Instructions to use victorqueiroz/chessmate-net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use victorqueiroz/chessmate-net with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("victorqueiroz/chessmate-net") - Notebooks
- Google Colab
- Kaggle
teacher/v6distill-v1: v6 self-play-imitation distill (UNDERPERFORMED, ladder 1599 ~v5)
Browse files
teacher/v6distill-v1/keras_model.keras
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version https://git-lfs.github.com/spec/v1
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oid sha256:372c62aacd231aaeec045ce9423d560b7a2addddcab011d00422734c218ed1ac
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size 295790767
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teacher/v6distill-v1/metrics.json
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{
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"val_metrics": {
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"policy_top1": 0.44727495407225965,
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"policy_top3": 0.7080220453153705,
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"policy_top5": 0.8075015309246785,
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"n": 32660,
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"value_mae": 0.27902138233184814,
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"value_sign_agree": 0.6192896509491733
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},
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"best_val_loss": 2.974766254425049,
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"epochs_ran": 14,
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"train_n": 293947,
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"val_n": 32660,
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"value_scale": 400.0,
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"soft_temp_cp": 150.0,
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"arch": {
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"filters": 256,
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"blocks": 20,
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"policy_conv_filters": 48,
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"value_conv_filters": 8,
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"value_hidden": 384,
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"value_hidden2": 192,
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"policy_loss_weight": 1.0,
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"value_loss_weight": 2.0,
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"value_head": "tanh"
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},
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"params": 24608635,
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"data_mode": "selfplay_pi_z",
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"init_from": "/root/gen1.keras",
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"resumed_from": null
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}
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teacher/v6distill-v1/v6distill_ladder.json
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{
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"model_a": "/root/v6distill/keras_model.keras",
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"opponent": "stockfish-elo-ladder[1500,1700,1900,2100]",
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"mode": "stockfish_elo_ladder",
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"sims": 160,
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"mcts_batch": 16,
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"sf_nodes": 100000,
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"max_moves": 200,
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"openings": 63,
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"openings_source": "/root/openings_balanced.txt",
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"openings_used": 50,
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"pairs_per_rung": 50,
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"absolute_elo": 1598.8,
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"absolute_elo_ci95": [
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1452.6,
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1745.0
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],
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"absolute_elo_ci95_fixed": [
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1564.0,
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1633.6
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],
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"rung_spread_elo": 333.7,
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"heterogeneity_tau": 144.5,
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"heterogeneity_q": 51.6,
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"n_rungs_used": 4,
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"rungs": [
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{
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"sf_elo": 1500,
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"score_rate": 0.44,
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"net_elo": 1458.1,
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"net_elo_ci95": [
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1391.4,
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1521.8
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]
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},
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{
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"sf_elo": 1700,
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"score_rate": 0.28,
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"net_elo": 1535.9,
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"net_elo_ci95": [
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1468.9,
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1593.1
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]
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},
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{
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"sf_elo": 1900,
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"score_rate": 0.265,
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"net_elo": 1722.8,
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"net_elo_ci95": [
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1647.1,
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1785.5
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]
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},
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{
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"sf_elo": 2100,
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"score_rate": 0.145,
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"net_elo": 1791.8,
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"net_elo_ci95": [
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1684.6,
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1865.8
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]
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}
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]
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}
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