Fine-tune large multimodal models using Amazon SageMaker

This article discusses how to fine-tune and deploy large multimodal models like LLaVA (Large Language and Vision Assistant) using Amazon SageMaker. LLaVA is trained to understand both visual and textual data, combining pre-trained language models like Vicuna or LLaMA with visual models like CLIP's visual encoder.

Specifically, the article covers:

• An overview of LLaVA's architecture and training process
• Preparing a dataset of image-text pairs with detailed annotations for fine-tuning
• Fine-tuning the LLaVA model using Amazon SageMaker, including techniques like LoRA and DeepSpeed
• Deploying the fine-tuned model on a SageMaker endpoint and making inferences
• Conclusion on the potential of multimodal models and future challenges