Building Large Language Model: A comprehensive guide to creating a Large Language Models (LLMs)

by Et Tu Code

Unlock the secrets of language model development with this comprehensive guide that takes you from the basics to advanced techniques. Whether you're a beginner or an experienced developer, this book covers every aspect of creating your own large language model.



1. Introduction to Language Model Development

Understand the role of language models in NLP tasksExplore the capabilities of large language models2. Basics of Natural Language Processing

Dive into essential NLP text preprocessing, tokenization, sentiment analysisCompare different types of language models and their strengths3. Choosing the Right Framework

Explore popular TensorFlow, PyTorch, KerasMake informed decisions on selecting the best framework for your project4. Collecting and Preprocessing Data

Learn effective data collection and preprocessing techniquesMaster best practices for data augmentation and normalization5. Model Architecture Design

Explore architecture neural networks, transformers, attention mechanismsDesign an effective model tailored to your project needs6. Training and Fine-Tuning

Step-by-step guide for training and fine-tuning your language modelCover hyperparameter tuning, model evaluation, and selection7. Evaluation Metrics and Validation

Understand perplexity, accuracy, F1 scoreImplement validation techniques for accurate model performance8. Deploying Your Language Model

Tips for deploying in applications like chatbots and sentiment analysis toolsCover model serving and containerization best practices9. Fine-Tuning for Specific Use Cases

Adapt your model for text classification, question answering, and moreGuide on dataset preparation, model adaptation, and hyperparameter tuning10. Handling Ethical and Bias Considerations

Address ethical fairness, privacy, transparencyMitigate biases in your language model development11. Optimizing Performance and Efficiency

Techniques for performance quantization, pruning, knowledge distillationEmphasize model parallelism and distributed training12. Popular Large Language Models

Overview of BERT, RoBERTa, XLNet, and moreUnderstand their strengths, weaknesses, and applications13. Integrating Language Model with Applications

Tips on integrating with chatbots, voice assistants, and content generation systemsBest practices for integration frameworks and API design14. Scaling and Distributed Training

Importance of scaling and distributed training for large language parallelization, distributed optimization, GPU utiliz

104 pages, Kindle Edition

Published December 25, 2023