gemma-4-12B-it

Name: gemma-4-12B-it
Author: google

by google

Open 12B multimodal model for image, audio, and long-context agent perception

581Kdl/month

12Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

google/gemma-4-12B-it

Feature URI

mixpeek://image_extractor@v1/google_gemma4_12b_it_v1

Overview

Gemma 4 12B IT is an instruction-tuned open model from Google DeepMind. The model card describes Gemma 4 as multimodal, with text and image input across the family and audio support on the E2B, E4B, and 12B variants. It is a strong fit for agents that need to inspect retrieved images, short audio clips, or mixed evidence after first-stage search.

On Mixpeek, Gemma 4 12B belongs in the inspection layer. Use cheaper embeddings and filters to retrieve candidates, then ask Gemma to produce concise observations, answer bounded visual questions, or turn multimodal evidence into structured fields that downstream agents can cite.

Architecture

Instruction-tuned Gemma 4 multimodal model exposed through Hugging Face Transformers. The 12B checkpoint supports a 256K context window, multilingual text handling, image input, audio input, and text output.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so gemma-4-12B-it 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: { "multimodal-embedding": embeddingOfModelOutput },
        },
      ],
    }),
  },
);

// Managed alternative, if this exact model is not the requirement:
// universal_extractor@v1 runs google/gemini-embedding-2
// (3072-d) over a bucket, with no inference of your own.