huggingface/cookbook
Nov 2025 – Nov 2025
1 Month active
Languages Used
Python
Technical Skills
Data ScienceJupyter NotebookMachine LearningPythondata sciencemachine learning
Benjamin Bossan
PROFILE
Benjamin Bossan
Worked on stabilizing the VLM fine-tuning workflow in the huggingface/cookbook repository by addressing issues that caused runtime errors and hindered reproducibility. Focused on improving the Jupyter Notebook experience for data science and machine learning practitioners, the work involved removing a redundant get_peft_model call to prevent double application of PEFT during SFTTrainer runs with a PEFT configuration. Additionally, corrected the JSON structure within the notebook to ensure proper execution. These changes, implemented in Python, enhanced the reliability and maintainability of the fine-tuning process, resulting in a smoother and more predictable workflow for end-users conducting VLM experiments.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
0
Lines of code
44
Activity Months1
Your Network
29 peopleShared Repositories
29
Work History
November 2025
2 Commits
Nov 1, 2025In November 2025, stabilized the VLM fine-tuning workflow in huggingface/cookbook by removing a manual get_peft_model usage, correcting JSON structure, and ensuring a single PEFT application under TRL. The changes reduce runtime errors, improve training reliability, and simplify notebook execution for end-users. This work enhances reproducibility and maintains a cleaner integration with PEFT-based fine-tuning.
2 Commits
Nov 1, 2025In November 2025, stabilized the VLM fine-tuning workflow in huggingface/cookbook by removing a manual get_peft_model usage, correcting JSON structure, and ensuring a single PEFT application under TRL. The changes reduce runtime errors, improve training reliability, and simplify notebook execution for end-users. This work enhances reproducibility and maintains a cleaner integration with PEFT-based fine-tuning.
November 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ScienceJupyter NotebookMachine LearningPythondata sciencemachine learning
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