ParaVT-8B

Name: ParaVT-8B
Author: ParaVT

by ParaVT

Agentic long-video model trained for parallel temporal tool calls

544dl/month

8B classparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

ParaVT/ParaVT-8B

Feature URI

mixpeek://video_extractor@v1/paravt_8b_v1

Overview

ParaVT-8B is a video-text-to-text model focused on long-video understanding through tool use. Its model card describes a parallel video tool-calling approach where the model can dispatch multiple temporal crop requests in one turn instead of walking sequentially through a video.

On Mixpeek, ParaVT is relevant when an agent needs to search long clips and then decide which time windows to inspect. It belongs after retrieval: use vector, transcript, or scene search to narrow the corpus, then let ParaVT reason over candidate spans and request focused temporal crops.

Architecture

Final post-RL ParaVT checkpoint based on Qwen3VLForConditionalGeneration and Qwen/Qwen3-VL-8B-Instruct. The release uses cold-start SFT followed by PARA-GRPO reinforcement learning for parseable, parallel temporal tool-calling behavior.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so ParaVT-8B 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: { "text-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.