huggingface/smollm
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
Computer VisionDeep LearningMachine LearningModel InferenceNatural Language ProcessingVideo Processing
Miquel Farre
PROFILE
Miquel Farre
During November 2024, Miquel Farre developed an end-to-end video content analysis capability for the huggingface/smollm repository. He engineered a Python-based inference script leveraging SmolVLM to automate the extraction of video frames, load the appropriate model and processor, and generate textual responses to questions about video content. This solution integrated computer vision, deep learning, and natural language processing techniques to enable automated video question answering. The implementation focused on creating a robust and reusable pipeline, ready for integration with existing workflows and demos. Miquel’s work demonstrated depth in model inference and video processing, addressing practical needs in video understanding.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
156
Activity Months1
Your Network
77 peopleSame Organization
@huggingface.co58
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024Monthly summary for 2024-11 focused on the HuggingFace Smollm project contributions. Delivered a new end-to-end video content analysis capability via SmolVLM-based inference script, enabling automated frame extraction, model/processor loading, and generation of textual responses about video content. The work is anchored by a concrete commit illustrating the implemented workflow.
1 Commits • 1 Features
Nov 1, 2024Monthly summary for 2024-11 focused on the HuggingFace Smollm project contributions. Delivered a new end-to-end video content analysis capability via SmolVLM-based inference script, enabling automated frame extraction, model/processor loading, and generation of textual responses about video content. The work is anchored by a concrete commit illustrating the implemented workflow.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Computer VisionDeep LearningMachine LearningModel InferenceNatural Language ProcessingVideo Processing
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