Applications Open now for September 2024 Batch | Applications Close: Sep 15, 2024 | Exam: Oct 27, 2024

Applications Open now for September 2024 Batch | Applications Close: Sep 15, 2024 | Exam: Oct 27, 2024

Degree Level Course

Large Language Models

Understanding the Transformer architecture Understanding the concept of pretraining and fine-tuning language models Compare and contrast different types of tokenizers like BPE, wordpiece, sentencepiece Understanding different LLMs architectures: encoder-decoder, encoder-only, decoder-only Exploring common datasets like C4,mc4,Pile, Stack and so on Addressing the challenges of applying vanilla attention mechanisms for long range context windows. Apply different types of fine-tuning techniques to fine-tune large language models

by Mitesh M.Khapra

Course ID: BSCS5001

Course Credits: 4

Course Type: Elective

Pre-requisites: None

Course structure & Assessments

For details of standard course structure and assessments, visit Academics page.

WEEK 1 Transformers: Introduction to transformers - Self-attention - cross- attention-Masked attention-Positional encoding
WEEK 2 A deep dive into number of parameters, computational complexity and FLOPs- Introduction to language modeling
WEEK 3 Causal Language Modeling: What is a language model?- Generative Pretrained Transformers (GPT) - Training and inference
WEEK 4 Masked Language Modeling : Bidirectional Encoder Representations of Transformers (BERT) - Fine-tuning - A deep dive into tokenization: BPE, SentencePiece, wordpiece
WEEK 5 Bigger Picture: T5, A deep dive into text-to-text (genesis of prompting), taxonomy of models, road ahead
WEEK 6 Data: Datasets, Pipelines, effectiveness of clean data, Architecture: Types of attention, positional encoding (PE) techniques, scaling techniques
WEEK 7 Training: Revisiting optimizers, LION vs Adam, Loss functions, Learning schedules, Gradient Clipping, typical failures during training
WEEK 8 Fine Tuning: Prompt Tuning,Multi-task Fine-tuning,Parametric Efficient Fine-Tuning, Instruction fine-tuning datasets
WEEK 9 Benchmarks: MMLU, BigBench, HELM,OpenLLM, Evaluation Frameworks
WEEK 10 Training Large Models: Mixed precision training,Activation checkpointing, 3D parallelism, ZERO, Bloom as a case study
WEEK 11 Scaling Laws: Chinchilla,Gopher, Palm v2
WEEK 12 Recent advances
+ Show all weeks

Prescribed Books

The following are the suggested books for the course:

Research papers, articles

About the Instructors

Mitesh M.Khapra
Associate Professor, Department of Computer Science and Engineering, IIT Madras

Mitesh M. Khapra is an Associate Professor in the Department of Computer Science and Engineering at IIT Madras and is affiliated with the Robert Bosch Centre for Data Science and AI. He is also a co-founder of One Fourth Labs, a startup whose mission is to design and deliver affordable hands-on courses on AI and related topics. He is also a co-founder of AI4Bharat, a voluntary community with an aim to provide AI-based solutions to India-specific problems. His research interests span the areas of Deep Learning, Multimodal Multilingual Processing, Natural Language Generation, Dialog systems, Question Answering and Indic Language Processing. Prior to IIT Madras, he was a Researcher at IBM Research India for four and a half years, where he worked on several interesting problems in the areas of Statistical Machine Translation, Cross Language Learning, Multimodal Learning, Argument Mining and Deep Learning. Prior to IBM, he completed his PhD and M.Tech from IIT Bombay in Jan 2012 and July 2008 respectively.During his PhD he was a recipient of the IBM PhD Fellowship (2011) and the Microsoft Rising Star Award (2011). He is also a recipient of the Google Faculty Research Award (2018), the IITM Young Faculty Recognition Award (2019) and the Prof. B. Yegnanarayana Award for Excellence in Research and Teaching (2020).


Other courses by the same instructor: BSCS3004 - Deep Learning