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untappedvc/glance-qwen3-vl-4b
glance-qwen3-vl-4b is a visual question answering model from untappedvc. Use it for the visual question answering task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Probability-first visual decisions using Glance with Qwen3-VL-4B-Instruct.
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Updated Sep 21, 2026
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From the Hugging Face model README
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.
Replicate users can run the public model with their own account and credits.
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"]),
},
)
{
"answer": "Yes",
"confidence": 0.98,
"probabilities": [
{"label": "Yes", "probability": 0.99},
{"label": "No", "probability": 0.01}
]
}
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.
Use of the base model and Glance remains subject to their respective licenses.