Best of Multimodal — 2024

  1. 1
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    A crash course on RAG systems—Part 6

    Part 6 of the crash course on RAG systems explores how to build a more extensive and capable multimodal RAG system using CLIP embeddings, multimodal prompting, and tool calling. The post includes a unique dataset combining social media posts with images to provide a practical learning experience. The series covers everything from foundational components and evaluation to optimization and handling complex documents, aiming to help users implement reliable RAG systems and solve key NLP challenges with LLMs.

  2. 2
    Article
    Avatar of itsfossIt's Foss·2y

    TEN AI: Open Source Framework for Quickly Creating Real-Time Multimodal AI Agents

    TEN (Transformative Extensions Network) is an open-source framework designed to streamline the creation of real-time, multimodal AI agents capable of handling tasks involving voice, video, and text interactions. It offers features like real-time multimodal interactions, flexible extension support, edge-cloud integration, and visual workflow management. TEN aims to simplify the development process with minimal coding required, making it accessible for developers of all levels to build scalable AI projects.

  3. 3
    Article
    Avatar of aimodelsfyiAIModels.fyi·2y

    LLMs can speak in JPEG

    Large language models (LLMs) like JPEG-LM and AVC-LM can generate images and videos by outputting compressed file bytes in JPEG and H.264 formats. This approach outperforms specialized vision models on various benchmarks and highlights the potential for unified multimodal AI systems capable of handling text, images, and video through a common architecture. While this method shows promise in generating diverse visual elements, questions remain about its scalability, flexibility, and applicability to tasks like image classification and visual understanding.

  4. 4
    Article
    Avatar of newstackThe New Stack·2y

    Top 7 Tools for Building Multimodal AI Applications

    Multimodal AI leverages large language models to simultaneously process various data types like text, images, and videos. Key models include OpenAI's CLIP, Meta AI’s ImageBind, DeepMind’s Flamingo, OpenAI’s GPT-4o, Runway’s Gen2, Google’s Gemini, and Anthropic’s Claude 3. These models are applied in tasks ranging from image annotation and caption generation to creating promotional videos and processing long-form data.

  5. 5
    Article
    Avatar of hnHacker News·2y

    samuel-vitorino/lm.rs: Minimal LLM inference in Rust

    lm.rs enables running inference on Language Models locally on the CPU using Rust. The project now supports multimodal models like PHI-3.5-vision, in addition to text-only models like PHI-3.5-mini and Llama 3.2. Currently, image processing is being optimized to reduce latency. The guide includes steps for converting models to the LMRS format, compiling Rust code, and running both the CLI and WebUI interfaces. Future plans include adding sampling methods, testing larger models, and improving quantization support.

  6. 6
    Video
    Avatar of communityCommunity Picks·2y

    Gemini AI MultiModal Model Course

    This course teaches how to build an app using the Gemini multimodal AI model developed by Google. The app can analyze uploaded images and provide text-based responses to questions about those images. The course covers an introduction to Gemini, setting up the development environment, authentication, and building the app using Node.js and React. Google provided funding through a grant to make the course possible.

  7. 7
    Article
    Avatar of taiTowards AI·1y

    Building Multimodal RAG Application #5: Multimodal Retrieval from Vector Stores

    Multimodal RAG combines textual and visual data to improve the retrieval process, enhancing the accuracy and detail of large language models. This guide covers setting up multimodal retrieval using the LanceDB vector database, highlighting installation, configuration, and ingestion of text and image data using LangChain. It concludes with a practical walkthrough for performing efficient multimodal searches.

  8. 8
    Article
    Avatar of mlnewsMachine Learning News·2y

    MiniCPM-V 2.6: A GPT-4V Level Multimodal LLMs for Single Image, Multi-Image, and Video on Your Phone

    MiniCPM-V 2.6 is a cutting-edge multimodal LLM built on SigLip-400M and Qwen2-7B frameworks with 8 billion parameters. It excels in single image, multi-image, and video understanding, achieving top scores in benchmarks like OpenCompass, Mantis-Eval, and Video-MME. The model offers strong OCR capabilities, efficient token density, and is optimized for real-time video understanding on devices with limited resources. It supports various formats and setups, making it versatile and user-friendly for a wide range of visual processing tasks.

  9. 9
    Article
    Avatar of mlnewsMachine Learning News·2y

    This AI Paper by Meta FAIR Introduces MoMa: A Modality-Aware Mixture-of-Experts Architecture for Efficient Multimodal Pre-training

    MoMa, developed by Meta's FAIR, is an innovative modality-aware mixture-of-experts (MoE) architecture designed for efficient multimodal pre-training. It addresses the computational challenges in multimodal AI by employing modality-specific expert groups and advanced routing techniques. MoMa significantly improves processing efficiency by integrating text and image data effectively, achieving substantial reductions in floating-point operations (FLOPs) compared to traditional dense models. This advancement paves the way for more efficient and capable multimodal AI systems.

See all Multimodal archives