---
license: mit
library_name: pytorch
tags:
  - chess
  - transformer
  - reinforcement-learning
  - stockfish
  - policy
language:
  - en
pipeline_tag: other
---

# Chess Transformer 200M — MaxElo (policy-only)

Finetune of [`avewright/chess-transformer-200m-compact-soft`](https://huggingface.co/avewright/chess-transformer-200m-compact-soft)
for **high Elo without MCTS**: next-move prediction via legal-mask policy argmax.

## Model card

| | |
|--|--|
| Architecture | ChessTransformer ~204M (fused board encoder, 16L × 1024d × 16H) |
| Vocab | **compact** (`MOVE_VOCAB_VERSION=compact`) |
| Heads | Spatial policy + 3-class WDL value |
| Inference | Policy argmax only (no search) |
| Base | `chess-transformer-200m-compact-soft` |
| Train run | **exp189** (soft MultiPV + deep soft mix + hard depth≥15) |

### Checkpoints in this repo

| File | Meaning |
|------|---------|
| `best_model.pt` | Best blended soft holdout top-1 during deep-mix resume |
| `latest_model.pt` | Shutdown weights at step **2851** |
| `config.json` | Architecture + training metadata |
| `PROGRESS.md` | Full session write-up |
| `elo_eval.json` | Raw Elo ladder results |

Lean checkpoints contain `model_state_dict` + metadata (no optimizer).

## Training data mix

1. **Shallow soft** — MultiPV soft targets from [`avewright/exp186-sf-multipv-2m`](https://huggingface.co/datasets/avewright/exp186-sf-multipv-2m)
2. **Deep soft** — phase-balanced SF18 MultiPV from [`avewright/exp190-phase-deep-soft`](https://huggingface.co/datasets/avewright/exp190-phase-deep-soft) (~40% of soft steps)
3. **Hard ballast** — HF Stockfish-labeled stream, `min_depth ≥ 15`

Augmentation: horizontal flip on soft batches (`hflip_p=0.5`).

## Elo (pure policy)

Evaluated with `elo_eval_latest.py` vs Stockfish 18 `UCI_LimitStrength`
(50ms/move, opening book + Syzygy, 8 openings × both colors):

| Opponent Elo | Score | W–D–L |
|--------------|-------|-------|
| 1500 | 0.625 | 7–6–3 |
| 1800 | 0.438 | 4–6–6 |

**Estimated Elo ≈ 1700** (bracket 1500–1800; small sample, noisy).

## Quick load

```python
import os, torch
os.environ["MOVE_VOCAB_VERSION"] = "compact"
from play import load_model  # or elo_eval_latest.load_eval_model

model = load_model("best_model.pt", device="cuda")
model.eval()
```

Or play in the local GUI:

```bash
export MOVE_VOCAB_VERSION=compact
python play_factory_gui.py --checkpoint best_model.pt
```

## Known limits

- Plateaued on the available soft mix; more deep phase-balanced data should help
- Weaker as **Black** in the Elo sample
- Castling indices use Chess960-style UCI in the vocab; GUI converts to standard UCI for chess.js

## Citation / code

Training code: [avewright/transform](https://github.com/avewright/transform) —
`experiments/exp189_200m_maxelo_policy.py`, `docs/PROGRESS_2026-07-10.md`.