CLIP-ViT-bigG-14-laion2B-39B-b160k

Name: CLIP-ViT-bigG-14-laion2B-39B-b160k
Author: laion

by laion

Open-source CLIP trained on 2B image-text pairs at giant scale

77Kdl/month

318likes

1.8Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

laion/CLIP-ViT-bigG-14-laion2B-39B-b160k

Feature URI

mixpeek://image_extractor@v1/laion_openclip_bigG_v1

Overview

OpenCLIP is the open-source reproduction of CLIP by the LAION/ML Foundations community. This ViT-bigG/14 variant was trained on LAION-2B (2 billion image-text pairs), achieving up to 85.4% ImageNet zero-shot accuracy, surpassing OpenAI's original CLIP.

On Mixpeek, OpenCLIP provides the highest-accuracy open-weight visual embeddings for text-to-image and image-to-image retrieval at scale.

Architecture

Vision Transformer (ViT-bigG/14) with ~1.8B vision parameters. Trained with contrastive learning on LAION-2B dataset for 39B samples seen. Produces 1280-dim embeddings projected to shared vision-text space.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so CLIP-ViT-bigG-14-laion2B-39B-b160k 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 vector name has to match a vector index on the collection.
          vectors: { "image-embedding": yourVector },
          payload: { source_key: "archive/2026/asset-00412" },
        },
      ],
    }),
  },
);

// 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.