Hands-On Large Language Models: Language Understanding and Generation

Overview

A practical book and code repository that teaches how to build, train, and deploy large language models. It covers transformer architectures, fine-tuning, and generation techniques with hands-on Jupyter notebooks and PyTorch code.

Best for

Best for
Python developers and ML engineers who want to deeply understand and implement LLMs from code.

Use cases

• Learning transformer internals from scratch through code exercises
• Fine-tuning pretrained models on custom datasets for classification or generation
• Deploying LLMs in production with inference optimization

Notes

A practical book and code repository that teaches how to build, train, and deploy large language models. It covers transformer architectures, fine-tuning, and generation techniques with hands-on Jupyter notebooks and PyTorch code.

Use cases

• Learning transformer internals from scratch through code exercises
• Fine-tuning pretrained models on custom datasets for classification or generation
• Deploying LLMs in production with inference optimization

Pros

• Provides clear, runnable code examples for every key concept
• Covers both understanding and practical deployment of LLMs
• Free to access online with extensive supplementary materials

Cons

• Only covers up to early 2024 models and techniques
• Assumes prior knowledge of PyTorch and deep learning basics
• Not a reference for the latest industry APIs or closed-source models

Indexed from awesome-llm and enriched against its public facts.