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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

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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