Changelog

v0.1.0

Model

  • Replaced the learnable channel-wise scaling with a fixed constant.

  • Added an exponential moving average (EMA) of model weights during training.

  • Changed the final profile convolution to kernel_width=1.

  • Set the default model size to 96 filters.

  • Tuned the Muon and AdamW learning rates and weight decay values for improved convergence.

  • Best-model selection now monitors the validation profile loss rather than the total validation loss.

Training

  • Default max_jitter for fitting lowered from 500 to 50 (applied across the package’s fitting defaults).

Packaging and tooling

  • Migrated from setup.py to pyproject.toml with uv support.

  • Refactored the CLI from a monolithic script into the cherimoya_cli modular package.

  • Raised the minimum Python version to 3.10 and minimum PyTorch to 2.9.

  • Added macs3, bam2bw, bpnet-lite, triton, and joblib as dependencies.

  • Added a Sphinx documentation site hosted on Read the Docs.

v0.0.1

  • Initial release of the Cherimoya model and pipeline.

  • Includes the CheriBlock architecture and custom kernels.

  • Features a dual-optimizer training strategy (AdamW + Muon).

  • Implements a full end-to-end processing and modeling pipeline.