NVIDIA-NeMo/Megatron-Bridge
Feb 2026 – Feb 2026
1 Month active
Languages Used
BashMarkdownPython
Technical Skills
AIComputer VisionDeep LearningMachine LearningModel Fine-tuningNLP
kamran-nvidia
PROFILE
Kamran-nvidia
Kourosh Jafari-Sadegh contributed to the NVIDIA-NeMo/Megatron-Bridge repository by developing end-to-end documentation and tooling for two vision-language models, Ministral 3 and Qwen 3. He engineered Python scripts to support multimodal inference, checkpoint conversion, and fine-tuning workflows, while updating configuration files to enable training of the vision projection layer. His work included detailed Markdown documentation and architecture guides, improving onboarding and enabling faster experimentation for developers. By aligning documentation references and metadata, Kourosh ensured consistency across the project. The depth of his contributions reflects strong skills in AI, computer vision, and Python scripting, with a focus on maintainable engineering practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
1,178
Activity Months1
Your Network
1513 people
Work History
February 2026
4 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for NVIDIA-NeMo/Megatron-Bridge: Key features delivered include Ministral 3 Vision-Language Model documentation with multimodal script support and inference/conversion tooling, and Qwen 3 Vision-Language Model documentation with checkpoint conversion, inference, and fine-tuning enablement, including config changes to allow training of the vision projection layer. Major bug fixed: Qwen3 VL 30b_a3b config fix to stabilize training/inference. Overall impact: improved developer onboarding and faster experimentation, enabling end-to-end workflows for two VL models. Technologies/skills demonstrated: documentation engineering, script/tooling development, model configuration tuning, and strong version-control discipline across models.
4 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for NVIDIA-NeMo/Megatron-Bridge: Key features delivered include Ministral 3 Vision-Language Model documentation with multimodal script support and inference/conversion tooling, and Qwen 3 Vision-Language Model documentation with checkpoint conversion, inference, and fine-tuning enablement, including config changes to allow training of the vision projection layer. Major bug fixed: Qwen3 VL 30b_a3b config fix to stabilize training/inference. Overall impact: improved developer onboarding and faster experimentation, enabling end-to-end workflows for two VL models. Technologies/skills demonstrated: documentation engineering, script/tooling development, model configuration tuning, and strong version-control discipline across models.
February 2026
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
BashMarkdownPython
Technical Skills
AIComputer VisionDeep LearningMachine LearningModel Fine-tuningNLPPython Scriptingdocumentationmachine learningmodel training
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline