huggingface/optimum-habana
Jun 2025 – Jun 2025
1 Month active
Languages Used
BashMarkdownPython
Technical Skills
Dataset HandlingMachine LearningModel EvaluationPerformance BenchmarkingScripting
Rafal Bogdanowicz
PROFILE
Rafal Bogdanowicz
Rafal Bogdanowicz developed an end-to-end MLCommons Evaluation Framework for the Mixtral 8x7B model within the huggingface/optimum-habana repository, enabling objective assessment of model accuracy and throughput. He extended the command-line interface and generation scripts to support MLCommons dataset inputs, producing standardized evaluation artifacts such as accuracy.json and throughput metrics. Using Python and Bash, Rafal delivered setup scripts and a ready-to-run workflow that streamlined environment configuration and reproducibility for users. His work focused on dataset handling, model evaluation, and performance benchmarking, providing a robust solution for transparent and repeatable evaluation of large language models in production environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
286
Activity Months1
Your Network
2068 peopleSame Organization
@intel.com2035
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered end-to-end MLCommons Evaluation Framework for the Mixtral 8x7B model in huggingface/optimum-habana, enabling objective accuracy and throughput assessment. Implemented end-to-end evaluation workflow, CLI arguments, and generation script adjustments to support MLCommons inputs. Generated accuracy.json and throughput metrics, and provided ready-to-run evaluation workflow and environment setup scripts for easy adoption by users.
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered end-to-end MLCommons Evaluation Framework for the Mixtral 8x7B model in huggingface/optimum-habana, enabling objective accuracy and throughput assessment. Implemented end-to-end evaluation workflow, CLI arguments, and generation script adjustments to support MLCommons inputs. Generated accuracy.json and throughput metrics, and provided ready-to-run evaluation workflow and environment setup scripts for easy adoption by users.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashMarkdownPython
Technical Skills
Dataset HandlingMachine LearningModel EvaluationPerformance BenchmarkingScripting
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