December 2025 monthly summary for metatensor/metatrain: Focused on increasing accessibility of fine-tuning workflows through comprehensive documentation and practical examples. Delivered end-user guidance and YAML templates for training and evaluating fine-tuned models; no major bugs fixed this month. Overall impact includes faster experimentation, improved onboarding, and broader adoption of fine-tuning workflows. Technologies demonstrated include documentation best practices, YAML configurations for model training, and collaborative development across the team.

October 2025 monthly summary for metatensor/metatrain: Delivered critical documentation and dataset accessibility improvements, added a new PET fine-tuning example with user guidance, and fixed a transfer learning script to improve reliability. These efforts reduce onboarding time, streamline experimentation, and strengthen the robustness of fine-tuning workflows. Demonstrated strong Python development, ML tooling, and documentation practices.

September 2025 (metatensor/metatrain) focused on increasing contributor friendliness and expanding PET model capabilities. Delivered two key features: simplified contributor testing instructions and atom-based sample selection for PET features, with tests and code changes. No major user-facing bug fixes this month; maintained stability and improved test coverage. Impact: faster onboarding for contributors and more flexible feature engineering for PET models, enabling researchers to experiment with atom-level selections. Technologies/skills: Python, documentation best practices, PyTorch tensor operations, test-driven development, pytest, code review discipline.

February 2025 monthly summary for metatensor/metatensor: Delivered new difference and difference_and_mapping methods in the Python Labels API to compute set differences between Labels objects and optionally map entries to result positions. The work included changelog updates and tests for labels.py, aligned with commit 1883d108c31b35d86521d7efa1d09063a12ac1d7. No major bug fixes this month; focus was on feature delivery, test coverage, and documentation. Impact: enables advanced data comparison workflows, improves reproducibility, and broadens the API for data analysis. Technologies/skills demonstrated: Python API design, set operations, mapping logic, test-driven development, and changelog/documentation practices.