social-jev-1.7B-preview

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…

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

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…

Native MLX conversion of ewin-reg/WeMM-Embedding-2B-Quantized — the hybrid FP8-protected attention + group-16 INT4 checkpoint — that preserves its mixed-precision allocation instead of flattening everything to one precision. The source checkpoint is curvature-guided: it deliberately keeps the precision-sensitive path at ~8-bit (FP8 E4M3) and compresses only the quantization-robust masses to INT4. Uniform 4-bit quantization destroys that — the source README itself documents softmax attention collapse (DKL >= 7.50) when Q/K go to 4 bits. This build mirrors the Two deviations from the source, both improvements or forced by MLX: - 8-bit group-64 is finer than the source's per-tensor-scalar FP8…