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Pedram Razavi

PROFILE

Pedram Razavi

Worked on reliability and compatibility improvements in deep learning infrastructure, focusing on bug fixes in jeejeelee/vllm and NVIDIA/TransformerEngine. Addressed silent weight corruption during model pooling in jeejeelee/vllm by implementing robust weight cloning and loading logic, which improved model weight integrity and deployment stability. In NVIDIA/TransformerEngine, delivered a compatibility fix for FlashAttention 2, expanding support for head dimensions up to 256 across multiple GPU architectures by updating gating logic and test tolerances. Utilized Python, CUDA, and PyTorch, with an emphasis on unit testing and code quality, ensuring robust model management and broader hardware compatibility in production environments.

Overall Statistics

Feature vs Bugs

0%Features

Repository Contributions

2Total
Bugs
2
Commits
2
Features
0
Lines of code
169
Activity Months2

Your Network

1526 people

Work History

June 2026

1 Commits

Jun 1, 2026

June 2026: NVIDIA/TransformerEngine focused on robustness and compatibility of FlashAttention 2. Delivered a head-dimension compatibility fix that broadens support up to head_dim 256 across architectures, aligning with flash-attn gating and reducing edge-case failures. Replaced the exact-match compute capability allowlist with a >= sm80 range, validated on hardware with head_dim=256 (covering sm103, sm89, sm86, and other supported GPUs). Updated tests by loosening kv-cache tolerances for head_dim>128 to reflect real-world behavior. Included pre-commit auto-fixes to maintain code quality. All changes tracked under PR with commit 5535b09823b413596414e338c8f4dcd1a5fed463.

April 2026

1 Commits

Apr 1, 2026

Month: 2026-04 — Focused on reliability improvements in jeejeelee/vllm by fixing weight integrity during model pooling with buffer-reusing iterators. Implemented a robust clone/load path to prevent silent weight corruption, significantly strengthening model weight management and deployment stability.

Activity

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Quality Metrics

Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%

Skills & Technologies

Programming Languages

Python

Technical Skills

CUDADeep LearningMachine LearningPyTorchmachine learningunit testing

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

jeejeelee/vllm

Apr 2026 Apr 2026
1 Month active

Languages Used

Python

Technical Skills

PyTorchmachine learningunit testing

NVIDIA/TransformerEngine

Jun 2026 Jun 2026
1 Month active

Languages Used

Python

Technical Skills

CUDADeep LearningMachine LearningPyTorch