This book is a comprehensive guide aiming to demystify the world of transformers -- the architecture that powers Large Language Models (LLMs) like GPT and BERT. From PyTorch basics and mathematical foundations to implementing a Transformer from scratch, you'll gain a deep understanding of the inner workings of these models.

That's just the beginning. Get ready to dive into the realm of pre-training your own Transformer from scratch, unlocking the power of transfer learning to fine-tune LLMs for your specific use cases, explore advanced techniques like PEFT (Prompting for Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) for fine-tuning.

Step into the deployment of LLMs, delivering these state-of-the-art language models into the real-world, whether integrating them into cloud platforms or optimizing them for edge devices, this section ensures you're equipped with the know-how to bring your AI solutions to life. Whether you're a seasoned AI practitioner, a data scientist, or a curious developer eager to advance your knowledge on the powerful LLMs, this book is your ultimate guide to mastering these cutting-edge models.

By translating convoluted concepts into understandable explanations and offering a practical hands-on approach, this treasure trove of knowledge is invaluable to both aspiring beginners and seasoned professionals. Table of Contents1.

INTRODUCTION1. 1 What is AI, ML, DL, Generative AI and Large Language Model1.

2 Lifecycle of Large Language Models1. 3 Whom This Book Is For1.

4 How This Book Is Organized1. 5 Source Code and Resources2.

PYTORCH BASICS AND MATH FUNDAMENTALS2. 1 Tensor and Vector2.

2 Tensor and Matrix2. 3 Dot Product2.

4 Softmax2. 5 Cross Entropy2.

6 GPU Support2. 7 Linear Transformation2.

8 Embedding2. 9 Neural Network2.

10 Bigram and N-gram Models2. 11 Greedy, Random Sampling and Beam2.

12 Rank of Matrices2. 13 Singular Value Decomposition (SVD)2.

14 Conclusion3. TRANSFORMER3.

1 Dataset and Tokenization3. 2 Embedding3.

3 Positional Encoding3. 4 Layer Normalization3.

5 Feed Forward3. 6 Scaled Dot-Product Attention3.

7 Mask3. 8 Multi-Head Attention3.

9 Encoder Layer and Encoder3. 10 Decoder Layer and Decoder3.

11 Transformer3. 12 Training3.

13 Inference3. 14 Conclusion4.

PRE-TRAINING4. 1 Machine Translation4.

2 Dataset and Tokenization4. 3 Load Data in Batch4.

4 Pre-Training nn. Transformer Model4.

5 Inference4. 6 Popular Large Language Models4.

7 Computational Resources4. 8 Prompt Engineering and In-context Learning (ICL)4.

9 Prompt Engineering on FLAN-T54. 10 Pipelines4.

11 Conclusion5. FINE-TUNING5.

1 Fine-Tuning5. 2 Parameter Efficient Fine-tuning (PEFT)5.

3 Low-Rank Adaptation (LoRA)5. 4 Adapter5.

5 Prompt Tuning5. 6 Evaluation5.

7 Reinforcement Learning5. 8 Reinforcement Learning Human Feedback (RLHF)5.

9 Implementation of RLHF5. 10 Conclusion6.

DEPLOYMENT OF LLMS6. 1 Challenges and Considerations6.

2 Pre-Deployment Optimization6. 3 Security and Privacy6.

4 Deployment Architectures6. 5 Scalability and Load Balancing6.

6 Compliance and Ethics Review6. 7 Model Versioning and Updates6.

8 LLM-Powered Applications6. 9 Vector Database6.

10 LangChain6. 11 Chatbot, Example of LLM-Powered Application6.

12 WebUI, Example of LLM-Power Application6. 13 Future Trends and Challenges6.

14 ConclusionINDEXREFERENCESABOUT THE AUTHOR. .