CSCfi/csc-user-guide
Aug 2025 – Sep 2025
2 Months active
Languages Used
MarkdownPython
Technical Skills
AWQDeep LearningDocumentationDocumentation ManagementGPTQHugging Face Transformers
Mahnoor Mahnoor
PROFILE
Mahnoor Mahnoor
Mahnoor Mahnoor enhanced the CSCfi/csc-user-guide repository by overhauling and consolidating documentation for large language model quantization. She focused on improving guidance for PTQ, QAT, GPTQ, AWQ, and related tools such as bitsandbytes and llmcompressor, providing practical Python code examples and clarifying distinctions between runtime quantization and one-time compression. Her work included disciplined documentation management in Markdown, iterative updates for accuracy, and the removal of outdated content to streamline onboarding and reduce support overhead. Mahnoor’s contributions deepened the repository’s technical clarity, making quantization workflows more accessible and maintainable for users working with Hugging Face Transformers.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
2
Lines of code
576
Activity Months2
Your Network
88 people
Work History
September 2025
5 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for CSCfi/csc-user-guide: Key feature delivered – ML quantization documentation overhaul for ml-llm, covering PTQ, QAT, GPTQ, AWQ; updated code examples for bitsandbytes and GPTQ; replaced dataset references; removed outdated Quantization.md. Commits contributing – 5 changes across the feature: 94d4d6b9bd5bc7b78a2888bc65f3f8c90d0a01aa, 990d5b9afa697ba243a78ce86a7e95cd2c80cdec, c98ec15c667280b2aadbb91ab20d55727076c98c, 3133ad7d3d718868bf94f5c785f160e03b61a976, 8af6c8ac070d3b29ff8507cc026a2ebe07808a85. No major bugs fixed this month; the focus was on documentation quality, consistency, and maintainability. Overall impact and business value: improved onboarding and maintainability of quantization guidance, reducing support overhead and aligning with current tooling. Technologies/skills demonstrated: technical writing, ML quantization concepts, code-example curation, dataset handling, and disciplined version control.
5 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for CSCfi/csc-user-guide: Key feature delivered – ML quantization documentation overhaul for ml-llm, covering PTQ, QAT, GPTQ, AWQ; updated code examples for bitsandbytes and GPTQ; replaced dataset references; removed outdated Quantization.md. Commits contributing – 5 changes across the feature: 94d4d6b9bd5bc7b78a2888bc65f3f8c90d0a01aa, 990d5b9afa697ba243a78ce86a7e95cd2c80cdec, c98ec15c667280b2aadbb91ab20d55727076c98c, 3133ad7d3d718868bf94f5c785f160e03b61a976, 8af6c8ac070d3b29ff8507cc026a2ebe07808a85. No major bugs fixed this month; the focus was on documentation quality, consistency, and maintainability. Overall impact and business value: improved onboarding and maintainability of quantization guidance, reducing support overhead and aligning with current tooling. Technologies/skills demonstrated: technical writing, ML quantization concepts, code-example curation, dataset handling, and disciplined version control.
September 2025
August 2025
7 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for CSCfi/csc-user-guide focusing on quantization documentation enhancements and maintainability. Delivered comprehensive Quantization Documentation Enhancements for LLMs, consolidating PTQ, QAT, GPTQ, AWQ, bitsandbytes, and llmcompressor with practical code examples and clear guidance on runtime quantization vs. one-time compression. Performed documentation housekeeping (rename and formatting) to improve usability and consistency. No major bug fixes this period; all work focused on documentation and knowledge sharing to enable faster, safer model quantization deployments.
August 2025
7 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for CSCfi/csc-user-guide focusing on quantization documentation enhancements and maintainability. Delivered comprehensive Quantization Documentation Enhancements for LLMs, consolidating PTQ, QAT, GPTQ, AWQ, bitsandbytes, and llmcompressor with practical code examples and clear guidance on runtime quantization vs. one-time compression. Performed documentation housekeeping (rename and formatting) to improve usability and consistency. No major bug fixes this period; all work focused on documentation and knowledge sharing to enable faster, safer model quantization deployments.
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
AWQDeep LearningDocumentationDocumentation ManagementGPTQHugging Face TransformersLLM CompressionLLM CompressorLLM QuantizationLarge Language ModelsMachine LearningModel QuantizationQuantizationTechnical Writingbitsandbytes
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