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Hands-On LLMs: A Practical Guide to Implementation
- Overview: The book "Hands-On Large Language Models" by Alammar and Grootendorst offers a practical, code-focused guide to understanding and implementing LLMs, covering theory, practical notebooks, and real-world applications. The target audience is Python programmers with basic ML knowledge.
- Core Topics: The book covers a wide range of topics, including tokenization, embeddings, Transformer architectures, text classification, clustering, topic modeling, prompt engineering, advanced text generation, semantic search, RAG, multimodal LLMs, and fine-tuning representation/generation models.
- Methodological Approach: The book emphasizes hands-on learning through practical examples in Google Colab, allowing users to execute code without local installation. It uses existing libraries and pretrained models for tasks like text classification, search, and clustering.
- Key Applications: Practical applications covered include copywriting, summarization, building semantic search systems, text classification/clustering, and implementing chatbots/search engines.
- Code Repository: An accompanying code repository provides practical code examples for implementing and experimenting with LLMs, complementing the book's concepts. According to additional sources, specific technical details such as dependencies, API details, and performance characteristics are available within the code repository and the book itself.
- Authors' Expertise: Jay Alammar provides expertise in visually explaining ML concepts, while Maarten Grootendorst contributes expertise in communicating complex ML concepts from a psychological point of view and is the author of open-source LLM packages.
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