September 3, 2025 (v0.2.4)

Added automatic model downloading from HuggingFace Hub with load_model(), get_model_info(), and clear_model_cache() functions
Removed model weights from git history to reduce repository size - users should re-clone the repository
Added ability for interrupting inference with improved UI to Streamlit app

August 29, 2025 (v0.2.3)

Refactored ShEPhERD inference code to be more modular (backwards compatible).
- The original inference code can still be imported: from shepherd.inference import inference_sample
- New inference functions can be imported with:
  - from shepherd.inference import generate
  - from shepherd.inference import generate_from_intermediate_time
Inference now supports atom and bond inpainting
- generate is updated to allow atom inpainting from t=T
- generate_from_intermediate_time is specialized to allow atom inpainting from an intermediate time (T ≤ t < 0)
Inference can store full diffusion trajectories by setting return_trajectories=True during sampling.
shepherd.extract
- Added remove_side_groups_with_geometry
- Added remove_overlaps to quickly filter sampled molecules that use atom-inpainting.

Refactored ShEPhERD (aided by Matthew Cox's fork: https://github.com/mcox3406/shepherd/)
- Updated import statements: throughout repo to import directly from shepherd assuming local install.
- Fix depreciation warnings:
  - torch.load() -> torch.load(weights_only=True)
  - @torch.cuda.amp.autocast(enabled=False) -> @torch.amp.autocast('cuda', enabled=False)
- Training scripts
  - Updated src/shepherd/datasets.py for higher versions of PyG. Required changes to the batching functionality for edges (still backwards compatible).
  - Slight changes to training/train.py for upgraded versions of PyTorch Lightning.
Model checkpoints have been UPDATED for PyTorch Lightning v2.5.1
- The original checkpoints for PyTorch Lightning v1.2 can be found in previous commits (c3d5ec0 or before), the original publication Release, or at the Dropbox data link: https://www.dropbox.com/scl/fo/rgn33g9kwthnjt27bsc3m/ADGt-CplyEXSU7u5MKc0aTo?rlkey=fhi74vkktpoj1irl84ehnw95h&e=1&st=wn46d6o2&dl=0
Created a basic unconditional generation test script
Updated the environment and relevant files to be compatible with PyTorch >= v2.5.1
Bug fix for shepherd.datasets.HeteroDataset.__getitem__ where x3 point extraction should use get_x2_data

Thank you to Matthew Cox for his contributions in the updated code.