intel/neural-compressor
Feb 2025 – Mar 2025
2 Months active
Languages Used
C++PythonShell
Technical Skills
Deep LearningHPU AccelerationModel OptimizationPyTorchQuantizationGPTQ
Asaf Karnieli
PROFILE
Asaf Karnieli
During a two-month period, Akarnieli developed advanced quantization features for the intel/neural-compressor repository, focusing on efficient large-model deployment. He implemented Hybrid GPTQ support, enabling mixed-precision quantization with int4 weights and FP8 activations, and introduced new modules and quantizer classes in Python and C++. In the following month, he delivered block-wise GPTQ quantization with sharded checkpoints and safetensors integration, optimizing memory usage and performance for HPU-based inference. His work addressed the challenges of scalable model optimization and deployment, demonstrating depth in deep learning, PyTorch, and model quantization while aligning with broader production-readiness and compatibility goals for neural_compressor.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
848
Activity Months2
Your Network
129 people
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary focusing on key accomplishments and business impact. Key feature delivered: Block-wise GPTQ quantization for intel/neural-compressor with sharded checkpoints and safetensors for HPU; updated requirements for safetensors to ensure HPU compatibility. No major bugs fixed reported this month. Overall impact: improved memory efficiency and performance for large-model quantization, enabling scalable deployment on HPUs and broader adoption. Technologies/skills demonstrated: GPTQ quantization, sharded checkpoints, safetensors integration, dependency management, and integration with Intel neural-compressor repo.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary focusing on key accomplishments and business impact. Key feature delivered: Block-wise GPTQ quantization for intel/neural-compressor with sharded checkpoints and safetensors for HPU; updated requirements for safetensors to ensure HPU compatibility. No major bugs fixed reported this month. Overall impact: improved memory efficiency and performance for large-model quantization, enabling scalable deployment on HPUs and broader adoption. Technologies/skills demonstrated: GPTQ quantization, sharded checkpoints, safetensors integration, dependency management, and integration with Intel neural-compressor repo.
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for intel/neural-compressor: Delivered Hybrid GPTQ support, enabling mixed-precision quantization with int4 weights and FP8 activations. Implemented new modules and quantizer classes to support this capability (Phase 1). The work lays groundwork for more efficient model execution and broader deployment options in neural_compressor.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for intel/neural-compressor: Delivered Hybrid GPTQ support, enabling mixed-precision quantization with int4 weights and FP8 activations. Implemented new modules and quantizer classes to support this capability (Phase 1). The work lays groundwork for more efficient model execution and broader deployment options in neural_compressor.
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance95.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonShell
Technical Skills
Deep LearningGPTQHPUHPU AccelerationModel OptimizationPyTorchQuantizationSafetensorsShell Scripting
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