tencent/WeMM-Embedding 9B/4B/2B

Reddit r/LocalLLaMA Models
multimodal-embedding open-source tencent-ai qwen3.5 vision-model text-embedding video-understanding

Summary

Tencent introduces WeMM-Embedding, a series of universal multimodal embedding models in 9B, 4B, and 2B sizes, built on Qwen3.5, supporting text, images, videos, and visual documents for embedding generation.

WeMM-Embedding-9B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 4,096-dimensional L2-normalized embedding. Audio input is not supported. https://huggingface.co/tencent/WeMM-Embedding-9B https://huggingface.co/tencent/WeMM-Embedding-4B https://huggingface.co/tencent/WeMM-Embedding-2B https://github.com/Tencent/WeMM-Embedding/blob/main/assets/WeMM_Embedding_tech_report.pdf
Original Article

Similar Articles

Nemotron-3-Embed 1B/8B

Reddit r/LocalLLaMA

NVIDIA released Nemotron-3-Embed 1B and 8B models, state-of-the-art multilingual text embedding models for retrieval and semantic similarity, optimized for RAG systems.

UEmbed: Unified Sparse and Dense Multimodal Embeddings

Hugging Face Daily Papers

UEmbed is a decoder-only multimodal embedding model that produces both sparse and dense representations in a single forward pass, released at 2B, 4B, and 9B scales. It outperforms existing public-data-trained multimodal embedding models on MMEB-v2 and remains competitive on BEIR.

Qwen3.7 Preview lands on Arena (1 minute read)

TLDR AI

Alibaba Qwen announces two major model releases: Qwen3-Omni, the first natively end-to-end omni-modal AI unifying text, image, audio and video, and Qwen3-Next-80B-A3B, an ultra-efficient MoE model with 3B activated parameters per token, achieving SOTA performance and 10x faster inference than Qwen3-32B.

tencent/EVIE-Preview-4.5B · Hugging Face

Reddit r/LocalLLaMA

EVIE-Preview-4.5B is a state-of-the-art multilingual Visual Document Retrieval model from Tencent that uses ColBERT-style late interaction and achieves leading performance on ViDoRe benchmarks with 128-dimensional token embeddings.

Qwen3.8-27B

Hacker News Top

Qwen released open weights for Qwen3.8-27B, a native multimodal dense model with 27B parameters that outperforms Qwen3.7-Plus, supports 262K native context extendable to 1M, and is licensed under Apache 2.0.