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Update default main net to nn-b1a57edbea57.nnue
Created by retraining the previous main net `nn-baff1edbea57.nnue` with: - some of the same options as before: ranger21, more WDL skipping - the addition of T80 nov+dec 2023 data - increasing loss by 15% when prediction is too high, up from 10% - use of torch.compile to speed up training by over 25% ```yaml experiment-name: 2560--S9-514G-T80-augtodec2023-more-wdl-skip-15p-more-loss-high-q-sk28 training-dataset: # official-stockfish#4782 - /data/S6-514G-1ee1aba5ed.binpack - /data/test80-aug2023-2tb7p.v6.min.binpack - /data/test80-sep2023-2tb7p.binpack - /data/test80-oct2023-2tb7p.binpack - /data/test80-nov2023-2tb7p.binpack - /data/test80-dec2023-2tb7p.binpack early-fen-skipping: 28 start-from-engine-test-net: True nnue-pytorch-branch: linrock/nnue-pytorch/r21-more-wdl-skip-15p-more-loss-high-q-torch-compile num-epochs: 1000 lr: 4.375e-4 gamma: 0.995 start-lambda: 1.0 end-lambda: 0.7 ``` Epoch 819 trained with the above config led to this PR. Use of torch.compile decorators in nnue-pytorch model.py was found to speed up training by at least 25% on Ampere gpus when using recent pytorch compiled with cuda 12: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch See recent main net PRs for more info on - ranger21 and more WDL skipping: official-stockfish#4942 - increasing loss when Q is too high: official-stockfish#4972 Training data can be found at: https://robotmoon.com/nnue-training-data/ Passed STC: https://tests.stockfishchess.org/tests/view/65cd76151d8e83c78bfd2f52 LLR: 2.98 (-2.94,2.94) <0.00,2.00> Total: 78336 W: 20504 L: 20115 D: 37717 Ptnml(0-2): 317, 9225, 19721, 9562, 343 Passed LTC: https://tests.stockfishchess.org/tests/view/65ce5be61d8e83c78bfd43e9 LLR: 2.95 (-2.94,2.94) <0.50,2.50> Total: 41016 W: 10492 L: 10159 D: 20365 Ptnml(0-2): 22, 4533, 11071, 4854, 28 closes official-stockfish#5056 Bench: 1351997
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