pytorch/FBGEMM
Jun 2025 – Jun 2025
1 Month active
Languages Used
C++CUDAPython
Technical Skills
C++CUDA ProgrammingDeep LearningGPU ComputingMachine LearningPyTorch
Summer Deng
PROFILE
Summer Deng
During June 2025, Summer Deng developed NVFP4 quantization emulation kernels for FP4 quantization-aware training on LLaMa3 8B within the pytorch/FBGEMM repository. She implemented new CUDA kernels and C++ functions to enable accurate FP4 emulation, allowing researchers to prototype and benchmark quantization workflows for large language models. Her work provided a ready-to-use reference implementation, supporting experimentation with quantization techniques and facilitating performance benchmarking. By integrating deep learning, GPU computing, and quantization expertise, Summer delivered a focused feature that addressed the need for efficient FP4 QAT emulation, though the scope was limited to feature development without major bug fixes during this period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
486
Activity Months1
Your Network
2401 people
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for pytorch/FBGEMM: Delivered NVFP4 quantization emulation kernels as a reference implementation for FP4 QAT on LLaMa3 8B. Implemented new CUDA kernels and C++ functions to support FP4 emulation, enabling researchers to prototype and benchmark quantization workflows. No major bugs fixed this month. Impact: provides a ready-to-use reference for FP4 QAT studies, accelerating experimentation and potential inference efficiency improvements. Technologies demonstrated: CUDA, C++, kernel development, quantization emulation, LLaMa3 8B integration, performance benchmarking readiness.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for pytorch/FBGEMM: Delivered NVFP4 quantization emulation kernels as a reference implementation for FP4 QAT on LLaMa3 8B. Implemented new CUDA kernels and C++ functions to support FP4 emulation, enabling researchers to prototype and benchmark quantization workflows. No major bugs fixed this month. Impact: provides a ready-to-use reference for FP4 QAT studies, accelerating experimentation and potential inference efficiency improvements. Technologies demonstrated: CUDA, C++, kernel development, quantization emulation, LLaMa3 8B integration, performance benchmarking readiness.
June 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDAPython
Technical Skills
C++CUDA ProgrammingDeep LearningGPU ComputingMachine LearningPyTorchQuantization
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline