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
Download chess_meta.json from datafreak/laya-chess: direct link, hf CLI and curl.
- Browser
- Download file 3.42 kB
-
https://huggingface.co/datafreak/laya-chess/resolve/main/chess_meta.json
- Command line
-
hf download hf://datafreak/laya-chess/chess_meta.json
-
curl -L -o chess_meta.json https://huggingface.co/datafreak/laya-chess/resolve/main/chess_meta.json
3.42 kB
| { | |
| "tag": "final", | |
| "final": true, | |
| "step": 32000, | |
| "consumed": 2048000, | |
| "n_levels": 10, | |
| "crit": [ | |
| "0-10%", | |
| "10-20%", | |
| "20-30%", | |
| "30-40%", | |
| "40-50%", | |
| "50-60%", | |
| "60-70%", | |
| "70-80%", | |
| "80-90%", | |
| "90-100%" | |
| ], | |
| "ins_template": "{side} plays {san} ({piece} {frm}-{to}{extra}). Win chance for {side}?", | |
| "state_format": "piece lists v2", | |
| "cfg": { | |
| "MODEL_ID": "convaiinnovations/laya", | |
| "N_TRAIN_SHARDS": 2, | |
| "MAX_TRAIN_RECORDS": null, | |
| "N_VAL_RECORDS": 4000, | |
| "N_VAL_POSITIONS": 300, | |
| "N_LEVELS": 10, | |
| "LABEL_SIGMA": 0.6, | |
| "BATCH_SIZE": 32, | |
| "GRAD_ACCUM": 2, | |
| "LR_ENCODER": 4e-05, | |
| "LR_HEAD": 0.0002, | |
| "WARMUP_STEPS": 150, | |
| "MIN_LR_FRAC": 0.05, | |
| "WEIGHT_DECAY": 0.01, | |
| "NUM_WORKERS": 4, | |
| "TIME_BUDGET_H": 11.0, | |
| "CKPT_EVERY_MIN": 45, | |
| "EVAL_EVERY_STEPS": 2000, | |
| "LOG_EVERY_STEPS": 50, | |
| "SAVE_OPTIMIZER": true, | |
| "RESUME_FROM": "/tmp/resume_ckpt", | |
| "RESUME_LR_SCALE": 0.5, | |
| "HF_REPO_NAME": "laya-chess", | |
| "SEED": 0 | |
| }, | |
| "history": [ | |
| { | |
| "step": 29700, | |
| "loss": 1.3451 | |
| }, | |
| { | |
| "step": 29750, | |
| "loss": 1.3177 | |
| }, | |
| { | |
| "step": 29800, | |
| "loss": 1.3399 | |
| }, | |
| { | |
| "step": 29850, | |
| "loss": 1.3225 | |
| }, | |
| { | |
| "step": 29900, | |
| "loss": 1.3635 | |
| }, | |
| { | |
| "step": 29950, | |
| "loss": 1.3372 | |
| }, | |
| { | |
| "step": 30000, | |
| "loss": 1.3466 | |
| }, | |
| { | |
| "val_loss": 1.3347, | |
| "val_wp_mae": 0.0836, | |
| "top_move_acc": 0.26666666666666666, | |
| "step": 30000 | |
| }, | |
| { | |
| "step": 30050, | |
| "loss": 1.3223 | |
| }, | |
| { | |
| "step": 30100, | |
| "loss": 1.3455 | |
| }, | |
| { | |
| "step": 30150, | |
| "loss": 1.3595 | |
| }, | |
| { | |
| "step": 30200, | |
| "loss": 1.353 | |
| }, | |
| { | |
| "step": 30250, | |
| "loss": 1.336 | |
| }, | |
| { | |
| "step": 30300, | |
| "loss": 1.3472 | |
| }, | |
| { | |
| "step": 30350, | |
| "loss": 1.3322 | |
| }, | |
| { | |
| "step": 30400, | |
| "loss": 1.3222 | |
| }, | |
| { | |
| "step": 30450, | |
| "loss": 1.3287 | |
| }, | |
| { | |
| "step": 30500, | |
| "loss": 1.3303 | |
| }, | |
| { | |
| "step": 30550, | |
| "loss": 1.3306 | |
| }, | |
| { | |
| "step": 30600, | |
| "loss": 1.3406 | |
| }, | |
| { | |
| "step": 30650, | |
| "loss": 1.3396 | |
| }, | |
| { | |
| "step": 30700, | |
| "loss": 1.3391 | |
| }, | |
| { | |
| "step": 30750, | |
| "loss": 1.3411 | |
| }, | |
| { | |
| "step": 30800, | |
| "loss": 1.3317 | |
| }, | |
| { | |
| "step": 30850, | |
| "loss": 1.336 | |
| }, | |
| { | |
| "step": 30900, | |
| "loss": 1.3278 | |
| }, | |
| { | |
| "step": 30950, | |
| "loss": 1.346 | |
| }, | |
| { | |
| "step": 31000, | |
| "loss": 1.3564 | |
| }, | |
| { | |
| "step": 31050, | |
| "loss": 1.3447 | |
| }, | |
| { | |
| "step": 31100, | |
| "loss": 1.3781 | |
| }, | |
| { | |
| "step": 31150, | |
| "loss": 1.3377 | |
| }, | |
| { | |
| "step": 31200, | |
| "loss": 1.3316 | |
| }, | |
| { | |
| "step": 31250, | |
| "loss": 1.3476 | |
| }, | |
| { | |
| "step": 31300, | |
| "loss": 1.3544 | |
| }, | |
| { | |
| "step": 31350, | |
| "loss": 1.3401 | |
| }, | |
| { | |
| "step": 31400, | |
| "loss": 1.36 | |
| }, | |
| { | |
| "step": 31450, | |
| "loss": 1.3304 | |
| }, | |
| { | |
| "step": 31500, | |
| "loss": 1.3174 | |
| }, | |
| { | |
| "step": 31550, | |
| "loss": 1.3442 | |
| }, | |
| { | |
| "step": 31600, | |
| "loss": 1.3621 | |
| }, | |
| { | |
| "step": 31650, | |
| "loss": 1.3217 | |
| }, | |
| { | |
| "step": 31700, | |
| "loss": 1.3186 | |
| }, | |
| { | |
| "step": 31750, | |
| "loss": 1.329 | |
| }, | |
| { | |
| "step": 31800, | |
| "loss": 1.333 | |
| }, | |
| { | |
| "step": 31850, | |
| "loss": 1.3585 | |
| }, | |
| { | |
| "step": 31900, | |
| "loss": 1.3238 | |
| }, | |
| { | |
| "step": 31950, | |
| "loss": 1.3538 | |
| }, | |
| { | |
| "step": 32000, | |
| "loss": 1.3216 | |
| }, | |
| { | |
| "val_loss": 1.3315, | |
| "val_wp_mae": 0.082, | |
| "top_move_acc": 0.26666666666666666, | |
| "step": 32000 | |
| }, | |
| { | |
| "val_loss": 1.3315, | |
| "val_wp_mae": 0.082, | |
| "top_move_acc": 0.26666666666666666, | |
| "step": 32000 | |
| } | |
| ] | |
| } |