huggingface/peft
Jul 2025 – Jul 2025
1 Month active
Languages Used
MarkdownPythonShell
Technical Skills
Adapter-based Fine-tuningDeep LearningDocumentationLibrary IntegrationMachine LearningModel Implementation
kkb-code
PROFILE
Kkb-code
Kbhardwa developed and integrated the SHiRA Adapters feature into the huggingface/peft repository, introducing a new parameter-efficient fine-tuning (PEFT) method for large language models. This work involved implementing Sparse High Rank Adapters with full configuration options, model code, and comprehensive documentation, all designed to streamline and optimize the fine-tuning process. Using Python and leveraging deep learning and library integration skills, Kbhardwa ensured that SHiRA Adapters could be seamlessly adopted within existing PEFT workflows. The contribution addressed the need for more efficient model adaptation, demonstrating depth in both model implementation and technical documentation within a production machine learning library.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,632
Activity Months1
Your Network
138 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly performance summary for huggingface/peft. Delivered SHiRA Adapters as a new PEFT method, with configurations, model implementations, documentation, and example usage, integrated into the core PEFT library to enable more efficient fine-tuning of large language models.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly performance summary for huggingface/peft. Delivered SHiRA Adapters as a new PEFT method, with configurations, model implementations, documentation, and example usage, integrated into the core PEFT library to enable more efficient fine-tuning of large language models.
July 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonShell
Technical Skills
Adapter-based Fine-tuningDeep LearningDocumentationLibrary IntegrationMachine LearningModel ImplementationParameter-Efficient Fine-Tuning (PEFT)
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