#Multi-modal

Discover an extensive compilation of surveys on Large Language Models, addressing critical areas such as instruction tuning, human alignment, and multi-modal integrations. Understand challenges like hallucination and compression, with insights into their applications in domains like health, finance, and others. A valuable tool for researchers involved in LLM development.

Access an extensive collection of open-source datasets for instruction tuning, suitable for training both text and multi-modal chat-based large language models (LLMs) like GPT-4, ChatGPT, LLaMA, and Alpaca. This repository includes visual-instruction, text-instruction, and RLHF datasets, offering crucial resources for LLM fine-tuning and development. It provides multilingual and multi-task datasets created from both human and machine sources, which facilitate specific task solutions. Leverage these datasets and a comprehensive codebase to advance LLM research and development.

A comprehensive approach to image segmentation leveraging multi-modal prompts, known for its versatility and interactive features. It supports diverse prompt types, such as visual and textual cues, permitting customizable combination for enhanced user experience. Capable of managing complex scenarios with its compositional ability and maintaining session history for streamlined interaction. Recent updates showcase its integration into projects like LLaVA-Interactive and Set-of-Mark Prompting, underscoring its versatility and potential in image-editing contexts.