microsoft/ltp-sglang
Aug 2025 – Sep 2025
2 Months active
Languages Used
PythonC++
Technical Skills
BenchmarkingModel OptimizationObject-Oriented ProgrammingPerformance AnalysisSoftware DesignDeep Learning Frameworks
Jiamin-MS
PROFILE
Jiamin-ms
Jiamin Li developed a benchmarking framework for model metrics in the microsoft/ltp-sglang repository, enabling detailed measurement of parameters, FLOPs, and memory accesses across diverse model architectures. Using Python and C++, Jiamin designed a base Counter class and a registry system to support extensible performance analysis and future metric types. In addition, Jiamin upgraded the SGLang library, refactored test utilities for maintainability, and expanded multimodal test coverage to models like Qwen2.5-VL and Gemma3. Addressing distributed systems challenges, Jiamin resolved a memory safety issue in data parallelism, demonstrating depth in memory management, dependency handling, and deep learning frameworks.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
185,286
Activity Months2
Your Network
4172 people
Work History
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for microsoft/ltp-sglang focusing on key accomplishments and impact in features delivery and bug fixes.
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for microsoft/ltp-sglang focusing on key accomplishments and impact in features delivery and bug fixes.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 2025In August 2025, delivered a new benchmarking framework for model metrics in microsoft/ltp-sglang, enabling measurement of core model stats across diverse architectures. The framework provides a base Counter class and a registry to manage metric counters, supporting extensible performance analysis and data-driven optimization.
August 2025
1 Commits • 1 Features
Aug 1, 2025In August 2025, delivered a new benchmarking framework for model metrics in microsoft/ltp-sglang, enabling measurement of core model stats across diverse architectures. The framework provides a base Counter class and a registry to manage metric counters, supporting extensible performance analysis and data-driven optimization.
Activity
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Quality Metrics
Correctness83.4%
Maintainability83.4%
Architecture86.6%
Performance66.6%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
BenchmarkingDeep Learning FrameworksDependency ManagementDistributed SystemsMemory ManagementModel OptimizationMultimodal AIObject-Oriented ProgrammingParallel ComputingPerformance AnalysisPython DevelopmentSoftware DesignTesting
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