sophgo/LLM-TPU
Jun 2025 – Jun 2025
1 Month active
Languages Used
C++CMake
Technical Skills
C++ DevelopmentCMake Build SystemComputer VisionDeep Learning InferenceLLMModel Integration
ruoning.yu
PROFILE
Ruoning.yu
During June 2025, Ruoning Yu developed an end-to-end Qwen2_5_VL model demo for the sophgo/LLM-TPU repository, targeting the Sophgo platform. Leveraging C++ and the CMake build system, Ruoning implemented a hardware-ready runtime demo, integrating model and image-processing headers to support evaluation workflows. A key contribution was the attention-masking utility, which calculates sequence lengths to ensure correct model behavior with variable-length inputs. The work produced a reproducible demo stack, including CMakeLists.txt and reusable header modules, enabling streamlined future deployments. This project demonstrated depth in model integration, computer vision, and deep learning inference, establishing a robust foundation for internal validation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
357,093
Activity Months1
Your Network
25 people
Work History
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 highlights: Completed an end-to-end Qwen2_5_VL Model Demo on the Sophgo Platform, adding a C++ runtime demo, build tooling, and interaction/image-processing headers to enable hardware-ready evaluation. Implemented an attention-masking sequence-length utility to ensure correct model behavior with variable input lengths. All changes are captured in two commits: 2c7b7e87d8b9a882d6ec883857cdebde2d311b80 (feat) and 4e26d3ec0c8776db1a22cda3dfb780b27e2bf8cc (fix). These workstreams delivered a reproducible demo stack (CMakeLists.txt, headers, and utility) that can be reused for future Qwen-style deployments.
2 Commits • 1 Features
Jun 1, 2025June 2025 highlights: Completed an end-to-end Qwen2_5_VL Model Demo on the Sophgo Platform, adding a C++ runtime demo, build tooling, and interaction/image-processing headers to enable hardware-ready evaluation. Implemented an attention-masking sequence-length utility to ensure correct model behavior with variable input lengths. All changes are captured in two commits: 2c7b7e87d8b9a882d6ec883857cdebde2d311b80 (feat) and 4e26d3ec0c8776db1a22cda3dfb780b27e2bf8cc (fix). These workstreams delivered a reproducible demo stack (CMakeLists.txt, headers, and utility) that can be reused for future Qwen-style deployments.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMake
Technical Skills
C++ DevelopmentCMake Build SystemComputer VisionDeep Learning InferenceLLMModel IntegrationModel Optimization
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