Instructions to use roufaen/HieroSA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use roufaen/HieroSA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="roufaen/HieroSA")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("roufaen/HieroSA")
model = AutoModelForImageTextToText.from_pretrained("roufaen/HieroSA")
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

vLLM

How to use roufaen/HieroSA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "roufaen/HieroSA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "roufaen/HieroSA",
		"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/roufaen/HieroSA

SGLang

How to use roufaen/HieroSA 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 "roufaen/HieroSA" \
    --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": "roufaen/HieroSA",
		"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 "roufaen/HieroSA" \
        --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": "roufaen/HieroSA",
		"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 roufaen/HieroSA with Docker Model Runner:
```
docker model run hf.co/roufaen/HieroSA
```

Improve model card: add pipeline tag, library name, and metadata

by nielsr HF Staff - opened Jan 12

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-6

Files changed (1) hide show

README.md +9 -6

README.md CHANGED Viewed

@@ -1,10 +1,13 @@
 ---
-license: mit
-language:
-- en
 base_model:
 - Qwen/Qwen3-VL-4B-Instruct
 ---
 # HieroSA (Chinese)
 [Paper](https://arxiv.org/abs/2601.05508) | [GitHub](https://github.com/THUNLP-MT/HieroSA)
@@ -15,17 +18,17 @@ HieroSA supports both modern logographic scripts and ancient hieroglyphs 🌍, e
 ## More Details
-Please refer to our [GitHub Repository](https://github.com/THUNLP-MT/HieroSA) for more details about this model.
 ## Citation
 If you find our work helpful for your research, please consider citing our work.
-```plain
 @article{luo2026hierosa,
     title={Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors},
     author={Fuwen Luo and Zihao Wan and Ziyue Wang and Yaluo Liu and Pau Tong Lin Xu and Xuanjia Qiao and Xiaolong Wang and Peng Li and Yang Liu},
     journal={arXiv preprint arXiv:2601.05508},
     year={2026}
 }
-```

 ---
 base_model:
 - Qwen/Qwen3-VL-4B-Instruct
+language:
+- en
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 # HieroSA (Chinese)
 [Paper](https://arxiv.org/abs/2601.05508) | [GitHub](https://github.com/THUNLP-MT/HieroSA)
 ## More Details
+Please refer to our [GitHub Repository](https://github.com/THUNLP-MT/HieroSA) for more details about this model, including environment setup and inference scripts.
 ## Citation
 If you find our work helpful for your research, please consider citing our work.
+```bibtex
 @article{luo2026hierosa,
     title={Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors},
     author={Fuwen Luo and Zihao Wan and Ziyue Wang and Yaluo Liu and Pau Tong Lin Xu and Xuanjia Qiao and Xiaolong Wang and Peng Li and Yang Liu},
     journal={arXiv preprint arXiv:2601.05508},
     year={2026}
 }
+```