openvinotoolkit/nncf
May 2025 – Jun 2025
2 Months active
Languages Used
PythonShellMarkdown
Technical Skills
Hugging Face TransformersLarge Language Models (LLMs)LoRA (Low-Rank Adaptation)Neural Low-Rank Adapter Search (NLS)OpenVINOPyTorch
J. Pablo Muñoz
PROFILE
J. Pablo Muñoz
Contributed to the openvinotoolkit/nncf repository by developing advanced features for quantization-aware training and evaluation of large language models. Delivered a runnable example integrating Neural Low-Rank Adapter Search (NLS) with quantization-aware fine-tuning, including new Python scripts, updates to NNCF components, and comprehensive documentation to support reproducibility. Enhanced the evaluation pipeline by introducing a fast_eval option, refactoring logic for accelerated assessments, and improving output reporting for clearer results. Leveraged expertise in Python, PyTorch, and model optimization to streamline experimentation and performance analysis, enabling practitioners to efficiently apply NLS techniques to large language models within the OpenVINO ecosystem.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,643
Activity Months2
Your Network
2277 peopleSame Organization
@intel.com2254
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for openvinotoolkit/nncf: Delivered a fast_eval option for Neural Low-Rank Adapter Search (NLS) to accelerate evaluations; refactored the evaluation pipeline to support a faster evaluation path; and enhanced output reporting for clearer results. These changes reduce evaluation time, enable faster experimentation, and improve result clarity for stakeholders. Commit reference: 0c28d3bf03a1e5e57c595602b0c62e2e0532239e (#3508).
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for openvinotoolkit/nncf: Delivered a fast_eval option for Neural Low-Rank Adapter Search (NLS) to accelerate evaluations; refactored the evaluation pipeline to support a faster evaluation path; and enhanced output reporting for clearer results. These changes reduce evaluation time, enable faster experimentation, and improve result clarity for stakeholders. Commit reference: 0c28d3bf03a1e5e57c595602b0c62e2e0532239e (#3508).
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for openvinotoolkit/nncf: Delivered a focused QAT with Neural Low-Rank Adapter Search (NLS) for Large Language Models, including a runnable example, new Python scripts, updates to NNCF library components, and a README detailing usage and observed results. The release aligns with the goal of making quantized fine-tuning with NLS practical for LLM tasks and provides a clear reference implementation for teams to reproduce improvements on downstream tasks. Commit reference tied to this work: a283adc0fd45766573e36cd3882243bfb0120071 (Release QAT example with NLS, #3480).
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for openvinotoolkit/nncf: Delivered a focused QAT with Neural Low-Rank Adapter Search (NLS) for Large Language Models, including a runnable example, new Python scripts, updates to NNCF library components, and a README detailing usage and observed results. The release aligns with the goal of making quantized fine-tuning with NLS practical for LLM tasks and provides a clear reference implementation for teams to reproduce improvements on downstream tasks. Commit reference tied to this work: a283adc0fd45766573e36cd3882243bfb0120071 (Release QAT example with NLS, #3480).
Activity
Loading activity data...
Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture85.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonShell
Technical Skills
Deep LearningFull Stack DevelopmentHugging Face TransformersLarge Language Models (LLMs)LoRA (Low-Rank Adaptation)Machine LearningModel OptimizationNeural Low-Rank Adapter Search (NLS)OpenVINOPerformance OptimizationPyTorchPythonQuantization-Aware Training (QAT)
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline