You're reading from Transformers for Natural Language Processing and Computer Vision Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3

Product type Paperback

Published in Feb 2024

Publisher Packt

ISBN-13 9781805128724

Length 730 pages

Edition 3rd Edition

Languages

Processing

Tools

Processing

Concepts

Computer Vision

Author (1):

Denis Rothman

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Table of Contents (24) Chapters

Preface

1. What Are Transformers?

2. Getting Started with the Architecture of the Transformer Model FREE CHAPTER

3. Emergent vs Downstream Tasks: The Unseen Depths of Transformers

4. Advancements in Translations with Google Trax, Google Translate, and Gemini

5. Diving into Fine-Tuning through BERT

6. Pretraining a Transformer from Scratch through RoBERTa

7. The Generative AI Revolution with ChatGPT

8. Fine-Tuning OpenAI GPT Models

9. Shattering the Black Box with Interpretable Tools

10. Investigating the Role of Tokenizers in Shaping Transformer Models

11. Leveraging LLM Embeddings as an Alternative to Fine-Tuning

12. Toward Syntax-Free Semantic Role Labeling with ChatGPT and GPT-4

13. Summarization with T5 and ChatGPT

14. Exploring Cutting-Edge LLMs with Vertex AI and PaLM 2

15. Guarding the Giants: Mitigating Risks in Large Language Models

16. Beyond Text: Vision Transformers in the Dawn of Revolutionary AI

17. Transcending the Image-Text Boundary with Stable Diffusion

18. Hugging Face AutoTrain: Training Vision Models without Coding

19. On the Road to Functional AGI with HuggingGPT and its Peers

20. Beyond Human-Designed Prompts with Generative Ideation

21. Index

Appendix A: Revolutionizing AI: The Power of Optimized Time Complexity in Transformer Models

1. Appendix B: Answers to the Questions

WandB

WandB has advanced AI tracking capabilities. Imagine the future. Imagine that one day, OpenAI GPT-4 can understand WandB tracking information on its activity, such as the Auto-Big-bench.ipynb notebook. Once AI can do that, the door to functional AGI is wide open!

Open WandB_Prompts_Quickstart.ipynb from the chapter’s repository.

The notebook is self-explanatory. You will need a WandB key and an OpenAI key, as we saw in Chapter 8, Fine-Tuning OpenAI GPT Models.

You can run the notebook and follow the instructions to see how WandB can track OpenAI and LangChain activity.

Let’s focus on the following cell:

tool_span.add_named_result({"input": "search: google founded in year"}, {"response": "1998"})
chain_span.add_named_result({"input": "calculate: 2023 - 1998"}, {"response": "25"})
llm_span.add_named_result({"input": "calculate: 2023 - 1998", &quot...