Chess GPT-4.5M

Overview

Chess GPT-4.5M is a generative language model trained specifically to generate chess moves and analyze chess games. The model is based on the GPT architecture and was trained with a custom 32-token vocabulary reflecting key chess symbols and notations.

Model Details

• Architecture: GPT-based language model (GPT2LMHeadModel)
• Parameters: Approximately 4.5M parameters
• Layers: 8 transformer layers
• Heads: 4 attention heads per layer
• Embedding Dimension: 256
• Training Sequence Length: 1024 tokens per chess game
• Vocabulary: 32 tokens (custom vocabulary)

Training Data

The model was trained on tokenized chess game data prepared from the Lichess dataset. The preparation process involved:

• Tokenizing chess games using a custom 32-token vocabulary.
• Creating binary training files (train.bin and val.bin).
• Saving vocabulary information to meta.pkl.

Training Configuration

The training configuration, found in config/mac_chess_gpt.py, includes:

• Dataset: lichess_hf_dataset
• Batch Size: 2 (optimized for Mac's memory constraints)
• Block Size: 1023 (1024 including the positional embedding)
• Learning Rate: 3e-4
• Max Iterations: 140,000
• Device: 'mps' (Mac-specific settings)
• Other Settings: No dropout and compile set to False for Mac compatibility

How to Use

Generating Chess Moves

After fine-tuning, use the generation script to sample chess moves. Example commands: bash Sample from the model without a provided prompt: python sample.py --out_dir=out-chess-mac Generate a chess game sequence starting with a custom prompt: python sample.py --out_dir=out-chess-mac --start=";1.e4"

Loading the Model in Transformers

Once the model card and converted model files are pushed to the Hugging Face Hub, you can load the model using:

python from transformers import GPT2LMHeadModel, GPT2Tokenizer model = GPT2LMHeadModel.from_pretrained("your-hf-username/chess-gpt-4.5M") tokenizer = GPT2Tokenizer.from_pretrained("your-hf-username/chess-gpt-4.5M")

Note: The tokenizer uses a custom vocabulary provided in vocab.json.

Intended Use

The model is intended for:

• Generating chess move sequences.
• Assisting in automated chess analysis.
• Educational purposes in understanding language model training on specialized domains.

Limitations

• The model is trained on a relatively small (4.5M parameter) architecture and may not capture extremely complex chess strategies.
• It is specialized on chess move generation and may not generalize to standard language tasks.

Training Process Summary

1. Data Preparation: Tokenized the Lichess chess game dataset using a 32-token vocabulary.
2. Model Training: Used custom training configurations specified in config/mac_chess_gpt.py.
3. Model Conversion: Converted added checkpoint from out-chess-mac/ckpt.pt into a Hugging Face compatible format with convert_to_hf.py.
4. Repository Setup: Pushed the converted model files (including custom tokenizer vocab) to the Hugging Face Hub with Git LFS handling large files.

Acknowledgements

This model was developed following inspiration from GPT-2 and adapted for the chess domain.

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