metadata
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
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 for the LLM Course Chess Challenge.