kb-wemm-embedding-2b-mlx-mixed

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Feature extraction

jina-embeddings-v5-omni-small

Jina AI

Average score vs. parameter count across image (MIEB-Lite), video (MMEB-V), and audio (MAEB) benchmarks — jina-v5-omni-nano and jina-v5-omni-small define the open-weight frontier (Table 1 in the ArXiv report). jina-embeddings-v5-omni-small is a multimodal embedding model that accepts text, images, video, and audio and produces embeddings in a shared vector space aligned with the text-only jinaai/jina-embeddings-v5-text-small — so you can index with text and query with any modality, no reindexing. For a more compact alternative, see jinaai/jina-embeddings-v5-omni-nano. This is the base repository — it holds all task adapters (retrieval, classification, clustering, text-matching). For a…

A taxonomy-informed sparse DNA foundation model for microbial genomics. MicroGlot is a 23-layer decoder-only mixture-of-experts transformer pretrained on 378.3 billion nucleotides from 3.70 million sequences across 99,700 microbial species, spanning bacteria, archaea, fungi, protists, viruses and plasmids. It encodes the taxonomic hierarchy as hyperbolic (Poincaré) embeddings and uses them both as an input token and to steer expert routing. Paper: A Taxonomy-Informed Sparse DNA Foundation Model for Microbial Genomics\ A species is known if it is one of the 99,700 pretraining species (check with tokenizer.hasspecies(name)). For taxonomic classification tasks, use MicroGlot-plain, as…

Model · Feature extraction

Vela-1.0-Omni-Mini

vLLM Semantic Router

Text, images, speech and environmental sounds in one embedding space. Vela Omni Mini supports multimodal search, routing and clustering with normalized vectors that can be compared directly. Scores are 0–100; higher is better. The comparison uses the same held-out evaluation examples, complete retrieval pools and 128-token text cap for both models. Bold marks improvement over the original large model. These known test pools are reused across releases. The common protocol uses labeled TRAIN prototypes for text classification and all matching positives for retrieval; it is separate from official MTEB classification. All 14 metrics, Macro-F1, exact counts and uncertainty. The primary metric…

Model · Feature extraction

patent_retriever_multilingual_nemotron_v0.1

Elplaguister

Dense retriever for patent prior-art search (abstract → abstract), fine-tuned from on patent-family pairs (same invention filed in different jurisdictions / languages). Trained for Korean ↔ English cross-lingual and monolingual patent retrieval. Do not use the base model's asymmetric convention (query: / passage: ). It raises no error but silently degrades retrieval quality, because training was symmetric. corpus (other family members masked; one relevant document per query). crosssame: translation pairs (KR↔EN), crosssimilar: cross-lingual same-family non-translation pairs, monosimilar: monolingual same-family pairs. Scores on the 50k-document subset; full-corpus (209,671 documents)…

Model · Feature extraction

vit_giant_patch14_224.dinobloom

Laureηt Fainsin

Model Type: Feature backbone; Params: 1136M (giant); Image size: 224 x 224 x 3; Patch size: 14 x 14 x 3; Repository: github.com:marrlab/DinoBloom; Original Weights: Zenodo.