| ---
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| title: CERNenv Trainer
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| emoji: ⚛️
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| colorFrom: indigo
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| colorTo: pink
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| sdk: docker
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| suggested_hardware: a100-large
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| suggested_storage: medium
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| pinned: false
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| license: bsd-3-clause
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| short_description: GRPO trainer for CERNenv (Unsloth + LoRA, A100)
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| ---
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|
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| # CERNenv Trainer (Hugging Face Space, A100)
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|
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| Fine-tunes a small instruction-tuned LLM (Large Language Model) to act as
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| an LHC (Large Hadron Collider) physicist inside the **CERNenv** OpenEnv
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| environment using **GRPO** (Group-Relative Policy Optimization),
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| **Unsloth**, and **LoRA** (Low-Rank Adaptation).
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|
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| ## Hardware
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| - Recommended: **A100 large (80 GB)**
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| - Minimum: T4 / L4 (will use a smaller model + fewer episodes)
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|
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| ## Required Space secrets
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| | Secret | Purpose |
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| | --- | --- |
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| | `HF_TOKEN` | Hugging Face token with `write` access for model push |
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| | `HF_USERNAME` | Hub username, used as the default model-repo owner |
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|
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| ## Optional environment variables
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| | Variable | Default | Notes |
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| | --- | --- | --- |
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| | `MODEL_NAME` | `unsloth/Qwen2.5-3B-Instruct` | Any chat model Unsloth supports |
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| | `TOTAL_EPISODES` | `400` | Prompts × generations rollouts |
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| | `DIFFICULTY` | `easy` | `easy` / `medium` / `hard` |
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| | `MAX_STEPS` | `18` | Steps per episode |
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| | `NUM_GENERATIONS` | `4` | GRPO group size |
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| | `OUTPUT_DIR` | `runs/unsloth-grpo` | LoRA adapter output |
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| | `PUSH_REPO` | `${HF_USERNAME}/cernenv-grpo-qwen2.5-3b` | Hub repo for adapters |
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| | `AUTOSTART` | `0` | Set to `1` to start training on Space boot |
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|
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| ## How to use
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|
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| This Space exposes a tiny FastAPI control panel:
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| - `GET /` — status + current run info
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| - `POST /train` — start / restart a training run
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| - `GET /logs` — live tail of `training.log`
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| - `GET /metrics` — reward + success-rate snapshots
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|
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| Click **"Start training"** in the UI, or set `AUTOSTART=1` in the Space variables to kick off immediately on boot.
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|
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| When training finishes, the LoRA adapters are pushed to `PUSH_REPO`.
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|
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| ## Local equivalent
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|
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| The same training run is reproducible locally with:
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|
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| ```bash
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| PYTHONPATH=. python -m training.training_unsloth \
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| --model_name unsloth/Qwen2.5-3B-Instruct \
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| --difficulty easy --total_episodes 400 --max_steps 18 \
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| --output_dir runs/unsloth-grpo
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| ```
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|
|
