Glance Qwen3-VL-4B

Probability-first visual decisions using Glance with Qwen3-VL-4B-Instruct.

This repository documents the public deployment and integration. It does not duplicate the base Qwen weights. The runtime downloads the pinned upstream model and uses Glance to project the model's hidden state directly onto yes/no or multiple-choice answers.

Try it

Replicate users can run the public model with their own account and credits.

Capabilities

  • Binary yes/no visual decisions
  • Multiple-choice visual classification with 2โ€“8 options
  • Probability distribution and confidence score
  • Structured output without token-by-token answer generation
  • Scale-to-zero Nvidia L40S deployment

Replicate API

import base64
import json
import replicate

image_base64 = base64.b64encode(open("image.jpg", "rb").read()).decode("ascii")

output = replicate.run(
    "untapped/glance-qwen3-vl-4b",
    input={
        "image_base64": image_base64,
        "question": "Is there a cat in this image?",
        "question_type": "yes_no",
        "options_json": "[]",
    },
)

print(output)

For multiple choice:

output = replicate.run(
    "untapped/glance-qwen3-vl-4b",
    input={
        "image_base64": image_base64,
        "question": "Which animal is shown?",
        "question_type": "choice",
        "options_json": json.dumps(["Cat", "Dog", "Bird"]),
    },
)

Output

{
  "answer": "Yes",
  "confidence": 0.98,
  "probabilities": [
    {"label": "Yes", "probability": 0.99},
    {"label": "No", "probability": 0.01}
  ]
}

Architecture

The deployment uses the native Glance Qwen backend. It does not prompt the model to generate an answer. Instead, Glance reads the model's final hidden state and scores the requested answer labels directly.

The base model is loaded from the pinned upstream Qwen/Qwen3-VL-4B-Instruct revision during worker setup.

Source and licenses

Use of the base model and Glance remains subject to their respective licenses.

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