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 2โ8 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
- Base model: Qwen3-VL-4B-Instruct
- Glance: yoheinakajima/glance
- Deployment: Replicate model
Use of the base model and Glance remains subject to their respective licenses.
Model tree for untappedvc/glance-qwen3-vl-4b
Base model
Qwen/Qwen3-VL-4B-Instruct