| | ---
|
| | library_name: transformers
|
| | tags:
|
| | - chess
|
| | - llm-course
|
| | - chess-challenge
|
| | license: mit
|
| | ---
|
| |
|
| | # chess-ooooooooo
|
| |
|
| | A chess transformer model trained for the LLM Course Chess Challenge.
|
| |
|
| | ## Model Architecture
|
| |
|
| | This model uses a GPT-style transformer architecture optimized for chess move prediction:
|
| |
|
| | - **Parameters**: 948,352 (0.95M)
|
| | - **Vocabulary size**: 85
|
| | - **Embedding dimension**: 128
|
| | - **Number of layers**: 6
|
| | - **Attention heads**: 4
|
| | - **Feed-forward dimension**: 320
|
| | - **Context length**: 256
|
| | - **Dropout**: 0.1
|
| |
|
| | ## Training
|
| |
|
| | The model was trained on a subset of the Lichess 2025 dataset, focusing on learning valid chess move sequences. The architecture was carefully tuned to stay within the 1M parameter constraint while maintaining reasonable performance.
|
| |
|
| | ## Usage
|
| |
|
| | ```python
|
| | from transformers import AutoModelForCausalLM
|
| | from src.tokenizer import ChessTokenizer
|
| |
|
| | model = AutoModelForCausalLM.from_pretrained(
|
| | "LLM-course/chess-ooooooooo",
|
| | trust_remote_code=True
|
| | )
|
| | tokenizer = ChessTokenizer.from_pretrained(
|
| | "LLM-course/chess-ooooooooo",
|
| | trust_remote_code=True
|
| | )
|
| |
|
| | # Generate moves
|
| | input_text = "[BOS] WPe2e4"
|
| | input_ids = tokenizer.encode(input_text)
|
| | outputs = model.generate(input_ids, max_length=50)
|
| | predicted_moves = tokenizer.decode(outputs[0])
|
| | ```
|
| |
|
| | ## Submission
|
| |
|
| | Submitted by [etienneLefranc](https://huggingface.co/etienneLefranc) for the LLM Course Chess Challenge.
|
| |
|
