---
title: CERNenv Trainer
emoji: ⚛️
colorFrom: indigo
colorTo: pink
sdk: docker
suggested_hardware: a100x4
suggested_storage: medium
pinned: false
license: bsd-3-clause
short_description: GRPO trainer for CERNenv (Unsloth + LoRA, A100)
---

# CERNenv Trainer (Hugging Face Space, A100)

Fine-tunes a small instruction-tuned LLM (Large Language Model) to act as
an LHC (Large Hadron Collider) physicist inside the **CERNenv** OpenEnv
environment using **GRPO** (Group-Relative Policy Optimization),
**Unsloth**, and **LoRA** (Low-Rank Adaptation).

## Hardware

- Recommended: **4× A100 (`a100x4`, 320 GB VRAM, ~$10/hr)**
- Single GPU also supported: `a100-large` (slower, fewer episodes recommended)
- Minimum: T4 / L4 (use the Colab notebook fallback)

### Budget guidance (~$27 envelope, the default for this hackathon run)

A 1500-episode GRPO run with `MODEL_NAME=unsloth/Qwen2.5-3B-Instruct`,
`NUM_GENERATIONS=8`, `MAX_STEPS=18` typically lands as follows:

| Hardware     | $/hr  | Wall-clock | Cost (1× run) | Headroom in $27 |
| ------------ | ----- | ---------- | ------------- | --------------- |
| `a100x4`     | ~$10  | ~1.5–2 h   | ~$15–20       | 1 re-run        |
| `a100-large` | ~$4   | ~2.5–3 h   | ~$10–12       | 2+ re-runs      |
| `l40sx4`     | ~$8   | ~2 h       | ~$16          | 1 re-run        |

`a100x4` gets the trained adapters + evidence into your hands fastest; the
multi-GPU launcher (`accelerate launch --num_processes 4`) is already wired
in `_build_training_cmd`. If you want extra safety margin in case anything
needs a re-run, drop to `a100-large` — wall-clock is ~2× longer but cost
is ~50% lower, leaving you with budget for two complete attempts.

## Required Space secrets
| Secret | Purpose |
| --- | --- |
| `HF_TOKEN` | Hugging Face token with `write` access for model push |
| `HF_USERNAME` | Hub username, used as the default model-repo owner |

## Optional environment variables
| Variable | Default | Notes |
| --- | --- | --- |
| `MODEL_NAME` | `unsloth/Qwen2.5-3B-Instruct` | Any chat model Unsloth supports |
| `TOTAL_EPISODES` | `1500` | Prompts × generations rollouts |
| `DIFFICULTY` | `easy` | Starting tier when `CURRICULUM=1`; static tier when `CURRICULUM=0` |
| `CURRICULUM` | `1` | `1` enables easy→medium→hard prompt-ramp + adaptive eval-tier |
| `CURRICULUM_PROMOTE` | `0.55` | Held-out success rate that promotes the eval tier one step |
| `CURRICULUM_DEMOTE` | `0.10` | Rolling success rate that demotes the eval tier one step |
| `MAX_STEPS` | `18` | Max steps per episode |
| `NUM_GENERATIONS` | `8` | GRPO group size (bigger = better signal) |
| `NUM_GPUS` | auto-detected | `accelerate launch --num_processes` value |
| `CHECKPOINT_EVAL_STEPS` | `25` | Run a held-out eval every N updates |
| `CHECKPOINT_EVAL_EPISODES` | `8` | Episodes per mid-training eval |
| `EVAL_EPISODES` | `32` | Episodes for pre/post eval (statistical power) |
| `OUTPUT_DIR` | `runs/unsloth-grpo` | LoRA adapter output |
| `EVIDENCE_DIR` | `evidence` | Where curves, CSVs, plots are written |
| `PUSH_REPO` | `${HF_USERNAME}/cernenv-grpo-qwen2.5-3b` | Hub repo for adapters + evidence |
| `AUTOSTART` | `0` | Set to `1` to start training on Space boot |

## How to use

This Space exposes a tiny FastAPI control panel:
- `GET  /` — status + run info + **live training-progress evidence** (curves, before/after metrics, plots)
- `POST /train` — start / restart a training run
- `GET  /logs?tail=N` — live tail of `training.log`
- `GET  /metrics` — pre / post / Δ metrics JSON
- `GET  /evidence` — list of evidence artifacts on disk
- `GET  /evidence/{name}` — download an artifact (`training_curve.png`, `training_log.csv`, etc.)

### Training-progress evidence saved (and pushed to Hub)
- `training_log.csv` — per-step reward, loss, KL, lr, grad-norm
- `training_curve.png` — reward + loss vs step
- `reward_components.csv` — per-rollout terminal vs shaping reward, plus
  discovery / mass / channel / parsed-action rates per logging step.
  This is the "watch individual reward function columns" view recommended
  in the hackathon FAQ — it makes verifier hacks visible (rising mean
  reward without rising mass/channel correctness ⇒ red flag).
- `reward_components.png` — 2-panel plot rendered from the above CSV
- `checkpoint_evals.csv` — held-out eval every `CHECKPOINT_EVAL_STEPS` updates
- `checkpoint_progression.png` — mean reward + success/mass/channel accuracy vs step
- `pre_eval.jsonl` / `post_eval.jsonl` — full per-episode rollouts before vs after
- `before_after_summary.png` — pre/post bar chart with Δ annotations
- `reward_distribution.png` — pre vs post reward histogram
- `before_after_metrics.json` — machine-readable metrics + deltas
- `sample_trajectories.md` — cherry-picked pre vs post agent traces
- `curriculum_state.json` — adaptive-curriculum tier/promotion log

Click **"Start training"** in the UI, or set `AUTOSTART=1` in the Space variables to kick off immediately on boot.

When training finishes, the LoRA adapters are pushed to `PUSH_REPO`.

## Local equivalent

The same training run is reproducible locally with:

```bash
# single GPU (with curriculum)
PYTHONPATH=. python -m training.training_unsloth \
  --model_name unsloth/Qwen2.5-3B-Instruct \
  --difficulty easy --curriculum --total_episodes 1500 --max_steps 18 \
  --num_generations 8 --output_dir runs/unsloth-grpo \
  --evidence_dir evidence

# multi-GPU (e.g. 4× A100, with curriculum)
PYTHONPATH=. accelerate launch --num_processes 4 --mixed_precision bf16 \
  -m training.training_unsloth \
  --difficulty easy --curriculum \
  --total_episodes 1500 --num_generations 8 \
  --output_dir runs/unsloth-grpo --evidence_dir evidence
```