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
| 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" | |
| [**π Homepage**](https://zeyofu.github.io/ReFocus/) |[**π Paper**](https://huggingface.co/papers/2501.05452) | [**π 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: | |
| ``` |