huggingface/smol-course
Dec 2024 – Dec 2024
1 Month active
Languages Used
GitJSONJupyter NotebookPython
Technical Skills
Data PreprocessingDataset ProcessingFine-tuningGit ConfigurationHugging Face TransformersMachine Learning
Ashvanth.S
PROFILE
Ashvanth.s
Ashvanth contributed to the huggingface/smol-course repository by developing end-to-end Jupyter notebooks for chat model experimentation and supervised fine-tuning workflows. He created a chat templates notebook that guides users through chat model setup, tokenizer integration, and dataset conversion for tasks such as GSM8K, leveraging Python and Hugging Face Transformers. Ashvanth also built a supervised fine-tuning example using the BigCode dataset, covering data preprocessing and SFT trainer configuration. Additionally, he resolved critical issues in the DPO trainer by updating argument handling and environment settings, and improved repository hygiene by refining Git configuration to exclude generated outputs, supporting cleaner collaboration.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
3
Lines of code
453
Activity Months1
Work History
December 2024
4 Commits • 3 Features
Dec 1, 2024December 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.
4 Commits • 3 Features
Dec 1, 2024December 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
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture80.0%
Performance75.0%
AI Usage45.0%
Skills & Technologies
Programming Languages
GitJSONJupyter NotebookPython
Technical Skills
Data PreprocessingDataset ProcessingFine-tuningGit ConfigurationHugging Face TransformersMachine LearningNatural Language ProcessingPython Programming
Repositories Contributed To
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