cards and training artifacts to HuggingFace model repositories: | |
| - `docs/HF_MODEL_CARD_V4.md` β `jdsb06/lifestack-grpo-v4/README.md` | |
| - `docs/HF_MODEL_CARD_V1.md` β `jdsb06/lifestack-grpo/README.md` | |
| - `train_run_v1.log` β `jdsb06/lifestack-grpo/` | |
| - Relevant plots β `jdsb06/lifestack-grpo-v4/plots/` | |
| ```bash | |
| python scripts/upload_hf_model_cards.py # requires HF_TOKEN or huggingface-cli login | |
| ``` | |
| --- | |
| ## `scripts/run_episode.py` | |
| Runs one full episode with the `LifeStackAgent` (GRPO model or Groq API fallback). Requires credentials configured. | |
| ```bash | |
| python scripts/run_episode.py | |
| python scripts/run_episode.py --difficulty 3 --verbose | |
| ``` | |
| --- | |
| ## Related files | |
| - `docs/train_trl.md` β full train_trl.py reference | |
| - `docs/eval.md` β eval.py reference | |
| - `docs/training_guide.md` β end-to-end training guide | |