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by Denis Rothman

This repo is continually updated and upgraded.

Last updated: August 14, 2025
📝 For details on updates and improvements, see theChangelog.

🚩If you see anything that doesn't run as expected, raise an issue, and we'll work on it!

Look for 🐬 to explorenew bonus notebooks such as and DeepSeek-R1 and OpenAI o1 reasoning models, Midjourney's API, Google Vertex AI Gemini's API, OpenAI asynchronous batch API calls!
Look for 🎏 to explore existing notebooks for thelatest model or platform releases, such as OpenAI's latest models (GPT-4o and o1).
Look for 🛠 to run existing notebooks withnew dependency versions and platform API constraints and tweaks.

This is the code repository forTransformers for Natural Language Processing and Computer Vision, published by Packt.

Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3

Transformers for Natural Language Processing and Computer Vision, Third Edition, exploresLarge Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used forNatural Language Processing (NLP) andComputer Vision (CV).

Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication.

• Learn how to pretrain and fine-tune LLMs
• Learn how to work with multiple platforms, such as Hugging Face, OpenAI, and Google Vertex AI
• Learn about different tokenizers and the best practices for preprocessing language data
• Implement Retrieval Augmented Generation and rules bases to mitigate hallucinations
• Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP
• Create and implement cross-platform chained models, such as HuggingGPT
• Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V

1. What Are Transformers?
2. Getting Started with the Architecture of the Transformer Model
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

Appendix: Answers to the Questions

You can run the notebooks directly from the table below:

Chapter	Colab	Kaggle	Gradient	StudioLab
Part I The Foundations of Transformer Models
Chapter 1: What are Transformers?
🛠O_1_and_Accelerators.ipynb ChatGPT_Plus_writes_and_explains_AI.ipynb
Getting started with DeepSeek-R1 Reasoning models. Integrated into HuggingFace Hub and Together.
🐬DeepSeek_Hugging_Face.ipynb
Chapter 2: Getting Started with the Architecture of the Transformer Model
🛠Multi_Head_Attention_Sub_Layer.ipynb positional_encoding.ipynb
Explaining DeepSeek's Training innovations; Part 1: RL
🐬DeepSeek_R1_Zero_RL.ipynb
Explaining DeepSeek's Training innovations; Part 2: RoPE
🐬DeepSeek_attention_head_RoPE.ipynb
Chapter 3: Emergent vs Downstream Tasks: the Unseen Depths of Transformers
From_training_to_emergence.ipynb Transformer_tasks_with_Hugging_Face.ipynb
Chapter 4: Advancements in Translations with Google Trax, Google Translate, and Google Bard
WMT_translations.ipynb Trax_Google_Translate.ipynb
Chapter 5: Diving into Fine-Tuning through BERT
BERT_Fine_Tuning_Sentence_Classification_GPU.ipynb
Chapter 6: Pretraining a Transformer from Scratch through RoBERTa
🎏 KantaiBERT.ipynb 🎏🛠 Customer_Support_for_X.ipynb
Part II: The Rise of Suprahuman NLP
Chapter 7: The Generative AI Revolution with ChatGPT
OpenAI_Models.ipynb OpenAI_GPT_4_Assistant.ipynb 🎏Getting_Started_GPT_4_API.ipynb(GPT-4o) 🎏GPT_4_RAG.ipynb(GPT-4o)
OpenAI Reasoning models: the o1 API
🐬OpenAI_Reasoning_models_o1_API.ipynb
OpenAI Reasoning models: the o1-preview API
🐬OpenAI_Reasoning_models_o3_API.ipynb
Chapter 8: Fine-tuning OpenAI Models
Fine_tuning_OpenAI_Models.ipynb 🎏Fine_tuning_GPT_4o_mini_SQuAd.ipynb
Fine-Tuning GPT-4.1
🎏Fine_tuning_GPT_4.1_mini_SQuAd.ipynb
🐬RAG as an alternative to fine-tuning: Building Scalable Knowledge-based RAG-drive Generative AI
Click here to access an open-source library to implement RAG
Chapter 9: Shattering the Black Box with Interpretable tools
BertViz_Interactive.ipynb Hugging_Face_SHAP.ipynb
Chapter 10: Investigating the Role of Tokenizers in Shaping Transformer Models
Tokenizers.ipynb Sub_word_tokenizers.ipynb 🛠Exploring_tokenizers.ipynb
Chapter 11: Leveraging LLM Embeddings as an Alternative to Fine-Tuning
🛠Embedding_with_NLKT_Gensim.ipynb 🎏Question_answering_with_embeddings.ipynb 🛠Transfer_Learning_with_Ada_Embeddings.ipynb
Chapter 12: Towards Syntax-Free Semantic Role Labeling with BERT and OpenAI's ChatGPT
Semantic_Role_Labeling_GPT-4.ipynb
Chapter 13: Summarization with T5 and ChatGPT
🛠Summerizing_Text_T5.ipynb Summarizing_ChatGPT.ipynb
Chapter 14: Exploring Cutting-Edge NLP with Google Vertex AI(PaLM and🐬Gemini with gemini-1.5-flash-001
Google_Vertex_AI.ipynb 🐬Google_Vertex_AI_Gemini.ipynb
Gemini 2.5 Flash showcase of Generative AI tasks
🐬Google_Gemini_2.5_Flash.ipynb
Chapter 15: Guarding the Giants: Mitigating Risks in Large Language Models<
🎏Auto_Big_bench.ipynb(GPT-4o,synchronous) 🎏Auto_Big_bench.ipynb(GPT-4o-mini,synchronous) 🐬GPT API Speed++ with Asynchronous Batch Calls! 🛠WandB_Prompts_Quickstart.ipynb Encoder_decoder_transformer.ipynb Mitigating_Generative_AI.ipynb
Part III: Generative Computer Vision: A New Way to See the World
Chapter 16: Vision Transformers in the Dawn of Revolutionary AI
ViT_CLIP.ipynb Getting_Started_DALL_E_API.ipynb 🎏GPT-4V.ipynb(GPT-4o)
Chapter 17: Transcending the Image-Text Boundary with Stable Diffusion
Stable_Diffusion_Keras.ipynb Stable__Vision_Stability_AI.ipynb Stable__Vision_Stability_AI_Animation.ipynb Text_to_video_synthesis.ipynb TimeSformer.ipynb
Stable Diffusion with Hugging Face
🐬Stable_Diffusion_Hugging_Face.ipynb
Chapter 18: Automated Vision Transformer Training
🛠Hugging_Face_AutoTrain.ipynb
Chapter 19: On the Road to Functional AGI with HuggingGPT and its Peers
Computer_Vision_Analysis.ipynb
Chapter 20: Generative AI Ideation Vertex AI, Langchain, and Stable Diffusion
Automated_Design.ipynb Midjourney_bot.ipynb 🎏Automated_Ideation.ipynb 🐬 MyMidjourney_API.ipynb

You cancreate an issue We will be glad to provide support!in this repository if you encounter one in the notebooks.

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Denis Rothman graduated from Sorbonne University and Paris-Cité University, designing one of the first patented encoding and embedding systems and teaching at Paris-I Panthéon Sorbonne.He authored one of the first patented word encoding and AI bots/robots. He began his career delivering a Natural Language Processing (NLP) chatbot for Moët et Chandon(LVMH) and an AI tactical defense optimizer for Airbus (formerly Aerospatiale).Denis then authored an AI optimizer for IBM and luxury brands, leading to an Advanced Planning and Scheduling (APS) solution used worldwide.LinkedIn