jd-opensource/xllm
Sep 2025 – Dec 2025
3 Months active
Languages Used
C++MarkdownPython
Technical Skills
API DevelopmentBackend DevelopmentC++C++ DevelopmentDeep LearningDocumentation
wangyunlong.115
PROFILE
Wangyunlong.115
Over a three-month period, contributed to jd-opensource/xllm by building and enhancing multimodal input capabilities for large language models, focusing on both technical depth and maintainability. Developed unified input handling for GLM models, including new modules for image and video processing with resizing, normalization, and frame sampling, using C++ and Python. Refactored model architectures to standardize tensor management and device handling, improving scalability and deployment readiness. Updated documentation to streamline onboarding and clarify usage for models like Qwen3-VL and MiMo-VL, emphasizing clear guidance for developers. The work prioritized modularity, extensibility, and efficient resource management across multimodal AI workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
4
Lines of code
2,315
Activity Months3
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
2 Commits • 1 Features
Dec 1, 2025December 2025 — Delivered core multimodal input enhancements for GLM models in jd-opensource/xllm, expanding modality support and refining data pipelines. This work increases model applicability to real-world multimodal data, reduces preprocessing overhead, and lays groundwork for scalable multimodal deployments across GLM4v and GLM46v.
2 Commits • 1 Features
Dec 1, 2025December 2025 — Delivered core multimodal input enhancements for GLM models in jd-opensource/xllm, expanding modality support and refining data pipelines. This work increases model applicability to real-world multimodal data, reduces preprocessing overhead, and lays groundwork for scalable multimodal deployments across GLM4v and GLM46v.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 — jd-opensource/xllm: Focused on documenting multimodal model support for Qwen3-VL and Qwen3-VL-MoE to improve developer onboarding and adoption. Key contributions include updating the documentation with multimodal model usage instructions and integration guidance, enabling faster real-world implementation. The work strengthens user guidance and aligns with the project’s openness and extensibility goals. No major bugs fixed this month; the primary activity was knowledge transfer through documentation. Demonstrated skills in documentation best practices, version control, and familiarity with multimodal model workflows.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 — jd-opensource/xllm: Focused on documenting multimodal model support for Qwen3-VL and Qwen3-VL-MoE to improve developer onboarding and adoption. Key contributions include updating the documentation with multimodal model usage instructions and integration guidance, enabling faster real-world implementation. The work strengthens user guidance and aligns with the project’s openness and extensibility goals. No major bugs fixed this month; the primary activity was knowledge transfer through documentation. Demonstrated skills in documentation best practices, version control, and familiarity with multimodal model workflows.
September 2025
4 Commits • 2 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for jd-opensource/xllm. The month focused on expanding multimodal capabilities with MiMo-VL support and improving model maintainability through architecture refactors, laying groundwork for scalable deployments and faster iteration cycles.
4 Commits • 2 Features
Sep 1, 2025September 2025 (2025-09) monthly summary for jd-opensource/xllm. The month focused on expanding multimodal capabilities with MiMo-VL support and improving model maintainability through architecture refactors, laying groundwork for scalable deployments and faster iteration cycles.
September 2025
Activity
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Quality Metrics
Correctness91.4%
Maintainability90.8%
Architecture90.0%
Performance88.6%
AI Usage31.4%
Skills & Technologies
Programming Languages
C++MarkdownPython
Technical Skills
API DevelopmentAPI integrationBackend DevelopmentC++C++ DevelopmentDeep LearningDocumentationImage ProcessingModel RefactoringNetwork ProgrammingPyTorchPython Developmentdeep learningdocumentationimage processing
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