Instructions to use SuhZhang/GeoSR-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SuhZhang/GeoSR-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="SuhZhang/GeoSR-Model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SuhZhang/GeoSR-Model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SuhZhang/GeoSR-Model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SuhZhang/GeoSR-Model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SuhZhang/GeoSR-Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SuhZhang/GeoSR-Model
- SGLang
How to use SuhZhang/GeoSR-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 "SuhZhang/GeoSR-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": "SuhZhang/GeoSR-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 "SuhZhang/GeoSR-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": "SuhZhang/GeoSR-Model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SuhZhang/GeoSR-Model with Docker Model Runner:
docker model run hf.co/SuhZhang/GeoSR-Model
Add pipeline tag and link to paper
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by nielsr HF Staff - opened
README.md
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---
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license: apache-2.0
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library_name: transformers
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tags:
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- vision-language-model
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- spatial-reasoning
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This repository hosts the released checkpoints for **GeoSR: Make Geometry Matter for Spatial Reasoning**.
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## Checkpoints
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| Folder | Branch / task | Notes |
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@misc{zhang2026geosr,
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title={Make Geometry Matter for Spatial Reasoning},
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author={Shihua Zhang and Qiuhong Shen and Shizun Wang and Tianbo Pan and Xinchao Wang},
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year={2026}
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}
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```
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---
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library_name: transformers
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license: apache-2.0
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pipeline_tag: image-text-to-text
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tags:
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- vision-language-model
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- spatial-reasoning
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This repository hosts the released checkpoints for **GeoSR: Make Geometry Matter for Spatial Reasoning**.
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[**Paper**](https://arxiv.org/abs/2603.26639) | [**Project Page**](https://suhzhang.github.io/GeoSR/) | [**Code**](https://github.com/SuhZhang/GeoSR)
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## Checkpoints
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| Folder | Branch / task | Notes |
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@misc{zhang2026geosr,
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title={Make Geometry Matter for Spatial Reasoning},
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author={Shihua Zhang and Qiuhong Shen and Shizun Wang and Tianbo Pan and Xinchao Wang},
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year={2026},
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eprint={2603.26639},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2603.26639}
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}
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```
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