| --- |
| license: cc0-1.0 |
| task_categories: |
| - other |
| language: |
| - en |
| tags: |
| - chess |
| - stockfish |
| - distillation |
| - policy |
| - value |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - split: validation |
| path: val/val-*.parquet |
| --- |
| |
| # xLeverd/chess — Stockfish distillation dataset |
|
|
| Synthetic chess positions for supervised policy/value distillation. |
|
|
| ## What is inside |
|
|
| - **Positions:** short self-play games at weak Stockfish skill levels (3–7), |
| i.e. around the SF5 target band. Random opening plies, then engine moves. |
| - **Labels:** full-strength **Stockfish 19**, fixed depth 12, one principal |
| variation, `UCI_ShowWDL=true`. |
| - **No human games** in v1. Everything is generated and labeled on CPU. |
|
|
| ## Fields |
|
|
| | Column | Type | Meaning | |
| |---|---|---| |
| | `fen` | string | Full FEN after python-chess normalization | |
| | `move_uci` | string | Best move from Stockfish, UCI | |
| | `move_san` | string | Same move in SAN | |
| | `cp` | int16 | Centipawn score from side-to-move POV (clamped ±30000) | |
| | `mate` | int8 | Mate distance from side-to-move POV (nullable) | |
| | `wdl` | int8 | 2 = win, 1 = draw, 0 = loss, side-to-move POV | |
| | `pv` | string | First 6 moves of the principal variation, UCI | |
| | `depth` | int16 | Depth reported by Stockfish | |
| | `nodes` | int64 | Nodes searched for the label | |
| | `source` | string | `sf_selfplay` | |
|
|
| ## Generation |
|
|
| ```bash |
| python distill.py --target 100000 --workers 8 --depth 12 --out data |
| python distill.py --target 5000 --workers 6 --depth 12 --seed 987654 --out val |
| python upload_hf.py |
| ``` |
|
|
| Teacher: Stockfish 19, `Skill Level 20`, `Hash 16`, `Threads 1`, depth 12. |
| Position generator: Stockfish `Skill Level 3..7`, depth 1–3, 6–36 plies. |
|
|
| ## Current version (v1) |
|
|
| - 100,000 train rows, 5,000 validation rows. |
| - 99,554 unique train FENs; 0 illegal moves / 0 SAN mismatches in validation. |
| - Generated at ~156 positions/s on 8 CPU workers. |
| - Median position length: 14 plies; WDL split: 65% draw, 21% win, 14% loss |
| from side-to-move POV. |
|
|
| ## Intended use |
|
|
| Policy/value distillation for a small chess model, then optional MCTS. |
| The labels are Stockfish outputs; they are not human annotations. |
|
|
| ## Baseline (v1, 100k train rows) |
|
|
| - Model: ResNet 128×10, 3.0M params, 20 planes, 4096 policy logits. |
| - 10 epochs on RTX 2060: best validation top-1 **36.1%**, top-5 **71.6%**, |
| value MAE **0.223**, WDL accuracy **0.693**. |
| - Play vs Stockfish Skill 5 with one-ply value search: **0W 0D 4L**. |
| - Diagnosis: overfits after ~4 epochs on 100k; one-ply value search does not |
| see immediate recaptures. Next: 500k+ positions, 8-symmetry augmentation, |
| 2-ply/MCTS. |
|
|
| ## Files |
|
|
| - `distill.py` — CPU Stockfish distillation generator. |
| - `prepare.py` — FEN → 20 planes / 4096 policy / value targets. |
| - `model.py`, `train.py` — ResNet policy/value baseline. |
| - `play.py` — model vs Stockfish arena. |
| - `kaggle_distill.ipynb` — CPU Kaggle notebook for dataset generation. |
|
|
| ## Limitations |
|
|
| - Positions are synthetic and biased toward weak-engine self-play. |
| - Depth 12 is strong but not perfect; tactical noise exists. |
| - No endgame tablebase labels in v1. |
| - `mate` may be entirely null in a shard; cast at training time if needed. |
|
|
| ## License |
|
|
| CC0-1.0 for the generated dataset. Stockfish itself is GPLv3; only its |
| output labels are stored here. |
|
|
