#Vision Language Models

MLX-VLM provides tools to perform inference and fine-tune vision-language models on macOS. It supports efficient interaction through a command-line interface and Gradio chat UI, and is compatible with models like Idefics 2 and Phi3-Vision. With features like multi-image chat support and model enhancement using LoRA and QLoRA, MLX-VLM facilitates comprehensive image analysis. Installation is straightforward via pip.

Discover the innovative dual-encoder framework designed for large language models ranging from 2B to 34B, specialized in image comprehension and generation. This open-source project, built upon LLaVA, provides detailed resources for training, setup, and assessment. Engage with advanced vision-language integration via its demos and vast datasets such as COCO and GQA, available on Hugging Face Spaces. Follow recent model developments and performance evaluations.

ComfyUI VLM Nodes provide integration with top Vision Language Models (VLMs) for sophisticated prompt generation and structured outputs. Utilizing llama-cpp-python, these nodes support GGUF and ROCm formats, offering features like automatic prompt creation, image-to-music transformation, and advanced visual analysis with InternLM-XComposer2. This package caters to both new and experienced developers, offering multilingual capabilities and high-resolution image processing, making it suitable for diverse multimedia applications.

Examine the evolution of Vision-Language Models in diverse visual tasks like image classification and object detection. This survey reviews the architectural frameworks, pre-training techniques, and datasets of these models, highlighting their role in zero-shot learning. It categorizes VLM methods into pre-training, transfer, and distillation, providing thorough analysis and future research avenues. Featured in TPAMI's Top 50, this publication is essential for understanding vision-language AI.

ColPali provides a streamlined approach to document retrieval through the use of vision language models, optimizing multi-vector embeddings for enhanced content interpretation. By integrating the ColBERT architecture and PaliGemma model, it simplifies processes by removing the need for layout recognition and OCR. This method ensures efficient alignment of text and visual content, making it a valuable tool for those seeking effective document retrieval.