Learn how to fine-tune multi-modal vision and language models like LLaVA in this comprehensive tutorial. Explore the architectures of LLaVA 1.5, LLaVA 1.6, and IDEFICS, and understand their applications compared to ChatGPT. Dive into the intricacies of vision encoder architecture and multi-modal model design. Master the process of data creation, dataset preparation, and fine-tuning techniques. Gain hands-on experience with data loading, LoRA setup, and evaluation methods. Follow along with practical demonstrations on training, inference, and post-training evaluation. Clarify technical concepts and summarize key takeaways to enhance your skills in working with advanced vision and language models.
Fine-tuning Multi-modal LLaVA Vision and Language Models
Overview
Syllabus
Fine-tuning Multi-modal Models
Overview
LLaVA vs ChatGPT
Applications
Multi-modal model architecture
Vision Encoder architecture
LLaVA 1.5 architecture
LLaVA 1.6 architecture
IDEFICS architecture
Data creation
Dataset creation
Fine-tuning
Inference and Evaluation
Data loading
LoRA setup
Recap so far
Training
Evaluation post-training
Technical clarifications
Summary
Taught by
Trelis Research
