#Multimodal Models

The Xorbits Inference library streamlines deployment and management for advanced language, speech recognition, and multimodal models. It offers seamless hardware integration and flexible resource utilization, supporting state-of-the-art open-source models. Designed for developers and data scientists, Xorbits Inference facilitates deployment with a single command and supports distributed deployment alongside various interfaces like RESTful API, CLI, and WebUI. Stay updated with recent framework enhancements and models through extensive documentation and community support.

This repository offers a comprehensive review of significant conference papers in LLMs, focusing on multimodal models, PEFT, low-shot QA, RAG, LMMs interpretability, agents, and CoT. It provides valuable resources such as 'LLMs Nine-Story Demon Tower' and 'LLMs Interview Notes'. Detailed insights into numerous series, including Gemini and GPT-4V evaluations, help AI algorithm engineers bridge essential understanding. Explore methods and advances in LLMs, NLP, and recommendation systems across sectors like healthcare and law, highlighting practical applications.

TinyLLaVA Factory provides an open-source, modular framework for developing small-scale large multimodal models with ease. Built on PyTorch and HuggingFace, the platform emphasizes user-friendly implementation, feature expansion, and reliable training reproducibility. This codebase simplifies model customization and reduces coding errors, integrating advanced tools and models including CLIP, SigLIP, and Qformer. It supports diverse training methods such as LoRA tuning and offers updates like a visualization tool for prediction analysis, enhancing its functionality to effectively compete with larger models.

Groma enhances multimodal language models using innovative localized visual tokenization, effectively improving region comprehension and visual grounding. Known for its superior performance in referring expression comprehension, Groma excels on benchmarks like RefCOCO and RefCOCOg. Through a detailed training pipeline from datasets like COCO to the Groma Instruct dataset, Groma is tailored for applications bridging visual and linguistic contexts.