hd787/humanchess-650-750-blitz

HumanChess 650-750 Blitz

HumanChess 650-750 Blitz is a chess move policy model trained by imitation on 650-750 Elo Lichess blitz games. Given a board position, it scores a fixed chess move vocabulary, masks illegal moves, and returns a distribution over legal moves.

This is not a search engine and does not include a value head. It is intended to produce low-rated, human-like blitz moves rather than strongest-play chess.

Model

• Architecture: CNN policy
• Channels: 128
• Residual blocks: 6
• Input: 19 x 8 x 8 board tensor
• Output: fixed move-vocabulary logits, masked to legal moves at inference
• Checkpoint: checkpoints/v3-cnn-128x6-20epoch.pt
• Saved epoch: 5

Validation Metrics

Validation metrics from the saved checkpoint:

Metric	Value
NLL	1.9231
Entropy	1.8335
Top-1 accuracy	41.73%
Top-3 accuracy	69.11%
Top-5 accuracy	80.11%
Validation positions	1,434,379

Intended Use

This model is useful for experiments with human-like chess move prediction, training tools, casual chess bots, and model-vs-model comparisons. It should not be treated as a calibrated Elo engine or a tactical oracle.

Usage

Use the inference provider repository:

git clone https://github.com/hd787/ChessModelServer.git
cd ChessModelServer

Then download this checkpoint from Hugging Face and point the server at:

checkpoints/v3-cnn-128x6-20epoch.pt

Training Data

The checkpoint was trained from a v3 split of Lichess blitz games filtered to roughly 650-750 Elo players. The model is supervised by the human move played in each retained position.

Limitations

• Policy-only: no search and no value evaluation.
• Move quality is intentionally limited by the training distribution.
• The model can choose illegal-looking strategic plans even though individual moves are masked to legal moves.
• Tactical puzzle fine-tuning experiments degraded tournament performance in our launch-style tests, so this release uses the blitz imitation checkpoint.