Fine-Tuning a 1B Parameter Model for FastAPI Code Adaptation

Abstract

This webinar explores the practical approach to fine-tuning a 1B parameter large language model (LLM) specifically for FastAPI code adaptation and backend development use cases. Participants will gain insights into preparing domain-specific datasets, selecting efficient fine-tuning strategies, and optimizing model performance for real-world API workflows. The session will cover how a mid-sized LLM can be adapted to understand FastAPI patterns, generate scalable API code, and assist in tasks such as endpoint creation, validation, and error handling. Attendees will also learn best practices for deployment, inference optimization, and integrating the fine-tuned model into modern development pipelines. By the end of the webinar, learners will have a clear understanding of how to build faster, more intelligent API development solutions using fine-tuned language models.

Date: Jan 29th, 2026

Time: 4:30 PM – 5:30 PM

Speaker: Mr. Rajeev Chandran

Agenda:

• What Goes Wrong with General LLMs
• Fine-Tuning Strategy Overview
• LoRA / QLoRA in Practice
• Practical Evaluation Strategy
• Inference & Deployment Architecture

Who Should Attend? 

• Python & FastAPI developers
• ML / AI engineers
• Backend & software developers
• Data scientists
• Engineering students

About Trainer:

Mr. Rajeev Chandran, a distinguished technology luminary with nearly two decades of industry-leading experience, embodies the essence of a visionary leader and architect of cutting-edge solutions. His remarkable ability to seamlessly integrate diverse realms such as products, technology, data science, and software has resulted in the creation of large-scale, high-performance solutions that transcend geographical boundaries.

With laser-focused expertise in Data Engineering, ML Engineering, Data Science Microservices, Search Engineering, Cloud, and DevOps, Rajeev has left an indelible mark on the eCommerce, Retail, Search, and Advertisement sectors.