Today i'm releasing chess-autocomplete, a new state-of-the-art for human move matching accuracy in chess. A chess bot that plays like a human, trained on 1.76 billion chess games.
In this blogpost I walk you through the full process from having an idea, creating a dataset, designing evaluations, and finally training a new state-of-the-art transformer model.
The methods used here have close parallels to how LLMs are trained, so if you're curious about them give this a read.
Everything is open-source:
- The datasets
- Training and inference code
- The final weights for all three model sizes
- Training curves
- Model checkpoints at multiple stages of training
PS: Want a challenge? Try beating the model, you can play against it in the blog post.
In this blogpost I walk you through the full process from having an idea, creating a dataset, designing evaluations, and finally training a new state-of-the-art transformer model.
The methods used here have close parallels to how LLMs are trained, so if you're curious about them give this a read.
Everything is open-source:
- The datasets
- Training and inference code
- The final weights for all three model sizes
- Training curves
- Model checkpoints at multiple stages of training
PS: Want a challenge? Try beating the model, you can play against it in the blog post.