---
license: mit
tags:
- chess
- game-ai
- pytorch
- safetensors
library_name: transformers
---

# ChessLC0 Chess Model

This is BT4, the model behind LeelaChessZero Engine, one of the best Neural Network based engine available. This model is way worse than stockfish but constitute one of the best 0 search heuristics out there.
For stronger play, reducing temperature T (lower is stronger) is suggested.

## Model Description

The ChessLC0 model is a transformer-based architecture designed for chess gameplay. It can:
- Predict the next best move given a move history (requires 7 prior boards)
- Evaluate chess positions
- Generate move probabilities

**Important**: This model requires move history (7 prior boards) to work properly. You must provide a list of UCI moves representing the game history.

## Please Like if this model is useful to you :)

A like goes a long way !

## Usage

```python
import torch
from transformers import AutoModel

model = AutoModel.from_pretrained("Maxlegrec/ChessLC0", trust_remote_code=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Example usage with move history (model requires 7 prior boards)
# This sequence provides enough history for the model
move_history = [
    "e2e4", "e7e5", "g1f3", "b8c6", "f1b5", "a7a6", "b5a4",
    "g8f6", "e1g1", "f8e7", "f1e1", "b7b5", "a4b3", "d7d6"
]

# Sample move from policy
move = model.get_move_from_history(move_history, T=0.1, device=device)
print(f"Policy-based move: {move}")

# Get the best move using value analysis
value_move, value = model.get_best_move_value(move_history, T=0, device=device)
print(f"Value-based move: {value_move}")
print(f"Position value [black_win, draw, white_win]: {value}")

# Get position evaluation
position_value = model.get_position_value(move_history, device=device)
print(f"Position value [current_side_win, draw, opposite_side_win]: {position_value}")

# Get move probabilities
probs = model.get_move_from_history(move_history, T=1, device=device, return_probs=True)
top_moves = sorted(probs.items(), key=lambda x: x[1], reverse=True)[:5]
print("Top 5 moves:")
for move, prob in top_moves:
    print(f"  {move}: {prob:.4f}")
```

## Requirements

python-version >=3.13
cuda-version < 13.0

- torch>=2.0.0
- transformers>=4.48.1
- bulletchess>=0.4.0
- numpy>=1.21.0

## Model Architecture

- **Transformer layers**: 15
- **Hidden size**: 1024
- **Feed-forward size**: 1536
- **Attention heads**: 32
- **Vocabulary size**: 1858 (chess moves)