quic/efficient-transformers
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
AI Framework DevelopmentDeep LearningMachine LearningModel OptimizationPythonPython programming
Amit Raj
PROFILE
Amit Raj
Worked on the quic/efficient-transformers repository to enhance diffusion-model readiness and optimize performance for Efficient Transformers. Developed integration of Diffusers architecture, enabling robust text-to-image generation workflows through JSON-based configuration, performance benchmarking, and parallel compilation. Improved the Flux attention mechanism by implementing blocking support, which increased throughput and computational efficiency. Expanded ONNX subfunction hashing test coverage to ensure correct hash variation when toggling the use_onnx_subfunction flag, reducing the risk of regressions during model export. Leveraged Python, deep learning, and model optimization skills to deliver features that accelerate diffusion-model adoption and strengthen the reliability of transformer-based AI frameworks.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
4,104
Activity Months1
Your Network
227 peopleSame Organization
@qti.qualcomm.com186
Shared Repositories
41
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Work History
December 2025
3 Commits • 2 Features
Dec 1, 2025December 2025 achieved diffusion-model readiness and performance improvements for Efficient Transformers. Delivered Diffusers architecture integration with JSON-based configuration, performance benchmarking, and parallel compilation support to enable robust text-to-image generation workflows. Implemented blocking for Flux attention to boost throughput and efficiency. Expanded ONNX subfunction hashing test coverage to verify correct hashing behavior when toggling use_onnx_subfunction, reducing regression risk. Overall impact: accelerates diffusion-model use, improves runtime efficiency, and strengthens release confidence through better tests.
3 Commits • 2 Features
Dec 1, 2025December 2025 achieved diffusion-model readiness and performance improvements for Efficient Transformers. Delivered Diffusers architecture integration with JSON-based configuration, performance benchmarking, and parallel compilation support to enable robust text-to-image generation workflows. Implemented blocking for Flux attention to boost throughput and efficiency. Expanded ONNX subfunction hashing test coverage to verify correct hashing behavior when toggling use_onnx_subfunction, reducing regression risk. Overall impact: accelerates diffusion-model use, improves runtime efficiency, and strengthens release confidence through better tests.
December 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AI Framework DevelopmentDeep LearningMachine LearningModel OptimizationPythonPython programmingdeep learningmachine learningmodel exporttestingtransformer models
Repositories Contributed To
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