This Post Has 11 Comments

1. It’s truly a gem
   I preordered “Hands-On Large Language Models” by Jay Alammar and Maarten Grootendorst as soon as it was available, and I’ve just received it. I’ve been eagerly anticipating this book, especially since Maarten is the author and maintainer of the BERTopic library, which has been crucial in many of my NLP projects. I’m grateful for his contributions, which have greatly supported my research efforts. This book captures that same spirit—it’s truly a gem!I’ve dabbled with LLMs before, particularly in areas like fine-tuning models and developing autonomous agents, but this book has significantly deepened my understanding. The way they break down complex concepts with crystal-clear visuals is not just educational, but also inspiring. For instance, their explanation of transformer attention mechanisms, paired with intuitive diagrams, made an otherwise abstract topic remarkably easy to grasp. It’s making me rethink how I communicate my own research—striving for a blend of depth, engaging visuals, and clear, relatable examples to make complex ideas accessible.When the authors say “hands-on,” they’re not kidding. Real datasets, practical coding projects, and digital resources—you’re not just reading; you’re doing. Jay and Maarten have managed to demystify the intricacies of large language models, particularly in chapters like the one on fine-tuning techniques, turning an intimidating topic (for those who had limited experience) into an engaging and approachable journey. Whether you’re looking to cover the basics or explore the finer points, this one’s a keeper.

   Reply

2. Gem of a book for Language AI and LLMs
   As a resident of Sweden, I was thrilled to discover the Kindle version of this book, allowing me to dive in immediately without waiting for international shipping. From the moment I started reading last week, I’ve been completely engrossed. The authors’ approach is brilliantly practical, seamlessly blending theoretical explanations of Language AI and LLMs with hands-on .ipynb exercises that bring concepts to life.The visuals are simply outstanding, offering incredibly detailed insights into the inner workings of LLMs. I particularly appreciate the balanced coverage of both open-source and licensed models, providing a comprehensive view of the field.I’ve been so impressed that I’ve already started sharing the book with a friend, who finds it equally enlightening. The clarity and depth of the content make it an invaluable resource for anyone interested in LLMs.I’m confident that this book will inspire countless innovations and breakthroughs in the field. Jay and Marteen have created a truly phenomenal work that’s both educational and inspiring. Thank you for this exceptional contribution to the AI community!

   Reply

3. The visuals are great! This book is easy to read despite the technical nature of the topic.
   The only book I finished cover-to-cover. This book covers LLM concepts thoroughly and provides detailed explanations, with complete examples, and diagrams.The information presented in the book is comprehensive, very comprehensible and logically organized. It covers core concepts of the transformer model, prompt engineering up to application (e.g. topic modeling, RAG, sentiment analysis) and fine-tuning your own models.The transformer model explanation was particularly clear, aided by helpful diagrams and examples. Topics like visual transformers and multimodal embeddings are invaluable.A must have for anyone working on LLM apps.

   Reply

4. Best LLM book available
   The authors do an amazing job of illustrating and explaining all things LLM

   Reply

5. Best parts are already included in Jay’s phenomenal blog posts
   I was really looking forward to reading this book to see how Jay extended his phenomenal blog posts. Unfortunately, there wasn’t much extension. Instead of digging into how transformers have evolved since 2018, the authors spent a bunch of time on non-generative tasks irrelevant to most readers and brief discussions of adjacent topics like RAG and prompting that are already a bit dated by the time the book went to print.I give this book three stars because the original posts are very clear and informative and having a printed copy is nice for some people, but there’s probably not much more here for people who are looking for that depth.If you want a very high level review of how LLMs work and can be applied, this is a great book.

   Reply

6. Best book to continue my studies in ML and AI
   Best book ever! I’m loving it, I truly recommend it

   Reply

7. amazing book
   i study more in statistics and computer vision. This is an amazing book that will help me grow in LLM.

   Reply

8. Fantastic book
   If you’re looking for a hands-on, no-nonsense guide to working with LLMs, this book is exactly what you need. This book stands out by taking a refreshingly practical approach, steering clear of dense mathematical theory and instead focusing on real-world applications.One of the strongest aspects of this book is its clarity. Rather than diving deep into the underlying theoretical details of LLMs, or giving you a laundry list of libraries and frameworks, the author provides clear, step-by-step instructions on how to implement and fine-tune these models for various tasks, including text generation, sentiment analysis, summarization, chatbots, and even more advanced use cases like RAG and agents.The code presented in Hands On Large Language Models is clear, well-structured, and easy to follow, however, given the rapid pace of development in AI, a few updates to the libraries were necessary when following along. Thankfully, these changes were minimal and easy to manage.

   Reply

9. completo y facil de entender, sobre el funcionamiento interno de los LLM, y las librerias de hugging face y langchain , heads (transfer knowledge), chat, RAG, prompt engineering, fine tunning etc. Un libro de referencia en este area

   Reply

10. Great book!

    Reply

11. I speak as a relative new comer to the field, but its been very helpful and informative.. A great purchase.

    Reply