Keye-VL-8B-Preview

Name: Keye-VL-8B-Preview
Author: Kwai-Keye

by Kwai-Keye

Short-video VLM with temporal precision via 3D positional encoding

38Kdl/month

8Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

Kwai-Keye/Keye-VL-8B-Preview

Feature URI

mixpeek://video_extractor@v1/kwai_keye_vl_8b_v1

Overview

Keye-VL is a multimodal VLM specifically engineered for short-form video understanding while maintaining general vision-language abilities. Built by Kuaishou (operator of one of the world's largest short-video platforms), it uses 3D RoPE for unified text/image/video processing with one-to-one correspondence between position encoding and absolute time.

Trained on 600B+ tokens with video emphasis, Keye-VL excels at understanding the dominant content format of the modern internet: short clips.

Architecture

8B parameter model built on Qwen3-8B + SigLIP vision encoder. Uses 3D RoPE (Rotary Position Embedding) for unified spatial-temporal encoding, enabling precise temporal grounding in video content.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so Keye-VL-8B-Preview 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.