Push model using huggingface_hub.

README.md

@@ -1,106 +1,16 @@
 ---
 license: mit
+pipeline_tag: tabular-classification
 tags:
+- board-games
 - chess
+- model_hub_mixin
+- pytorch_model_hub_mixin
 - reinforcement-learning
-- game-ai
-- pytorch
-library_name: transformers
-model-index:
-- name: ChessBot
-  results:
-  - task:
-      type: other
-      name: Chess Move Prediction
-    metrics:
-    - type: other
-      name: Parameters
-      value: 31.7M
-base_model_revision: main
-widget:
-- text: "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKQ w KQkq - 0 1"
-  example_title: "Starting Position"
-- text: "r1bqkb1r/pppp1ppp/2n2n2/1B2p3/4P3/5N2/PPPP1PPP/RNBQK2R w KQkq - 4 4"
-  example_title: "Italian Game"
+- transformer
 ---
 
-# ChessBot Chess Model
-
-This is a ChessBot model for chess move prediction and position evaluation.
-
-## Model Description
-
-The ChessBot model is a transformer-based architecture designed for chess gameplay. It can:
-- Predict the next best move given a chess position (FEN)
-- Evaluate chess positions
-- Generate move probabilities
-
-## Usage
-
-```python
-import torch
-from huggingface_hub import snapshot_download
-
-# Download the model files
-model_path = snapshot_download(repo_id="Maxlegrec/ChessBot")
-
-# Add to path and import
-import sys
-sys.path.append(model_path)
-from modeling_chessbot import ChessBotModel, ChessBotConfig
-
-# Load the model
-config = ChessBotConfig()
-model = ChessBotModel.from_pretrained(model_path)
-
-# Example usage
-fen = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = model.to(device)
-
-# Get the best move
-move = model.get_move_from_fen_no_thinking(fen, T=0.1, device=device)
-print(f"Policy-based move: {move}")
-
-# Get the best move using value analysis
-value_move = model.get_best_move_value(fen, T=0, device=device)
-print(f"Value-based move: {value_move}")
-
-# Get position evaluation
-position_value = model.get_position_value(fen, device=device)
-print(f"Position value [black_win, draw, white_win]: {position_value}")
-
-# Get move probabilities
-probs = model.get_move_from_fen_no_thinking(fen, 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
-
-- torch>=2.0.0
-- transformers>=4.30.0
-- python-chess>=1.10.0
-- numpy>=1.21.0
-
-## Model Architecture
-
-- **Transformer layers**: 10
-- **Hidden size**: 512
-- **Feed-forward size**: 736
-- **Attention heads**: 8
-- **Vocabulary size**: 1929 (chess moves)
-- **Total parameters**: 31,708,102 (~31.7M)
-- **Model size**: ~127MB (safetensors)
-
-## Training Data
-
-This model was trained on chess game data to learn optimal move selection and position evaluation.
-
-## Limitations
-
-- The model works best with standard chess positions
-- Performance may vary with unusual or rare positions
-- Requires GPU for optimal inference speed
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: https://github.com/user/chessbot
+- Paper: [More Information Needed]
+- Docs: [More Information Needed]

config.json

@@ -2,9 +2,7 @@
   "d_ff": 736,
   "d_model": 512,
   "max_position_embeddings": 64,
-  "model_type": "chessbot",
   "num_heads": 8,
   "num_layers": 10,
-  "transformers_version": "4.53.1",
   "vocab_size": 1929
-}
+}