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.
The better the models, the fewer guardrails they will need. However, the more you know about the process the more useful the guardrails are. Guardrails may become less useful over time, but you pay for it in uncertainty and likely token usage.
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.
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