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
Contributed to the huggingface/smollm repository by developing an end-to-end video content analysis capability using Python and deep learning techniques. The work centered on implementing a SmolVLM-based inference script that automates the extraction of frames from video files, loads the appropriate model and processor, and generates textual responses to questions about the video content. Leveraging skills in computer vision, model inference, and natural language processing, the solution enables automated analysis of video data and is designed for seamless integration with existing workflows and demonstration environments. The contribution focused on delivering a robust, ready-to-use pipeline without addressing bug fixes.
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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