google-ai-edge/mediapipe-samples
Jun 2025 – Jun 2025
1 Month active
Languages Used
JSONJupyter NotebookPython
Technical Skills
Bug FixingColaboratoryData EngineeringDeep LearningFile ManagementFine-tuning
Arian Arfaian
PROFILE
Arian Arfaian
Contributed to the google-ai-edge/mediapipe-samples repository by developing a Colab notebook for the IO Connect 2025 workshop, focused on enabling LoRA-based fine-tuning of the Gemma 3 1B model for text-to-SQL tasks. Leveraged Python and Jupyter Notebook to update generation prompts for improved accuracy and user guidance, while reorganizing the notebook structure to enhance maintainability and discoverability. Addressed repository hygiene by relocating files and ensuring clean, reproducible outputs through the removal of cell outputs and execution counts. Demonstrated proficiency in deep learning, Hugging Face Transformers, and collaborative workflows, delivering both new features and targeted bug fixes within one month.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
1
Lines of code
20,762
Activity Months1
Your Network
21 peopleWork History
June 2025
4 Commits • 1 Features
Jun 1, 2025In June 2025, delivered a Colab-focused contribution for the google-ai-edge/mediapipe-samples project to support the IO Connect 2025 workshop. The work centered on enabling LoRA-based fine-tuning for Gemma 3 1B to generate text-to-SQL, updating generation prompts, and reorganizing the notebook for maintainability and clarity. Also performed repository hygiene improvements by relocating the Colab notebook into the appropriate directory and ensuring clean, reproducible outputs for workshop participants.
4 Commits • 1 Features
Jun 1, 2025In June 2025, delivered a Colab-focused contribution for the google-ai-edge/mediapipe-samples project to support the IO Connect 2025 workshop. The work centered on enabling LoRA-based fine-tuning for Gemma 3 1B to generate text-to-SQL, updating generation prompts, and reorganizing the notebook for maintainability and clarity. Also performed repository hygiene improvements by relocating the Colab notebook into the appropriate directory and ensuring clean, reproducible outputs for workshop participants.
June 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability95.0%
Architecture90.0%
Performance90.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPython
Technical Skills
Bug FixingColaboratoryData EngineeringDeep LearningFile ManagementFine-tuningGoogle ColabHugging Face DatasetsHugging Face TRLHugging Face TransformersJupyter NotebooksLarge Language ModelsLoRAMachine LearningMediaPipe
Repositories Contributed To
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