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 in Python and Bash to support MLCommons dataset inputs, producing standardized evaluation artifacts such as accuracy.json and throughput metrics. Rafal also created setup and environment scripts to streamline user adoption and reproducibility of the evaluation workflow. His work focused on dataset handling, model evaluation, and performance benchmarking, delivering a ready-to-run solution that addressed the need for transparent, reproducible model performance assessment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
286
Activity Months1
Your Network
1993 people
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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