SenseNova-U1-8B-MoT

Name: SenseNova-U1-8B-MoT
Author: sensenova

by sensenova

8B any-to-any multimodal model for image understanding, generation, and editing

32.7Kdl/month

281likes

8Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

sensenova/SenseNova-U1-8B-MoT

Feature URI

mixpeek://image_extractor@v1/sensenova_u1_8b_mot_v1

Overview

SenseNova-U1-8B-MoT is an any-to-any multimodal model tagged for feature extraction, image-to-text, text-to-image, image editing, and custom-code inference. That mix matters for agents because perception is often not a single captioning call: an agent may need to inspect an image, generate an explanation, propose an edit, and preserve evidence of what changed.

On Mixpeek, SenseNova U1 fits pipelines that retrieve visual evidence first, then ask a multimodal model to explain or transform that evidence. It is especially relevant for creative QA, ad review, product imagery, and human-in-the-loop visual analysis.

Architecture

8B-class mixture-of-transformers style any-to-any multimodal model. Supports image-to-text, text-to-image, image editing, and feature extraction paths according to the Hugging Face model metadata.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so SenseNova-U1-8B-MoT runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
  "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      collection_id: "col_your_collection",
      documents: [
        {
          document_id: "asset-00412",
          // The model produces text, so it lands in payload. Give the
          // collection a text vector index and embed that text to make it
          // searchable rather than only filterable.
          payload: { extracted_text: modelOutput, source_key: "archive/2026/asset-00412" },
          vectors: { "image-embedding": embeddingOfModelOutput },
        },
      ],
    }),
  },
);

// Managed alternative, if this exact model is not the requirement:
// image_extractor@v1 runs google/siglip-base-patch16-224
// (768-d) over a bucket, with no inference of your own.