Instructions to use datafreak/laya-chess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Laya
How to use datafreak/laya-chess with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
final: step 32000, 2048000 examples, eval {'val_loss': 1.3315, 'val_wp_mae': 0.082, 'top_move_acc': 0.26666666666666666, 'step': 32000}
Browse files- README.md +2 -2
- chess_meta.json +68 -64
- model.safetensors +1 -1
- train_state.pt +1 -1
README.md
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for each legal move, a Laya `score` question predicts the side to move's win chance over 10 levels.
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The engine plays the move with the highest expected win chance (no search).
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- checkpoint: `
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- latest eval: `{'val_loss': 1.
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- encoding / levels / prompt template: see `chess_meta.json`
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- code: github.com/devroopsaha744/LayaChess
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for each legal move, a Laya `score` question predicts the side to move's win chance over 10 levels.
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The engine plays the move with the highest expected win chance (no search).
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+
- checkpoint: `final` (step 32000, 2,048,000 training examples)
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- latest eval: `{'val_loss': 1.3315, 'val_wp_mae': 0.082, 'top_move_acc': 0.26666666666666666, 'step': 32000}`
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- encoding / levels / prompt template: see `chess_meta.json`
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- code: github.com/devroopsaha744/LayaChess
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chess_meta.json
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{
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"n_levels": 10,
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"crit": [
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"0-10%",
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"crit": [
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{
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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-
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size 842609220
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size 842609220
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train_state.pt
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version https://git-lfs.github.com/spec/v1
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