Instructions to use ReFocus/Trained_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ReFocus/Trained_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ReFocus/Trained_Model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ReFocus/Trained_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ReFocus/Trained_Model with 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
- SGLang
How to use ReFocus/Trained_Model with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ReFocus/Trained_Model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "ReFocus/Trained_Model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ReFocus/Trained_Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ReFocus/Trained_Model with Docker Model Runner:
docker model run hf.co/ReFocus/Trained_Model
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license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
---
# <img src="assets/icon.png" width="35" /> ReFocus
This repo contains the model for the paper "ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding"
[**๐ Homepage**](https://zeyofu.github.io/ReFocus/) |[**๐ Paper**](https://huggingface.co/papers/2501.05452) | [**๐ Code**](https://github.com/zeyofu/ReFocus_Code)
# Introduction

# ReFocus Finetuning
We follow the [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook/blob/main/md/04.Fine-tuning/FineTuning_Vision.md) 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](https://huggingface.co/Fiaa/ReFocus).
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:
``` |