 | *I am not affiliated by the team behind the models LCO models. tl;dr: I've been using LCO-Embed 7b for personal use, creating a vector db with all my files and search across image, audio and text. I am very impressed and surprised not more people know about it. I also made some GGUF quants for them to share :) License: Apache 2 Hey community! Back to post more about embeddings. So almost a month ago, a new benchmark was released for audio embeddings: "MAEB". And from their paper, there was one model that blew the others out of the water. Now a couple things: Topping a benchmark on day 0 is a really impressive feat because you can't really intentionally optimize a model for a benchmark that doesn't exist. And I wasn't expecting a model with audio, text, AND VISION to top it. The LCO embed paper was accepted to neurips last year, yet looking at their HF repo they barely have any downloads or likes. Please try it out and show them some love by liking their model on hf! The models are based on Qwen2.5 omni and they have a 3b size variant as well. If you want to use these models in llama.cpp (or ollama), I made some GGUF quants here to check out :) https://huggingface.co/collections/marksverdhei/lco-embedding-omni-gguf [link] [comments] |
The hidden gem of open-source embedding models (text+image+audio): LCO Embedding
Reddit r/LocalLLaMA / 3/13/2026
💬 OpinionTools & Practical UsageModels & Research
Key Points
- LCO-Embedding is an open-source multimodal embedding suite (text, image, audio) with a 7B model (and a 3B variant) based on Qwen2.5 Omni.
- It topped the MAEB benchmark on day zero for audio embeddings, demonstrating strong cross-modal performance.
- The LCO-Embedding paper was accepted to NeurIPS last year, though the HuggingFace repo has relatively few downloads, prompting calls to support and try the model.
- The post shares GGUF quantizations and links to integrate with llama.cpp/ollama and HuggingFace collections, highlighting practical deployment options.
