Tarsier2-7b-0115

Name: Tarsier2-7b-0115
Author: omni-research

by omni-research

SOTA video description: detailed, temporally-aligned captions that outperform GPT-4o

45Kdl/month

7Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

omni-research/Tarsier2-7b-0115

Feature URI

mixpeek://video_extractor@v1/omni_tarsier2_7b_v1

Overview

Tarsier2 generates highly detailed, temporally-aligned video descriptions. It achieves state-of-the-art across 16 video understanding benchmarks spanning captioning, QA, grounding, and hallucination detection, outperforming GPT-4o and Gemini 1.5 Pro on video description quality.

For video RAG, detailed description quality is critical: the richer the textual representation of video content, the better text-based retrieval performs. Tarsier2 produces the kind of dense, accurate descriptions that make video truly searchable.

Architecture

7B parameter model from ByteDance research. Optimized for generating faithful, temporally-ordered descriptions that minimize hallucination while maximizing detail density.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so Tarsier2-7b-0115 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.