| --- |
| language: en |
| license: mit |
| tags: |
| - chess |
| - transformer |
| - custom-architecture |
| pipeline_tag: robotics |
| --- |
| |
| # ChessDumb-2.5M (`dumbc`) |
|
|
| **ChessDumb-2.5M** is a hyper-expressive, ultra-compact Chess AI architecture designed for extreme representation capacity with only **1.69M parameters**. |
|
|
| ## Key Features |
| - **DumbChessRetina**: Non-Euclidean grid folding with 4-axis directional convolutions and ray-tracing threat embeddings. |
| - **DumbAttention**: 4-way fused attention featuring Hadamard tensor braids, 3-body trinity tensor coupling, material differential biases, and dynamic temperature Softmax. |
| - **DumbFractalFFN**: Channel-shuffled polynomial interaction FFN. |
| - **DumbRecurrentEngine**: Virtual 15-layer deep thinking engine powered by a 5-layer parameter-reusing loop with step embeddings. |
|
|
| ## Usage |
| ```python |
| import torch |
| from transformers import AutoModel, AutoTokenizer |
| |
| model = AutoModel.from_pretrained("YOUR_USERNAME/ChessDumb", trust_remote_code=True) |
| tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/ChessDumb", trust_remote_code=True) |
| |
| # Dummy Forward Test |
| board = torch.randint(0, 14, (1, 64)) |
| mat_diff = torch.randn(1, 64, 64) |
| mat_weights = torch.randn(1, 64) |
| |
| outputs = model(board_state=board, mat_diff_matrix=mat_diff, material_weights=mat_weights) |
| print("Policy shape:", outputs["policy_matrix"].shape) # (1, 64, 64) |
| print("Value shape:", outputs["value_logits"].shape) # (1, 3) |
| |
| ``` |
|
|
