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Large Language Models : A Hands on Approach

Jan - May, 2026 @ Center for Continuing Education, Indian Institute of Science

Logistics

  • Duration: 18 weeks (Jan - May 2026)
  • Format: Online, Tue and Thu, 7:30 - 9:00 PM IST
  • Contact: TBA

Registration

To register please visit CCE Webpage for the course.

Course Description

LLMs have become mainstay of NLP and are transforming every domain, from software development, research, and business intelligence to education. However, deploying them efficiently remains a specialized engineering challenge.

This course provides an engineering-focused exploration of Large Language Models (LLMs). Participants will go from understanding transformer architectures and GPU internals to mastering fine-tuning, inference optimization, and large-scale deployment across GPUs, clusters, and edge devices. Through a theory-to-practice approach, including case studies, hands-on labs, and projects, learners will cover key topics such as model architecture, fine-tuning techniques, inference optimization, serving strategies, and applications in retrieval-augmented generation (RAG) and agentic systems.

Learning Outcomes

By the end of this course, participants will be able to:

  • Understand LLM Architecture: Master transformer architectures, attention mechanisms, and modern LLM variants (GPT-OSS, Qwen, Gemma, etc.).
  • Optimize Inference: Implement efficient inference strategies including quantization, KV caching, and serving via inference engines like vLLM.
  • Fine-tune Models: Apply various fine-tuning techniques including LoRA, QLoRA, instruction tuning, and preference alignment.
  • Build RAG Systems: Design and implement Retrieval-Augmented Generation pipelines.
  • Develop AI Agents: Create tool-using agents with the ReAct framework.
  • Deploy at Scale: Set up production-ready LLM serving infrastructure with cost optimization.
  • Multimodal Models: Work with vision-language models and speech.
  • Evaluation: Understand evaluation strategies for LLMs and RAG systems.

Prerequisites

  • Proficiency in Python and familiarity with any deep learning framework (PyTorch preferred).
  • Basic understanding of neural networks.
  • Working knowledge of Linux, Docker, and Git.
  • Optional but recommended: experience with GPU computing.

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