chessmodel

ChessModel-XPU — formal_1m checkpoint

A compact policy / WDL residual network for chess, trained by distilling Stockfish 18 MultiPV / WDL analysis. This is the formal_1m checkpoint from the ChessModel-XPU project — the main_xpu / main_cuda preset (12 residual blocks × 192 channels, 8,932,076 parameters, squeeze-and-excitation with hidden 32).

It is intended to be used together with chess rules and batched PUCT search as a neural chess engine. The network alone does not play chess; the playable engine runs MCTS-style PUCT over it. See the source repository for the search code.

Research artifact. The project optimizes for strength per unit compute, not absolute strength, and makes no Elo claim for this checkpoint. Measured strength is reported through the project's paired Stockfish evaluation matches, not asserted here.

Model details

Training

Distilled from Stockfish 18 teacher labels; trained on the companion dataset jinshuoli/chessmodel-data.

Key settings from configs/formal_1m.yaml:

Files

How to load

The checkpoint is a project-specific state_dict; you need the project's model code. Clone the repo and download the checkpoint so the path resolves unchanged:

git clone https://github.com/JinShuo-Li/ChessModel.git
cd ChessModel
hf download jinshuoli/chessmodel checkpoints/formal_1m_latest.pt --local-dir .

Load the weights:

import torch
from chess_ai.model import ChessNetwork
from chess_ai.training.checkpoint import load_checkpoint

state = torch.load("checkpoints/formal_1m_latest.pt", map_location="cpu", weights_only=False)
model = ChessNetwork(**state["architecture"])          # 12×192, SE hidden 32
load_checkpoint("checkpoints/formal_1m_latest.pt", model)
model.eval()

Run it as an engine / evaluate it through the project's CLI:

# UCI-style play (batched PUCT search)
python play.py --checkpoint checkpoints/formal_1m_latest.pt --device cuda --simulations 800

# Neural metrics on teacher shards
python evaluate.py --checkpoint checkpoints/formal_1m_latest.pt \
    --dataset data/formal_50k_validation --device cuda

Intended use and limitations

• Intended: chess-engine and reinforcement-learning research, distillation experiments, reproducibility of the project's results.
• Not a ready-to-use HF model: it cannot be run through the standard Inference API or transformers; it requires the project's search/engine code.
• Strength: deliberately modest — this is a compact research model, not a competitive engine. Do not treat it as a strong chess player.

Related

• Dataset: jinshuoli/chessmodel-data
• Source code & documentation: github.com/JinShuo-Li/ChessModel