Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,86 @@
+---
+license: mit
+tags:
+  - chess
+  - transformer
+  - behavioral-cloning
+  - no-search
+language:
+  - en
+pipeline_tag: other
+---
+
+# Artoria Zero
+
+**Grandmaster-Level Chess Without Search** — A decoder-only transformer trained via behavioral cloning to predict chess moves directly from board state, with no search algorithm (no MCTS, no Alpha-Beta).
+
+Based on the approach from [arXiv:2402.04494](https://arxiv.org/abs/2402.04494).
+
+## Model Variants
+
+| Variant | d_model | Layers | Heads | Parameters | Checkpoint |
+|---------|---------|--------|-------|------------|------------|
+| **Small** | 256 | 8 | 8 | ~19M | `small/checkpoint.pt` |
+| **Mid** | 512 | 16 | 8 | ~100M | `mid/checkpoint.pt` |
+| **Large** | 1024 | 40 | 32 | ~500M | `large/checkpoint.pt` |
+
+## Architecture
+
+- **Type**: Decoder-only Transformer (LLaMA-style)
+- **Normalization**: RMSNorm (pre-norm)
+- **FFN**: SwiGLU
+- **Attention**: Bidirectional (no causal mask)
+- **Input**: FEN string tokenized to 79 ASCII tokens
+- **Output**: Dual-head — Policy (move classification, ~4544 classes) + Value (position evaluation, tanh [-1, 1])
+- **Pooling**: Mean pooling over sequence
+
+## Usage
+
+```python
+import torch
+from artoria import ChessTokenizer, GrandmasterChessModel, ChessModelConfig
+import json
+
+# Load config
+with open("small/config.json") as f:
+    config = ChessModelConfig(**json.load(f))
+
+tokenizer = ChessTokenizer()
+config.num_classes = tokenizer.num_actions
+
+model = GrandmasterChessModel(config)
+checkpoint = torch.load("small/checkpoint.pt", map_location="cpu")
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+
+# Predict move from FEN
+fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
+tokens = tokenizer.tokenize(fen).unsqueeze(0)
+
+with torch.no_grad():
+    logits, value = model(tokens)
+
+# Get best legal move
+import chess
+board = chess.Board(fen)
+best_move, best_prob = None, -1
+probs = torch.softmax(logits[0], dim=0)
+for m in board.legal_moves:
+    idx = tokenizer.action_to_class(m.uci())
+    if idx != -1 and probs[idx].item() > best_prob:
+        best_move, best_prob = m.uci(), probs[idx].item()
+
+print(f"Best move: {best_move} (confidence: {best_prob:.4f}, eval: {value.item():.4f})")
+```
+
+## Training
+
+Trained on Lichess standard chess games via behavioral cloning (imitation learning). The model learns to predict the next move played by strong players given a board position.
+
+## Source Code
+
+[github.com/ShinapriLN/artoria](https://github.com/ShinapriLN/artoria)
+
+## License
+
+MIT