Image-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
conversational
text-generation-inference
Instructions to use OpenCausaLab/CauSight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenCausaLab/CauSight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="OpenCausaLab/CauSight") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("OpenCausaLab/CauSight") model = AutoModelForMultimodalLM.from_pretrained("OpenCausaLab/CauSight", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenCausaLab/CauSight with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenCausaLab/CauSight" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenCausaLab/CauSight", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/OpenCausaLab/CauSight
- SGLang
How to use OpenCausaLab/CauSight 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 "OpenCausaLab/CauSight" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenCausaLab/CauSight", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "OpenCausaLab/CauSight" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenCausaLab/CauSight", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use OpenCausaLab/CauSight with Docker Model Runner:
docker model run hf.co/OpenCausaLab/CauSight
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base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
language:
- en
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
---
# CauSight: Learning to Supersense for Visual Causal Discovery
This repository contains the **CauSight** model, a novel vision-language model designed to perform visual causal discovery through causally aware reasoning. CauSight enables AI systems to infer cause-and-effect relations among visual entities across diverse scenarios, moving beyond mere perception. It integrates training data curation, Tree-of-Causal-Thought (ToCT) for synthesizing reasoning trajectories, and reinforcement learning with a designed causal reward. Experiments demonstrate that CauSight significantly outperforms models like GPT-4.1 on visual causal discovery.
This work is introduced in the following paper:
**[CauSight: Learning to Supersense for Visual Causal Discovery](https://arxiv.org/abs/2512.01827)** [📄 arXiv]
**Project Page and Code:** [https://github.com/OpenCausaLab/CauSight](https://github.com/OpenCausaLab/CauSight)
## 🔧 User Guide
### 1. Clone the Repository
```bash
git clone https://github.com/OpenCausaLab/CauSight.git
cd CauSight
```
### 2. Set Up the Environment
We recommend using **conda**:
```bash
conda create -n causight python=3.10
conda activate causight
pip install -r requirements.txt
pip install -e .
```
### 3. Download the Dataset (VCG-32K)
```bash
mkdir -p VCG-32K
pip install huggingface_hub
hf login
hf download OpenCausaLab/VCG-32K \
--repo-type dataset \
--local-dir ./VCG-32K
```
```bash
tar -xzf ./VCG-32K/COCO/images.tar.gz -C ./VCG-32K/COCO
tar -xzf ./VCG-32K/365/images.tar.gz -C ./VCG-32K/365
```
### 4. Download the CauSight Model
```bash
mkdir -p model
huggingface-cli download OpenCausaLab/CauSight \
--repo-type model \
--local-dir ./model
```
### 5. Evaluation
Start the model server, then run inference:
```bash
bash model_server.sh
python run_inference.py
```
### 6. Tree-of-Causal-Thought (If you want to make your own SFT data with ToCT.)
```bash
bash model_server.sh
python run.py
```
## Citation
If you find our work helpful or inspiring, please consider citing it:
```bibtex
@article{zhang2025causight,
title={CauSight: Learning to Supersense for Visual Causal Discovery},
author={Zhang, Yize and Chen, Meiqi and Chen, Sirui and Peng, Bo and Zhang, Yanxi and Li, Tianyu and Lu, Chaochao},
journal={arXiv preprint arXiv:2512.01827},
year={2025},
url={https://arxiv.org/abs/2512.01827}
}
``` |