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ReFocus
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Trained_Model

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How to use from
vLLM
Install from pip and serve model 
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ReFocus/Trained_Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ReFocus/Trained_Model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker 
docker model run hf.co/ReFocus/Trained_Model
Quick Links

ReFocus

This repo contains the model for the paper "ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding"

🌐 Homepage |📑 Paper | 🔗 Code

Introduction

Alt text

ReFocus Finetuning

We follow the Phi-3 Cookbook for the supervised finetuning experiments.

Inference with the Finetuned Model

We release our best finetuned ReFocus model with full chain-of-thought data in this HuggingFace Link.

This model is finetuned based on Phi-3.5-vision, and we used the following prompt during evaluation

<|image|>\n{question}\nThought:

To enforce the model to generate bounding box coordinates to refocus, you could try this prompt:

<|image_1|>\n{question}\nThought: The areas to focus on in the image have bounding box coordinates:
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Paper for ReFocus/Trained_Model