Master Large Language Models & Guide To Publish Your Research Module

Expected Learning Outcomes: By the end of this course, students will:

Understand the architecture & working of LLMs
Be able to fine-tune & train LLMs on custom datasets
Learn to deploy LLM-powered applications
Develop a research paper on LLMs

Class 1: Introduction to Large Language Models (LLMs)

Topics Covered

Introduction to Natural Language Processing (NLP)
Evolution of LLMs: From rule-based to deep learning
Transformer Architecture (Self-attention, Multi-head attention)
Popular LLMs: BERT, GPT-2, GPT-3, T5, LLaMA, Falcon
Pretraining vs. Fine-tuning
Ethical considerations in LLMs

Practical (Hands-on)

Set up the environment (transformers, datasets)
Load a pretrained GPT-2 model for text generation
Experiment with BERT for fill-in-the-blanks (MLM)
Understand Tokenization & Model Parameters

Class 2: Pretrained Models & Fine-tuning LLMs

Topics Covered

Difference between Pretraining vs. Fine-tuning
Use cases of LLMs (Sentiment analysis
Text generation, Summarization)
Steps for fine-tuning a model
Challenges in fine-tuning (Overfitting, Hardware limitations, Bias)

Practical (Hands-on)

Load a pretrained sentiment analysis model
Fine-tune BERT on IMDb (Sentiment classification)
Train using Trainer API
Evaluate fine-tuned model

Class 3: Data Preparation & Tokenization for LLM Training

Topics Covered

Understanding datasets for LLMs
Hugging Face Datasets Hub, Data cleaning and preprocessing
Tokenization strategies (WordPiece for BERT, Byte-Pair Encoding for GPT and SentencePiece for T5)

Practical (Hands-on)

Load datasets using datasets library
Tokenize text using AutoTokenizer
Implement custom tokenization
Convert raw text into model input format.

Class 4: Training LLMs from Scratch

Topics Covered

When should we train from scratch?
Steps in training an LLM (Data preprocessing, Model architecture selection, Training process, Evaluation)
Computational challenges & hardware requirements.

Practical (Hands-on)

Implement a simple Transformer model from scratch
Train a small GPT model on a toy dataset
Monitor loss & optimize hyperparameters

Class 5: Advanced LLM Applications

Topics Covered

Text Generation Techniques (Greedy search, Beam search, Top-k & Top-p sampling)
Summarization using LLMs
Named Entity Recognition (NER)
Prompt Engineering & Zero-shot Learning

Practical (Hands-on)

Generate text using different sampling techniques
Perform text summarization using T5
Implement NER using spaCy & Transformers
Experiment with prompt engineering for GPT models

Class 6: Explainability & Bias in LLMs

Topics Covered

Explainable AI (XAI) in NLP
Bias & Fairness in LLMs
Techniques for LLM interpretability
Debugging LLM outputs

Practical (Hands-on)

Use SHAP/LIME for model explainability
Identify and mitigate bias in LLMs
Debug incorrect outputs using prompt tuning

Class 7: Deploying LLM Applications (Hugging Face & APIs)

Topics Covered

Hugging Face Model Hub
Deploying LLMs using APIs
Creating Web Apps with Gradio & Streamlit
Real-world use cases.

Practical (Hands-on)

Deploy a model on Hugging Face Spaces
Build a Gradio-based chatbot
Create a text summarization web app

Class 8: Research Trends & Project Development

Topics Covered

Recent advancements in LLM research
Ethical implications & future challenges
Steps to writing an LLM-based research paper
How to publish in top ML conferences (ACL, NeurIPS, etc.)

Practical (Hands-on)

Guide students on choosing a research topic
Help in experiment design & dataset selection
Assist in writing the paper structure
Discuss review & submission process

Final Project (Mandatory)

Objective: Each student will develop a mini-research paper based on LLMs.

Master Large Language Models & Guide To Publish Your Research Module

Expected Learning Outcomes: By the end of this course, students will:

Understand the architecture & working of LLMs

Be able to fine-tune & train LLMs on custom datasets

Learn to deploy LLM-powered applications

Develop a research paper on LLMs

Class 1: Introduction to Large Language Models (LLMs)

Class 2: Pretrained Models & Fine-tuning LLMs

Class 3: Data Preparation & Tokenization for LLM Training

Class 4: Training LLMs from Scratch

Class 5: Advanced LLM Applications

Class 6: Explainability & Bias in LLMs

Class 7: Deploying LLM Applications (Hugging Face & APIs)

Class 8: Research Trends & Project Development

Final Project (Mandatory)