
Contributed to the huggingface/smol-course repository by developing end-to-end Jupyter notebooks for chat model demonstrations and supervised fine-tuning workflows. Leveraged Python and Hugging Face Transformers to create reusable templates that guide users through chat model setup, tokenizer integration, and dataset conversion for tasks such as GSM8K. Enhanced the training pipeline by fixing DPO trainer configuration issues and updating the Python environment to ensure compatibility and reduce errors. Improved repository hygiene by refining Git configuration to exclude generated outputs, streamlining collaboration for researchers and engineers. The work emphasized data preprocessing, dataset processing, and practical machine learning techniques for natural language processing tasks.
December 2024 – huggingface/smol-course: Delivered end-to-end notebook-based chat and SFT demonstrations, fixed critical DPO trainer issues, and improved repository hygiene, enabling faster experimentation and cleaner workflows. These contributions provide researchers with ready-to-run templates for chat model tasks, robust training pipelines, and a streamlined repo experience for researchers and engineers.
December 2024 – huggingface/smol-course: Delivered end-to-end notebook-based chat and SFT demonstrations, fixed critical DPO trainer issues, and improved repository hygiene, enabling faster experimentation and cleaner workflows. These contributions provide researchers with ready-to-run templates for chat model tasks, robust training pipelines, and a streamlined repo experience for researchers and engineers.

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