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
Update citation to ECCV format
Browse files
README.md
CHANGED
|
@@ -67,13 +67,10 @@ Please refer to the main code repository for full training and evaluation instru
|
|
| 67 |
## Citation
|
| 68 |
|
| 69 |
```bibtex
|
| 70 |
-
@
|
| 71 |
-
title={Make Geometry Matter for Spatial Reasoning},
|
| 72 |
-
author=
|
| 73 |
-
|
| 74 |
-
|
| 75 |
-
archivePrefix={arXiv},
|
| 76 |
-
primaryClass={cs.CV},
|
| 77 |
-
url={https://arxiv.org/abs/2603.26639}
|
| 78 |
}
|
| 79 |
```
|
|
|
|
| 67 |
## Citation
|
| 68 |
|
| 69 |
```bibtex
|
| 70 |
+
@inproceedings{zhang2026geosr,
|
| 71 |
+
title = {Make Geometry Matter for Spatial Reasoning},
|
| 72 |
+
author = {Zhang, Shihua and Shen, Qiuhong and Wang, Shizun and Pan, Tianbo and Wang, Xinchao},
|
| 73 |
+
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
|
| 74 |
+
year = {2026}
|
|
|
|
|
|
|
|
|
|
| 75 |
}
|
| 76 |
```
|