
Jagriti Sahoo contributed to the FAIR-Chem/fairchem repository by improving configuration management and data workflows over a two-month period. She addressed a fine-tuning workflow bug by replacing static placeholders with dynamic dataset names in YAML templates, ensuring that training and validation processes consistently used the intended datasets. In addition, she enhanced data management and documentation for the OC25 release, adding markdown guides and updating leaderboard references to improve data discoverability and onboarding. Her work involved Python scripting, YAML configuration, and Markdown documentation, resulting in more reliable CI pipelines, reduced configuration errors, and clearer guidance for new contributors and maintainers.

September 2025: Focused on stabilizing OC25-related work in FAIR-Chem/fairchem. Delivered critical documentation and test improvements that boost data discoverability, leaderboard accuracy, and CI reliability. Shipped OC25 release (#1500) with OC25 dataset documentation, an OC25 markdown guide, and a link-backed OMol25 leaderboard reference. Resolved a test fixture calculator setup issue to improve test stability and reduce onboarding friction for contributors.
September 2025: Focused on stabilizing OC25-related work in FAIR-Chem/fairchem. Delivered critical documentation and test improvements that boost data discoverability, leaderboard accuracy, and CI reliability. Shipped OC25 release (#1500) with OC25 dataset documentation, an OC25 markdown guide, and a link-backed OMol25 leaderboard reference. Resolved a test fixture calculator setup issue to improve test stability and reduce onboarding friction for contributors.
In August 2025, delivered a targeted fix to the fine-tuning workflow in FAIR-Chem/fairchem by replacing a static placeholder with a dynamic dataset name and updating the YAML template to apply the dataset name in both training and validation configurations. The change ensures the fine-tuning process uses the intended dataset, reducing misconfigurations and wasted compute. This work improves pipeline reliability and supports scalable dataset experimentation.
In August 2025, delivered a targeted fix to the fine-tuning workflow in FAIR-Chem/fairchem by replacing a static placeholder with a dynamic dataset name and updating the YAML template to apply the dataset name in both training and validation configurations. The change ensures the fine-tuning process uses the intended dataset, reducing misconfigurations and wasted compute. This work improves pipeline reliability and supports scalable dataset experimentation.
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