TomoroAI/tomoro-colqwen3-embed-4b is an advanced ColPali-style multimodal embedding model that can map text queries, visual documents (such as images, PDFs) or short videos into aligned multi-vector embeddings. This model combines the advantages of Qwen3-VL-4B-Instruct and Qwen3-Embedding-4B, performs excellently in the ViDoRe benchmark test, and significantly reduces the embedding space occupation.

MultimodalTransformersOther

MM-VAE Lyra is a multimodal variational autoencoder specifically designed for text embedding conversion, using geometric fusion technology. It combines the CLIP-L and T5-base models and can effectively handle the encoding and decoding tasks of text embeddings, providing an innovative solution for multimodal data processing.

GME-VARCO-VISION-Embedding is a multimodal embedding model that focuses on calculating the semantic similarity between text, images, and videos in a high-dimensional embedding space, and is particularly good at video retrieval tasks.

MultimodalTransformersEnglish

An efficient embedding model built on granite-vision-3.3-2b, designed for multimodal document retrieval and capable of processing documents containing tables, charts, infographics, and complex layouts.

MultimodalTransformersEnglish

Jina Embeddings v4 is a general-purpose embedding model designed for multimodal and multilingual retrieval, especially suitable for retrieving complex documents, including visually rich documents containing charts, tables, and illustrations.

MultimodalTransformersOther

UniME is a general embedding learning framework based on multimodal large models, significantly enhancing multimodal embedding capabilities through text discriminative knowledge distillation and hard negative sample-enhanced instruction tuning strategies.

MultimodalTransformersEnglish

VLM2Vec-V2 is a model for large-scale multimodal embedding tasks. By training a vision-language model, it provides more powerful embedding capabilities for multimodal data such as videos, images, and visual documents.

MultimodalTransformersEnglish

UniME is a general embedding learning model based on a multimodal large model, trained with 336×336 image resolution and ranked first on the MMEB leaderboard.

MultimodalTransformersEnglish

UniME is a general embedding learning model based on a multimodal large model, focusing on breaking down modal barriers to achieve cross-modal retrieval and embedding learning.

MultimodalTransformersEnglish

A multimodal embedding model based on Qwen2.5-Omni-7B, supporting unified embedding representations for cross-lingual text, images, audio, and video

MultimodalSafetensors

Ovis2-1B is the latest member of the Ovis series of multimodal large language models (MLLM), focusing on structural alignment of vision and text embeddings, featuring high performance for small models, enhanced reasoning capabilities, video and multi-image processing, and multilingual OCR enhancement.

MultimodalTransformersMultiple Languages

ColNomic Embed Multimodal 7B is a state-of-the-art multi-vector multimodal embedding model, excelling in visual document retrieval tasks with support for multilingual and unified text-image encoding.

MultimodalSafetensorsMultiple Languages

A 7-billion-parameter multimodal embedding model specialized in visual document retrieval tasks, achieving outstanding performance on the Vidore-v2 benchmark

MultimodalSafetensorsMultiple Languages

Nomic Embed Multimodal 3B is a cutting-edge multimodal embedding model focused on visual document retrieval tasks, supporting unified text-image encoding, achieving an outstanding performance of 58.8 NDCG@5 in the Vidore-v2 test.

MultimodalSafetensorsMultiple Languages

ColNomic Embed Multimodal 3B is a 3-billion-parameter multimodal embedding model specifically designed for visual document retrieval tasks, supporting unified encoding of multilingual text and images.

MultimodalSafetensorsMultiple Languages

Marqo-FashionSigLIP is a multimodal embedding model optimized for fashion product search, achieving a 57% improvement in MRR and recall rate compared to FashionCLIP.

MultimodalTransformersEnglish

mmE5 is a multimodal multilingual embedding model trained on Llama-3.2-11B-Vision, improving embedding performance through high-quality synthetic data and achieving state-of-the-art results on the MMEB benchmark.

MultimodalTransformersMultiple Languages

LLaVE-7B is a 7-billion-parameter multimodal embedding model based on LLaVA-OneVision-7B, capable of embedding representations for text, images, multiple images, and videos.

MultimodalTransformersEnglish

LLaVE-2B is a 2-billion-parameter multimodal embedding model based on Aquila-VL-2B, featuring a 4K token context window and supporting embeddings for text, images, multiple images, and videos.

MultimodalTransformersEnglish

LLaVE is a multimodal embedding model based on the LLaVA-OneVision-0.5B model, with a parameter scale of 0.5B, capable of embedding text, images, multiple images, and videos.

MultimodalTransformersEnglish