lczerolens 🔍
Portable PyTorch evaluation and chess-decision evidence for lc0-family models.
Getting Started
Install the package. Use the hub extra for the example below:
pip install "lczerolens[hub]"
Evaluate a position:
import chess
from lczerolens import LczeroEvaluator, LczeroModel
model = LczeroModel.from_hf("lczerolens/maia-1100")
evaluator = LczeroEvaluator(model)
evaluation = evaluator.evaluate(chess.Board())
print(evaluation.policy.best_move)
print(evaluation.policy["e2e4"].probability)
Examples
The documentation provides executable tutorial notebooks and the generated API reference. The tutorials cover model inputs, evaluation, chess evidence, search, model comparison, and puzzle analysis, and each one can be launched in Colab.
Contribute
See CONTRIBUTING.md. Development and test commands live there rather than in the quick-start path.
Citation
If you use lczerolens in research, cite the version you used. The canonical
metadata is in CITATION.cff.
@software{poupart_lczerolens_2026,
author = {Poupart, Yoann},
title = {LczeroLens},
version = {0.5.1},
year = {2026},
url = {https://github.com/Xmaster6y/lczerolens}
}
License
lczerolens is licensed under the MIT License. See LICENSE for details.
1 maintainer
Metadata
1 maintainer
Release files for lczerolens 0.5.1
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