Visual RAG Unleashed: Harnessing ColQwen2.5 & Qwen2.5-VL-3B-Instruct for Next-Level AI

Visual RAG Unleashed: Harnessing ColQwen2.5 & Qwen2.5-VL-3B-Instruct for Next-Level AI In this ultimate AI guide, we deep dive into the world of multimodal AI, exploring how ColQwen2.5 and Qwen2.5-VL-3B-Instruct powers Visual RAG (Retrieval-Augmented Generation). In this video, we’ll break down how these cutting-edge models are transforming the way we process and interpret visual data, making them indispensable tools for researchers, developers, and AI enthusiasts alike. Whether you're new to visual RAG or looking to deepen your understanding of ColQwen2.5 and Qwen2.5-VL-3B-Instruct , this tutorial has something for everyone. Learn how these models combine state-of-the-art natural language processing (NLP) and computer vision capabilities to deliver unparalleled accuracy and efficiency in tasks like image captioning, visual question answering, and more. Key Topics Covered in This Video: - How Visual RAG can be implemented using colqwen2.5 based on Qwen2.5-VL-3B-Instruct with ColBERT strategy and Qwen2-VL-7B-Instruct for indexing and retrieval - How Qwen2.5-VL-3B-Instruct can be used for generating response. - How to set up and implement your own Visual RAG system Timestamps: 0:00 - Introduction to Visual RAG 1:25 - Architecture Overview of ColBERT 4:37 - Architecture Overview of ColQwen 7:59 - Deepdive into the use case and code implementation 27:53 - Q&A and Closing Thoughts GitHub link: https://github.com/ppanja/Visual-RAG-ColQwen2.5 Resources: ColBERT: https://arxiv.org/abs/2004.12832 Citation for COlBERT: @misc{khattab2020colbertefficienteffectivepassage, title={ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT}, author={Omar Khattab and Matei Zaharia}, year={2020}, eprint={2004.12832}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2004.12832}, } ColPali: https://arxiv.org/abs/2407.01449 Citation for COlPali: @misc{faysse2025colpaliefficien