huggingface/skills
Jan 2026 – Jan 2026
1 Month active
Languages Used
Python
Technical Skills
Deep LearningFull Stack DevelopmentHugging Face TransformersMachine LearningPythonUnsloth
Maxime Labonne
PROFILE
Maxime Labonne
Developed a practical fine-tuning workflow for the LFM2.5 model in the huggingface/skills repository, focusing on accelerating model customization and experimentation. Leveraged Python and deep learning frameworks, integrating Unsloth optimizations to streamline training processes. The work included creating an example script, updating documentation, and refining training configurations to support both epoch-based and step-based approaches. Enhanced dataset format handling and parameter flexibility, reducing onboarding time for new users and enabling faster adaptation to downstream tasks. No major bugs were reported during this period, reflecting a focused engineering effort on feature delivery and workflow improvements within the machine learning and full stack context.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
539
Activity Months1
Your Network
12 peopleWork History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for huggingface/skills. Delivered a practical fine-tuning workflow for LFM2.5 with Unsloth optimizations, including an example script, updated documentation, and more flexible training configurations. The update supports both epoch-based and step-based training and improves dataset format handling, enabling faster experimentation and time-to-value. No major bugs reported this period. This work reduces onboarding time for new users and accelerates model customization for downstream tasks.
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for huggingface/skills. Delivered a practical fine-tuning workflow for LFM2.5 with Unsloth optimizations, including an example script, updated documentation, and more flexible training configurations. The update supports both epoch-based and step-based training and improves dataset format handling, enabling faster experimentation and time-to-value. No major bugs reported this period. This work reduces onboarding time for new users and accelerates model customization for downstream tasks.
January 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningFull Stack DevelopmentHugging Face TransformersMachine LearningPythonUnsloth
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