This repository provides code and notebooks for the "Transformers for Natural Language Processing and Computer Vision, Third Edition" book, targeting developers and researchers interested in generative AI, LLMs, and multimodal models. It offers practical examples for leveraging Hugging Face, OpenAI (including GPT-4o and o1), and Google Vertex AI for advanced NLP and CV tasks.

How It Works

The project is structured around the book's chapters, offering runnable Jupyter notebooks that demonstrate key concepts and implementations. It covers transformer architectures, fine-tuning, retrieval-augmented generation (RAG), interpretability tools (BertViz, LIME, SHAP), tokenization, LLM embeddings, and vision transformers (CLIP, DALL-E, GPT-4V). The approach emphasizes practical application across multiple platforms and model combinations.

Quick Start & Requirements

Notebooks can be run directly via provided Colab, Kaggle, Gradient, or StudioLab links. Specific notebooks may require Python, Hugging Face libraries, OpenAI API keys, and potentially GPU access for training or inference. Links to official documentation and demos are integrated within the notebook descriptions.

Highlighted Details

• Comprehensive coverage of leading platforms: Hugging Face, OpenAI (GPT-4o, o1 API), and Google Vertex AI (Gemini).
• Practical implementation of RAG, fine-tuning, and cross-platform chained models (e.g., HuggingGPT).
• In-depth exploration of vision transformers, including CLIP, DALL-E 3, and GPT-4V.
• Demonstrations of interpretability tools like BertViz and SHAP for model analysis.
• Inclusion of cutting-edge models and techniques, marked with 🐬, 🎏, and 🛠 emojis for new features, updates, and dependency tweaks.

Maintenance & Community

The repository is actively updated by Denis Rothman, with a changelog available for tracking improvements. Users are encouraged to raise issues for support. A Discord server is available for community engagement and updates.

Licensing & Compatibility

The repository's code is generally available for use, but specific licensing details for the book's content and any third-party libraries used should be verified. Compatibility for commercial use would depend on the underlying library licenses.

Limitations & Caveats

Some notebooks may require specific API keys or configurations for external services like OpenAI and Google Vertex AI. The rapid evolution of AI models means some examples might require updates to dependencies or API versions to function as intended.