DataBytes-Organisation/Fine-Tuning-LLMs-for-Enterprise-Applications
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Deep LearningGradioHugging Face TransformersLLM Fine-tuningMachine LearningNatural Language Processing
Zaeem Rizan
PROFILE
Zaeem Rizan
Zaeem Rizan developed an end-to-end fine-tuning pipeline for Mistral 7B on the DataBytes-Organisation/Fine-Tuning-LLMs-for-Enterprise-Applications repository, targeting medical question answering for enterprise use. He handled comprehensive data preparation, established baseline model evaluation, and applied QLoRA-based fine-tuning to adapt the model to domain-specific requirements. The workflow included a direct performance comparison against the base model to quantify improvements. Zaeem also built a Gradio interface, enabling interactive demonstrations of the medical AI assistant for stakeholders. His work leveraged Python, Hugging Face Transformers, and deep learning techniques, resulting in a reproducible, enterprise-ready solution for rapid deployment of tailored AI assistants.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
12,904
Activity Months1
Your Network
5 peopleShared Repositories
5
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for DataBytes-Organisation/Fine-Tuning-LLMs-for-Enterprise-Applications. Key features delivered include end-to-end fine-tuning of Mistral 7B for medical QA using QLoRA on an enterprise dataset, comprehensive data preparation, baseline model evaluation, and a Gradio interface for interactive demonstration of the medical AI assistant. A direct performance comparison against the base model was established to quantify improvements, culminating in the Final Submission commit. Major bugs fixed: none reported this month. Overall impact: enables enterprise-ready customization of medical QA, supports rapid deployment of domain-tailored AI assistants, and provides a reproducible end-to-end pipeline with stakeholder-facing demos. Technologies/skills demonstrated: Mistral 7B, QLoRA, data preparation and evaluation, Gradio, end-to-end ML ops, reproducible experimentation, and strong commit discipline.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for DataBytes-Organisation/Fine-Tuning-LLMs-for-Enterprise-Applications. Key features delivered include end-to-end fine-tuning of Mistral 7B for medical QA using QLoRA on an enterprise dataset, comprehensive data preparation, baseline model evaluation, and a Gradio interface for interactive demonstration of the medical AI assistant. A direct performance comparison against the base model was established to quantify improvements, culminating in the Final Submission commit. Major bugs fixed: none reported this month. Overall impact: enables enterprise-ready customization of medical QA, supports rapid deployment of domain-tailored AI assistants, and provides a reproducible end-to-end pipeline with stakeholder-facing demos. Technologies/skills demonstrated: Mistral 7B, QLoRA, data preparation and evaluation, Gradio, end-to-end ML ops, reproducible experimentation, and strong commit discipline.
May 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage100.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningGradioHugging Face TransformersLLM Fine-tuningMachine LearningNatural Language ProcessingPEFTPythonQLoRA
Repositories Contributed To
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