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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Browse filesThis PR improves the model card metadata by adding the `image-text-to-text` pipeline tag, which ensures the model is correctly categorized on the Hugging Face Hub. It also adds explicit links to the paper, code repository, and project page for better accessibility.
README.md
CHANGED
|
@@ -1,6 +1,7 @@
|
|
| 1 |
---
|
| 2 |
-
license: apache-2.0
|
| 3 |
library_name: transformers
|
|
|
|
|
|
|
| 4 |
tags:
|
| 5 |
- vision-language-model
|
| 6 |
- spatial-reasoning
|
|
@@ -11,6 +12,8 @@ tags:
|
|
| 11 |
|
| 12 |
This repository hosts the released checkpoints for **GeoSR: Make Geometry Matter for Spatial Reasoning**.
|
| 13 |
|
|
|
|
|
|
|
| 14 |
## Checkpoints
|
| 15 |
|
| 16 |
| Folder | Branch / task | Notes |
|
|
@@ -65,6 +68,10 @@ Please refer to the main code repository for full training and evaluation instru
|
|
| 65 |
@misc{zhang2026geosr,
|
| 66 |
title={Make Geometry Matter for Spatial Reasoning},
|
| 67 |
author={Shihua Zhang and Qiuhong Shen and Shizun Wang and Tianbo Pan and Xinchao Wang},
|
| 68 |
-
year={2026}
|
|
|
|
|
|
|
|
|
|
|
|
|
| 69 |
}
|
| 70 |
-
```
|
|
|
|
| 1 |
---
|
|
|
|
| 2 |
library_name: transformers
|
| 3 |
+
license: apache-2.0
|
| 4 |
+
pipeline_tag: image-text-to-text
|
| 5 |
tags:
|
| 6 |
- vision-language-model
|
| 7 |
- spatial-reasoning
|
|
|
|
| 12 |
|
| 13 |
This repository hosts the released checkpoints for **GeoSR: Make Geometry Matter for Spatial Reasoning**.
|
| 14 |
|
| 15 |
+
[**Paper**](https://arxiv.org/abs/2603.26639) | [**Project Page**](https://suhzhang.github.io/GeoSR/) | [**Code**](https://github.com/SuhZhang/GeoSR)
|
| 16 |
+
|
| 17 |
## Checkpoints
|
| 18 |
|
| 19 |
| Folder | Branch / task | Notes |
|
|
|
|
| 68 |
@misc{zhang2026geosr,
|
| 69 |
title={Make Geometry Matter for Spatial Reasoning},
|
| 70 |
author={Shihua Zhang and Qiuhong Shen and Shizun Wang and Tianbo Pan and Xinchao Wang},
|
| 71 |
+
year={2026},
|
| 72 |
+
eprint={2603.26639},
|
| 73 |
+
archivePrefix={arXiv},
|
| 74 |
+
primaryClass={cs.CV},
|
| 75 |
+
url={https://arxiv.org/abs/2603.26639}
|
| 76 |
}
|
| 77 |
+
```
|