DataBytes-Organisation/Fine-Tuning-LLMs-for-Enterprise-Applications
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Deep LearningGoogle ColabLLM Fine-tuningMachine LearningNatural Language ProcessingPEFT
Shin
PROFILE
Shin
Hong Tham Nguyen developed an enterprise-ready sentiment analysis capability for healthcare reviews in the DataBytes-Organisation/Fine-Tuning-LLMs-for-Enterprise-Applications repository. She fine-tuned the Mistral 7B model using Python and Transformers, implementing data loading, preprocessing, inference, and evaluation pipelines tailored to healthcare feedback. Leveraging LoRA and Unsloth, she optimized training efficiency to support scalable enterprise deployment. Her work included preparing project artifacts and ensuring the repository was production-ready. By establishing an end-to-end workflow for automated sentiment classification, Hong enabled organizations to accelerate customer voice analytics and deploy robust natural language processing solutions across multiple healthcare feedback channels. The project demonstrated technical depth.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
13,164
Activity Months1
Your Network
5 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 performance summary for DataBytes-Organisation/Fine-Tuning-LLMs-for-Enterprise-Applications. Delivered an enterprise-capable sentiment analysis capability by fine-tuning Mistral 7B for healthcare reviews, including data loading, preprocessing, model inference, and evaluation pipelines. Optimized training efficiency with LoRA and Unsloth to enable scalable enterprise deployment. Artifacts uploaded and project prepared for production use (commit af249f44ddafbef78217109d10b74492ec568f9a). This work enables automated sentiment classification of healthcare feedback and accelerates time-to-value for customer voice analytics.
1 Commits • 1 Features
May 1, 2025May 2025 performance summary for DataBytes-Organisation/Fine-Tuning-LLMs-for-Enterprise-Applications. Delivered an enterprise-capable sentiment analysis capability by fine-tuning Mistral 7B for healthcare reviews, including data loading, preprocessing, model inference, and evaluation pipelines. Optimized training efficiency with LoRA and Unsloth to enable scalable enterprise deployment. Artifacts uploaded and project prepared for production use (commit af249f44ddafbef78217109d10b74492ec568f9a). This work enables automated sentiment classification of healthcare feedback and accelerates time-to-value for customer voice analytics.
May 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage100.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningGoogle ColabLLM Fine-tuningMachine LearningNatural Language ProcessingPEFTPandasPythonTRLTransformersUnsloth
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