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README.md

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+---
+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_fen_no_thinking(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 [black_win, draw, white_win]: {position_value}")
+
+# Get move probabilities
+probs = model.get_move_from_fen_no_thinking(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 <=3.11
+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)