jd-opensource/xllm
Sep 2025 – Dec 2025
2 Months active
Languages Used
C++
Technical Skills
Bug FixRuntime Error HandlingTensor ParallelismC++ developmentdeep learningmultimodal machine learning
wangziyue.28
PROFILE
Wangziyue.28
Over a two-month period, contributed to the jd-opensource/xllm repository by enhancing the Video Language Model (VLM) framework with end-to-end video processing capabilities and improving system stability under tensor parallelism. Developed C++ modules for video metadata handling, decoding, and seamless integration with existing multimodal machine learning components, enabling richer video dataset support and future extensibility. Addressed a runtime error in the VLM engine by refining input handling and ensuring correct data propagation during parallel inference, which reduced production risk and improved reliability. Demonstrated expertise in C++ development, deep learning, runtime error handling, and video processing within a complex, scalable codebase.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
686
Activity Months2
Your Network
112 peopleSame Organization
@jd.com71
chenda14Member
chenjiajun79Member
chenxiaoyu8Member
cuimaoxin1Member
dengfei.59Member
Yingxu DengMember
2extliuweijian32Member
yq33victorMember
gechunfa1Member
Shared Repositories
41
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for jd-opensource/xllm. Key achievement: Delivered end-to-end video processing support in the VLM (Video Language Model) framework, including video metadata handling, decoding, and integration with existing multimodal capabilities. This enables richer content understanding for video datasets and supports future video-centric features such as search and analytics. Work is anchored by commit 758f6c22f145f77cf171e07591f53132ab5d8040 (feat: support video modal for VLM).
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for jd-opensource/xllm. Key achievement: Delivered end-to-end video processing support in the VLM (Video Language Model) framework, including video metadata handling, decoding, and integration with existing multimodal capabilities. This enables richer content understanding for video datasets and supports future video-centric features such as search and analytics. Work is anchored by commit 758f6c22f145f77cf171e07591f53132ab5d8040 (feat: support video modal for VLM).
December 2025
September 2025
1 Commits
Sep 1, 2025September 2025 monthly summary for jd-opensource/xllm: Focused on stabilizing the VLM engine under tensor parallel by fixing a runtime error and ensuring correct data propagation. This work reduces production risk, enables safe scaling of parallel inference, and improves reliability of VLM workloads.
September 2025
1 Commits
Sep 1, 2025September 2025 monthly summary for jd-opensource/xllm: Focused on stabilizing the VLM engine under tensor parallel by fixing a runtime error and ensuring correct data propagation. This work reduces production risk, enables safe scaling of parallel inference, and improves reliability of VLM workloads.
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture70.0%
Performance70.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
Bug FixC++ developmentRuntime Error HandlingTensor Parallelismdeep learningmultimodal machine learningvideo processing
Repositories Contributed To
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