Summary for 2025-11: Delivered the MiniLLM Knowledge Distillation Trainer for huggingface/trl, enabling reverse-KLD-based distillation to produce smaller, efficient LLMs. Implemented core trainer with accompanying tests and documentation, and refined configuration handling with updates to the documentation index. These changes advance model compression workflows, reduce inference costs for end users, and improve reliability and discoverability of the feature.

Implemented a targeted fix to the Storycloze YAML dataset_name formatting in red-hat-data-services/lm-evaluation-harness, ensuring dataset_name values are properly formatted as strings to improve data handling reliability for Storycloze tasks. The change was committed as 8c5ca10f52034bc7d433880678081c45f6d0d782 ('fix storycloze datanames (#2409)'.) Key business value: reduces downstream parsing errors, increases task reliability, and accelerates iteration on Storycloze evaluations. Demonstrated skills in YAML data handling, debugging, Git-based change traceability, and code-quality practices. Overall impact: more robust evaluation pipelines and higher confidence in results.