vllm-project/llm-compressor
Jan 2026 – Feb 2026
2 Months active
Languages Used
PythonYAML
Technical Skills
Data ProcessingMachine LearningPerformance OptimizationPython programmingmachine learningquantization
ZewenShen-Cohere
PROFILE
Zewenshen-cohere
Worked on the vllm-project/llm-compressor repository to advance quantization and calibration for large language models, focusing on both performance and deployment readiness. Developed NVFP4A16 quantization support and improved calibration pipelines, leveraging Python and PyTorch to accelerate GPU-based workflows and enhance observability through richer logging. Introduced token-level masking for calibration, enabling more precise optimization of instruction-tuned models, and implemented robust activation caching for parallel transformer architectures. Addressed bugs related to activation averaging and weight handling, ensuring smoother calibration and deployment. The work emphasized model accuracy preservation, efficient data processing, and streamlined quantization, supporting faster, higher-quality model deployment cycles.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
5Total
Bugs
2
Commits
5
Features
2
Lines of code
578
Activity Months2
Your Network
106 peopleWork History
February 2026
3 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for vllm-project/llm-compressor. Focused on improving calibration precision and robustness for quantization in instruction-tuned models. Delivered token-level masking for calibration, added activation_hook_target for per-submodule activation caching in parallel transformer blocks, and hardened balance-layer weight handling to ensure smoothing works when layers are quantized or not. These changes sharpen model accuracy preservation, reduce calibration risk, and streamline deployment of efficient, high-quality models. Technologies exercised include Python, PyTorch, AWQ, and parallel transformer architectures; collaboration across the team (co-authored PRs with Dipika Sikka and HDCharles).
3 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for vllm-project/llm-compressor. Focused on improving calibration precision and robustness for quantization in instruction-tuned models. Delivered token-level masking for calibration, added activation_hook_target for per-submodule activation caching in parallel transformer blocks, and hardened balance-layer weight handling to ensure smoothing works when layers are quantized or not. These changes sharpen model accuracy preservation, reduce calibration risk, and streamline deployment of efficient, high-quality models. Technologies exercised include Python, PyTorch, AWQ, and parallel transformer architectures; collaboration across the team (co-authored PRs with Dipika Sikka and HDCharles).
February 2026
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for vllm-project/llm-compressor. Focused on expanding quantization capabilities, accelerating calibration pipelines, and improving observability to drive business value through faster, more accurate model deployment.
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for vllm-project/llm-compressor. Focused on expanding quantization capabilities, accelerating calibration pipelines, and improving observability to drive business value through faster, more accurate model deployment.
Activity
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Quality Metrics
Correctness92.0%
Maintainability84.0%
Architecture92.0%
Performance84.0%
AI Usage52.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Data ProcessingDeep LearningMachine LearningModel OptimizationPerformance OptimizationPythonPython ProgrammingPython programmingmachine learningquantizationtesting
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