AI Frameworks · HandsOnLLM

Hands-On-Large-Language-Models

Official code repository for the O'Reilly book "Hands-On Large Language Models" by Jay Alammar and Maarten Grootendorst. Contains 12 Jupyter notebooks covering LLM fundamentals, embeddings, transformers, classification, clustering, prompt engineering, RAG, multimodal models, and fine-tuning. Designed for practical learning with Google Colab integration.

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Source: GitHub — github.com/HandsOnLLM/Hands-On-Large-Language-Models

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Jupyter Notebook

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	HandsOnLLM/Hands-On-Large-Language-Models
Owner	HandsOnLLM
Primary language	Jupyter Notebook
License	Apache-2.0 — OSI-approved
Stars	27.4k
Forks	6.4k
Open issues	37
Latest release	Unknown
Last updated	2026-04-24
Source	https://github.com/HandsOnLLM/Hands-On-Large-Language-Models

What Hands-On-Large-Language-Models is

Collection of executable Jupyter notebooks demonstrating LLM concepts: tokenization, embedding generation, transformer internals, text classification/clustering, prompt engineering, semantic search/RAG, multimodal inference, custom embedding models, and fine-tuning workflows for both representation and generation models. Optimized for T4 GPU execution in Google Colab.

Quickstart

Get the Hands-On-Large-Language-Models source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/HandsOnLLM/Hands-On-Large-Language-Models.git2cd Hands-On-Large-Language-Models3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Educational Introduction to LLM Concepts

Ideal for engineers, data scientists, and product teams learning LLM fundamentals through visual explanations and working code. Chapters progress from transformer basics to production techniques like RAG and fine-tuning.

Hands-On Prototyping of LLM Applications

Notebooks serve as runnable templates for text classification, semantic search, prompt engineering, and retrieval-augmented generation pipelines. Low friction to modify and adapt for your own datasets.

Team Onboarding and Reference Material

Use as supplementary training resource for engineering teams entering LLM development. Notebooks pair with book's 300+ illustrations to explain tokenization, embeddings, fine-tuning trade-offs, and advanced generation techniques.

Implementation considerations

Requires Google Colab or local Python 3.x + PyTorch/transformers setup; see .setup/ folder for conda/dependency instructions.
Each notebook is standalone; cherry-pick examples relevant to your use case rather than running sequentially.
Notebooks rely on public model APIs and HuggingFace Hub; verify API rate limits and authentication before large-scale runs.
GPU memory (16GB Colab T4) may be insufficient for larger models; notebook code includes optional model size variants.
Outputs and results may vary slightly across OS/Python versions; book serves as ground truth for expected behavior.

When to avoid it — and what to weigh

Production Deployment Without Customization — Code is educational and optimized for Colab; not hardened for production workloads. Missing error handling, monitoring, scalability patterns, and security controls needed for production systems.
Dependency on Latest Research Methods — Repository is a book companion (created June 2024), so techniques may lag cutting-edge LLM research. Check publication date of underlying book before using for state-of-the-art implementations.
Commercial Model Training at Scale — Notebooks assume free/small-scale GPU access (Colab T4). Not designed for distributed training, multi-GPU orchestration, or enterprise infrastructure requirements.
Real-Time API or Inference Services — Notebooks are batch-oriented learning code. No built-in web service, API server, load balancing, or latency-optimized inference patterns for production serving.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), an OSI-approved permissive license. Permits commercial use, modification, and distribution with attribution and no warranty.

Apache-2.0 is permissive and OSI-compliant, allowing commercial use of the code. However, reproduction or commercial distribution of O'Reilly book content itself requires separate rights from the publisher. Use the code for your own LLM applications without restriction; do not republish the book or claim authorship of the educational material.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

Notebooks download pre-trained models from HuggingFace Hub and execute arbitrary Python in Colab; review model provenance and sanitize user inputs if adapting for production. No explicit security audit or vulnerability disclosure process documented. Use in controlled, trusted environments only.

Alternatives to consider

DeepLearning.AI Courses (short-form video)

Complementary resource; Andrew Ng-endorsed short courses on transformers. Video-first rather than hands-on code; covers similar conceptual ground.

LangChain / LlamaIndex Documentation

Production-focused LLM orchestration libraries with their own tutorials. More suited for building applications than learning internals; includes RAG and prompt chaining patterns.

HuggingFace Course & Transformers Documentation

Official transformer library docs and free course. More concise reference for specific models and API usage; less visual/educational narrative than this book.

Software development agency

Build on Hands-On-Large-Language-Models with DEV.co software developers

Explore the notebooks on GitHub, adapt them to your dataset, and let Devco help you scale from prototype to production. We specialize in AI application development and API integration.

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Hands-On-Large-Language-Models FAQ

Can I use these notebooks directly in production?

Not recommended without substantial refactoring. Notebooks are educational; they lack error handling, logging, monitoring, and scalability patterns. Extract logic and rebuild as production services.

Do I need the O'Reilly book to use the code?

No. Notebooks are self-contained and runnable. The book provides visual explanations and narrative; the code demonstrates concepts independently.

What models and APIs do the notebooks use?

Primarily HuggingFace pre-trained models (BERT, GPT, etc.) and public APIs. Some examples may use OpenAI or other third-party services; review each notebook for API key requirements.

Is there ongoing support or maintenance?

Unknown. Repository is a book companion with no formal SLA. Community issues exist (37 open); response time and bug fix priority are not guaranteed. Plan for self-maintenance if adopting.

Software development & web development with DEV.co

Adopting Hands-On-Large-Language-Models is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.

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