Multimodal Large Models: Algorithms, Applications and Fine-Tuning

This book covers the algorithm principles and practical applications of multi-modal large models in detail, and provides a wealth of fine-tuning technical details and practical cases. It is suitable for in-depth study and application by technicians who are interested in multi-modal large models. This book is divided into two parts: Algorithm Principles introduces in detail advanced deep learning models, including Transformer, GPT series, and deep generative models, from basic architecture, training methods to specific applications, including but not limited to Seq2Seq structure, position coding, attention mechanism, residual connection, variational autoencoder, GAN, ViT, CLIP, Stable Diffusion, and knowledge points on each model training practice. In addition, the emergent capabilities of pre-trained models, estimation of model parameters and communication data volume, and various techniques for distributed training, such as data parallelism, model parallelism, and mixed precision training, are discussed. Practical Application Chapter focuses on the practical application of deep learning models, especially the practical application of text and image generation, as well as code generation. Through specific practical projects, such as using Stable Diffusion for image generation and Code Llama for code generation, detailed details of fine-tuning technology are provided, and large model application frameworks such as LangChain are introduced.