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
Shreyas Karnik developed deployment and optimization features for the tobi/qmd repository, focusing on enabling browser-based model deployment and improving large-model export workflows. He implemented an ONNX conversion script in Python to support Transformer.js deployment, adding quantization options for efficient browser compatibility. Addressing the 2GB model size limit, he enhanced ONNX export robustness by introducing validation flags, input feed corrections, and no_post_process options. Shreyas also refined the quantization workflow, updating path handling and dtype mapping, and maintained comprehensive documentation to align with these changes. His work demonstrated depth in data processing, model deployment, and Python scripting within machine learning pipelines.
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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