Skip to content

Natural Language Processing: A Notebook-Based Introduction

Overview

mlciv/nlp-notebooks

Natural Language Processing: A Notebook-Based Introduction

mlciv/nlp-notebooks

Home
The Book
The Book
- Preface
- Roadmap
- License
Chapters
Chapters
- I — Language Model Foundations
  I — Language Model Foundations
- II — Machine Learning Foundation and NLP Tasks
  II — Machine Learning Foundation and NLP Tasks
  - Overview
  - 2 · Text Classification
- III — Representations & Sequences
  III — Representations & Sequences
  - Overview
  - 3 · Embeddings & Sequence Models
- IV — Transformers & LLMs
  IV — Transformers & LLMs
- V — LLM Alignment and Agentic AI
  V — LLM Alignment and Agentic AI
  - Overview Overview
    Table of contents
    
    Chapters in this part
    
    What you'll be able to do
  - 5 · Fine-Tuning with LoRA/QLoRA
  - 5 · Agentic NLP
Course
Course

V — LLM Alignment and Agentic AI¶

This part adapts large language models to specific tasks and turns them into agents.

Chapters in this part¶

Chapter 5: Fine-Tuning with LoRA/QLoRA — Supervised fine-tuning with parameter-efficient methods (LoRA/QLoRA via TRL), and building an LLM agent with LangGraph.

What you'll be able to do¶

Fine-tune an LLM efficiently with LoRA/QLoRA.
Compose an LLM into a simple agent with LangGraph.
Design tool functions and a grading agent for an agentic task.