jd-opensource/xllm
Aug 2025 – Apr 2026
9 Months active
Languages Used
CC++JSONCMakeprotobufPythonMarkdownCUDA
Technical Skills
C++C++ DevelopmentDistributed SystemsEmbedded SystemsJSON ParsingLLM Function Calling
Yingxu Deng
PROFILE
Yingxu Deng
Over nine months, contributed to jd-opensource/xllm by building and optimizing core inference features for large language models, focusing on cross-hardware compatibility and production stability. Developed streaming tool-call parsing, expanded model and embedding support, and integrated NPU backends using C++ and CUDA. Enhanced quantized inference reliability, improved batch decoding performance, and unified TORCH-backed layer interfaces for flexible deployment. Addressed runtime bugs and parsing robustness, refactored model architectures for maintainability, and streamlined quantization workflows. Leveraged skills in deep learning, distributed systems, and backend development to deliver features that reduced latency, increased throughput, and enabled scalable, hardware-agnostic model serving in production environments.
Overall Statistics
Feature vs Bugs
87%Features
Repository Contributions
61Total
Bugs
4
Commits
61
Features
26
Lines of code
27,767
Activity Months9
Your Network
112 peopleSame Organization
@jd.com71
chenda14Member
chenjiajun79Member
chenxiaoyu8Member
cuimaoxin1Member
dengfei.59Member
2extliuweijian32Member
yq33victorMember
gechunfa1Member
guojinrongMember
Shared Repositories
41
Work History
April 2026
3 Commits • 2 Features
Apr 1, 2026April 2026 – jd-opensource/xllm: Stability, performance, and deployment flexibility for Qwen3.5. Focused on preventing runtime errors, increasing throughput, and simplifying quantization workflows. Delivered three key items with commits as references.
3 Commits • 2 Features
Apr 1, 2026April 2026 – jd-opensource/xllm: Stability, performance, and deployment flexibility for Qwen3.5. Focused on preventing runtime errors, increasing throughput, and simplifying quantization workflows. Delivered three key items with commits as references.
April 2026
March 2026
21 Commits • 5 Features
Mar 1, 2026In March 2026, delivered substantial model support, runtime compatibility, performance optimizations, and robustness improvements for the jd-opensource/xllm project, with an emphasis on cross-hardware deployment, quantization efficiency, and test coverage. The work enabled broader model compatibility (Qwen3.5/Qwen3.5-MoE), auto-resolution of NPU runtimes, and improved initialization robustness, while achieving measurable performance gains in FP8 paths and activation/GEMM paths. This combination of features and fixes reduces deployment friction, accelerates inference, and strengthens the codebase for scalable production use.
March 2026
21 Commits • 5 Features
Mar 1, 2026In March 2026, delivered substantial model support, runtime compatibility, performance optimizations, and robustness improvements for the jd-opensource/xllm project, with an emphasis on cross-hardware deployment, quantization efficiency, and test coverage. The work enabled broader model compatibility (Qwen3.5/Qwen3.5-MoE), auto-resolution of NPU runtimes, and improved initialization robustness, while achieving measurable performance gains in FP8 paths and activation/GEMM paths. This combination of features and fixes reduces deployment friction, accelerates inference, and strengthens the codebase for scalable production use.
February 2026
4 Commits • 3 Features
Feb 1, 2026February 2026 monthly performance for jd-opensource/xllm focused on unifying the layer interface with TORCH backend support, expanding hardware compatibility via NPU tooling, and enhancing batch decoding performance for ACL graph execution. The work prioritized business value through reduced latency, broader hardware support, and improved maintainability.
4 Commits • 3 Features
Feb 1, 2026February 2026 monthly performance for jd-opensource/xllm focused on unifying the layer interface with TORCH backend support, expanding hardware compatibility via NPU tooling, and enhancing batch decoding performance for ACL graph execution. The work prioritized business value through reduced latency, broader hardware support, and improved maintainability.
February 2026
January 2026
4 Commits • 3 Features
Jan 1, 2026Month: 2026-01 — Delivered three major features in jd-opensource/xllm with measurable business value and targeted performance improvements, plus a codebase refactor to improve maintainability. Key initiatives span hardware-accelerated inference, model registry enhancements, and architectural cleanup: - NPU integration and optimization: Added wrapper for torch_npu layers with CMake support and NPU-specific attention implementations; optimized rotary embedding calculations in the NPU kernel to boost performance and reduce redundant computations. - GLM-4.7 support in reasoning detector: Extended the reasoning detector registry to handle GLM-4.7 interactions with this model. - Causal language model architecture refactor: Refactored causal LM implementations to inherit from a common base class (LlmForCausalLMImplBase), improving organization and enabling shared functionality across models.
January 2026
4 Commits • 3 Features
Jan 1, 2026Month: 2026-01 — Delivered three major features in jd-opensource/xllm with measurable business value and targeted performance improvements, plus a codebase refactor to improve maintainability. Key initiatives span hardware-accelerated inference, model registry enhancements, and architectural cleanup: - NPU integration and optimization: Added wrapper for torch_npu layers with CMake support and NPU-specific attention implementations; optimized rotary embedding calculations in the NPU kernel to boost performance and reduce redundant computations. - GLM-4.7 support in reasoning detector: Extended the reasoning detector registry to handle GLM-4.7 interactions with this model. - Causal language model architecture refactor: Refactored causal LM implementations to inherit from a common base class (LlmForCausalLMImplBase), improving organization and enabling shared functionality across models.
December 2025
10 Commits • 6 Features
Dec 1, 2025December 2025 monthly summary for jd-opensource/xllm: Delivered core GLM-4.7 model support and tooling, advanced NPU backend compatibility with wrappers for ATB/ACLNN fused operators, removal of MTP-specific requirements to enable non-MTP models, Qwen3 MOE decoder phase detection optimization, and ongoing codebase maintenance and reliability improvements. These efforts have enhanced model interoperability, backend readiness, stability, and development velocity, contributing to production-ready features and clearer documentation.
10 Commits • 6 Features
Dec 1, 2025December 2025 monthly summary for jd-opensource/xllm: Delivered core GLM-4.7 model support and tooling, advanced NPU backend compatibility with wrappers for ATB/ACLNN fused operators, removal of MTP-specific requirements to enable non-MTP models, Qwen3 MOE decoder phase detection optimization, and ongoing codebase maintenance and reliability improvements. These efforts have enhanced model interoperability, backend readiness, stability, and development velocity, contributing to production-ready features and clearer documentation.
December 2025
November 2025
10 Commits • 3 Features
Nov 1, 2025Concise monthly summary for 2025-11 highlighting core delivery, stability gains, and technical leadership across core inference services and distributed infra for jd-opensource/xllm. Business impact is measured by reduced incidents, improved model throughput, and stronger NPU/dGPU integration enabling larger scale usage.
November 2025
10 Commits • 3 Features
Nov 1, 2025Concise monthly summary for 2025-11 highlighting core delivery, stability gains, and technical leadership across core inference services and distributed infra for jd-opensource/xllm. Business impact is measured by reduced incidents, improved model throughput, and stronger NPU/dGPU integration enabling larger scale usage.
October 2025
1 Commits
Oct 1, 2025October 2025 (jd-opensource/xllm) focused on stability and reliability of the quantized inference path. No new features were released this month; the primary work centered on a critical bug fix in the Qwen3 quantized inference flow. The fix ensures normalization is applied only when quantization is active by conditioning ACLNN RMS Norm enablement on whether a quantization type is specified, eliminating a segmentation fault and stabilizing production workloads. This work reduces crash risk in deployment and improves model-serving reliability, demonstrating strong debugging and quantization-aware engineering. Technologies demonstrated include debugging complex inference paths, conditional feature toggles, and quantization-aware logic.
1 Commits
Oct 1, 2025October 2025 (jd-opensource/xllm) focused on stability and reliability of the quantized inference path. No new features were released this month; the primary work centered on a critical bug fix in the Qwen3 quantized inference flow. The fix ensures normalization is applied only when quantization is active by conditioning ACLNN RMS Norm enablement on whether a quantization type is specified, eliminating a segmentation fault and stabilizing production workloads. This work reduces crash risk in deployment and improves model-serving reliability, demonstrating strong debugging and quantization-aware engineering. Technologies demonstrated include debugging complex inference paths, conditional feature toggles, and quantization-aware logic.
October 2025
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for jd-opensource/xllm. Focused on delivering configurable thinking control in the chat template system and accelerating operator performance with a dedicated NPU backend, while tightening test reliability.
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for jd-opensource/xllm. Focused on delivering configurable thinking control in the chat template system and accelerating operator performance with a dedicated NPU backend, while tightening test reliability.
August 2025
3 Commits • 2 Features
Aug 1, 2025August 2025 monthly summary for jd-opensource/xllm. Focused on delivering streaming-enabled tool-call parsing and expanding embedding model support, with a bug fix to ensure reliability of streaming toggles. The work aligns with business goals of real-time data processing, broader model compatibility, and robust streaming pipelines.
3 Commits • 2 Features
Aug 1, 2025August 2025 monthly summary for jd-opensource/xllm. Focused on delivering streaming-enabled tool-call parsing and expanding embedding model support, with a bug fix to ensure reliability of streaming toggles. The work aligns with business goals of real-time data processing, broader model compatibility, and robust streaming pipelines.
August 2025
Activity
Loading activity data...
Quality Metrics
Correctness92.2%
Maintainability85.2%
Architecture88.6%
Performance86.0%
AI Usage34.4%
Skills & Technologies
Programming Languages
CC++CMakeCUDAJSONMarkdownPythonRustprotobuf
Technical Skills
AI DevelopmentAI model integrationAPI DesignAPI DevelopmentAPI developmentATBATB Operator IntegrationBackend DevelopmentBug FixBuild SystemsC++C++ DevelopmentC++ ProgrammingC++ developmentC++ programming
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline