tobi/qmd
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
Data ProcessingMachine LearningModel DeploymentModel OptimizationPythonPython Programming
Shreyas Karnik
PROFILE
Shreyas Karnik
Worked on the tobi/qmd repository to enhance browser deployment readiness and improve large-model export robustness for machine learning workflows. Developed an ONNX conversion script enabling Transformer.js deployment in browsers, incorporating quantization options for optimized performance. Addressed challenges with exporting large models by introducing validation flags, input feed corrections, and support for models up to 2GB. Improved the quantization workflow through path fixes, dtype mapping, and updated documentation to align with deployment capabilities. Leveraged Python, data processing, and model optimization skills to deliver features that streamline model deployment and ensure compatibility with modern browser-based machine learning environments.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
739
Activity Months1
Your Network
30 peopleWork History
March 2026
4 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for tobi/qmd focusing on browser deployment readiness, large-model export robustness, and quantization workflow improvements. Highlights include the ONNX conversion script for Transformer.js deployment with quantization options; hardening ONNX export for large models (2GB limit) with input feed fixes and validation; quantization workflow enhancements and updated docs; and complementary documentation updates to align with deployment capabilities.
4 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for tobi/qmd focusing on browser deployment readiness, large-model export robustness, and quantization workflow improvements. Highlights include the ONNX conversion script for Transformer.js deployment with quantization options; hardening ONNX export for large models (2GB limit) with input feed fixes and validation; quantization workflow enhancements and updated docs; and complementary documentation updates to align with deployment capabilities.
March 2026
Activity
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Quality Metrics
Correctness95.0%
Maintainability85.0%
Architecture90.0%
Performance85.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ProcessingMachine LearningModel DeploymentModel OptimizationPythonPython ProgrammingPython ScriptingTransformers.js
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