jd-opensource/xllm
Aug 2025 – Dec 2025
5 Months active
Languages Used
C++CMakeMarkdownPythonprotobufCUDAShell
Technical Skills
BRPCBackend DevelopmentC++Distributed SystemsLoggingNetwork Configuration
xiongjun3
PROFILE
Xiongjun3
Over five months, Xiongjun Xiong contributed core backend and distributed systems engineering to the jd-opensource/xllm repository, focusing on scalable AI model deployment and multimodal inference. He unified scheduler services, streamlined network configuration, and enhanced observability using C++ and gRPC, improving deployment speed and runtime reliability. Xiong implemented profiling and performance monitoring, introduced long-context attention masking, and delivered thread-safe multi-node processing. He also enabled GLM4v_moe multimodal support on NPU, integrating text, image, and video processing with PyTorch and CUDA. His work emphasized robust system design, production readiness, and maintainable code, addressing both feature delivery and critical bug fixes throughout.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
20Total
Bugs
4
Commits
20
Features
14
Lines of code
3,428
Activity Months5
Your Network
110 peopleSame Organization
@jd.com71
chenda14Member
chenjiajun79Member
chenxiaoyu8Member
cuimaoxin1Member
dengfei.59Member
Yingxu DengMember
2extliuweijian32Member
yq33victorMember
gechunfa1Member
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 — jd-opensource/xllm: Delivered GLM4v_moe on NPU with multimodal support (text + images + videos). Implemented new layers and adjusted architecture to process visual data alongside text, enabling real-world multimodal inference and broader deployment capabilities. The feature is backed by a targeted commit (9ea753a85fffc0e52675747b7b17eed8db302565). No major bugs fixed this month; focus was on feature delivery, code quality, and production readiness. Overall impact: expands the model’s applicability to multimedia tasks, enabling richer user experiences and potential business value in multimedia analytics, content moderation, and conversational AI across modalities. Technologies/skills demonstrated: NPU acceleration, multimodal model integration, neural architecture adjustments, codebase maintenance, and end-to-end validation readiness.
1 Commits • 1 Features
Dec 1, 2025December 2025 — jd-opensource/xllm: Delivered GLM4v_moe on NPU with multimodal support (text + images + videos). Implemented new layers and adjusted architecture to process visual data alongside text, enabling real-world multimodal inference and broader deployment capabilities. The feature is backed by a targeted commit (9ea753a85fffc0e52675747b7b17eed8db302565). No major bugs fixed this month; focus was on feature delivery, code quality, and production readiness. Overall impact: expands the model’s applicability to multimedia tasks, enabling richer user experiences and potential business value in multimedia analytics, content moderation, and conversational AI across modalities. Technologies/skills demonstrated: NPU acceleration, multimodal model integration, neural architecture adjustments, codebase maintenance, and end-to-end validation readiness.
December 2025
November 2025
6 Commits • 3 Features
Nov 1, 2025November 2025: Focused on delivering robust performance and reliability improvements for jd-opensource/xllm. Key features include TPOT profiling in disaggregated PD mode, AsyncResponseProcessor batch processing for mixed roles, and XLLM model enhancements with release stability. A major bug fix added safeguards against infinite loops in batch generation. These efforts closed critical gaps, improved runtime performance insights, and stabilized release cycles, driving higher deployment confidence and measurable efficiency gains.
November 2025
6 Commits • 3 Features
Nov 1, 2025November 2025: Focused on delivering robust performance and reliability improvements for jd-opensource/xllm. Key features include TPOT profiling in disaggregated PD mode, AsyncResponseProcessor batch processing for mixed roles, and XLLM model enhancements with release stability. A major bug fix added safeguards against infinite loops in batch generation. These efforts closed critical gaps, improved runtime performance insights, and stabilized release cycles, driving higher deployment confidence and measurable efficiency gains.
October 2025
4 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for jd-opensource/xllm focused on delivering long-context capabilities, stabilizing the runtime interfaces, simplifying setup, and hardening multi-threaded input construction. The team completed four key deliverables with clear business value: extended attention masking for longer sequences, API compatibility stabilization, streamlined setup/docs for LlmDataDist PD disaggregation, and a thread-safety fix in BatchInputBuilder.
4 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for jd-opensource/xllm focused on delivering long-context capabilities, stabilizing the runtime interfaces, simplifying setup, and hardening multi-threaded input construction. The team completed four key deliverables with clear business value: extended attention masking for longer sequences, API compatibility stabilization, streamlined setup/docs for LlmDataDist PD disaggregation, and a thread-safety fix in BatchInputBuilder.
October 2025
September 2025
6 Commits • 5 Features
Sep 1, 2025Performance-focused month for jd-opensource/xllm in Sept 2025. Delivered observability and profiling enhancements, startup TTFT profiling, and multi-node connection improvements, along with foundational work for the v0.6.0 release. A critical bug fix ensures ProfileManager initialization when disagg_pd is enabled, strengthening distributed runtime reliability and initialization latency measurements.
September 2025
6 Commits • 5 Features
Sep 1, 2025Performance-focused month for jd-opensource/xllm in Sept 2025. Delivered observability and profiling enhancements, startup TTFT profiling, and multi-node connection improvements, along with foundational work for the v0.6.0 release. A critical bug fix ensures ProfileManager initialization when disagg_pd is enabled, strengthening distributed runtime reliability and initialization latency measurements.
August 2025
3 Commits • 3 Features
Aug 1, 2025In 2025-08, jd-opensource/xllm delivered core architectural improvements and deployment conveniences, enhancing reliability, network readiness, and developer experience. Key changes include unifying prefill and decode schedulers into a single DisaggPDService, auto-detecting local IP when the host is not provided, and reducing startup verbosity by removing gflags logging. No major bugs were reported; the work increases deployment speed, simplifies distributed runtime management, and improves observability.
3 Commits • 3 Features
Aug 1, 2025In 2025-08, jd-opensource/xllm delivered core architectural improvements and deployment conveniences, enhancing reliability, network readiness, and developer experience. Key changes include unifying prefill and decode schedulers into a single DisaggPDService, auto-detecting local IP when the host is not provided, and reducing startup verbosity by removing gflags logging. No major bugs were reported; the work increases deployment speed, simplifies distributed runtime management, and improves observability.
August 2025
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Quality Metrics
Correctness87.0%
Maintainability85.6%
Architecture86.0%
Performance82.4%
AI Usage26.0%
Skills & Technologies
Programming Languages
C++CMakeCUDAMarkdownPythonShellprotobuf
Technical Skills
AI model supportBRPCBackend DevelopmentBug FixC++C++ DevelopmentC++ developmentCMakeCUDAConcurrencyContainerizationCore DevelopmentDeep LearningDevOpsDistributed Systems
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