Instructions to use sionic-ai/PepperOCR-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sionic-ai/PepperOCR-VL with Transformers:

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

pipe = pipeline("image-text-to-text", model="sionic-ai/PepperOCR-VL")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sionic-ai/PepperOCR-VL")
model = AutoModelForMultimodalLM.from_pretrained("sionic-ai/PepperOCR-VL", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sionic-ai/PepperOCR-VL with vLLM:

Install from pip and serve model

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

SGLang

How to use sionic-ai/PepperOCR-VL 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 "sionic-ai/PepperOCR-VL" \
    --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": "sionic-ai/PepperOCR-VL",
		"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 "sionic-ai/PepperOCR-VL" \
        --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": "sionic-ai/PepperOCR-VL",
		"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 sionic-ai/PepperOCR-VL with Docker Model Runner:
```
docker model run hf.co/sionic-ai/PepperOCR-VL
```

Kindred1000 commited on 27 days ago

Commit

4f943a0

verified ·

1 Parent(s): 1ec498c

Model card: move About up, mention agentic document parsing

Browse files

Files changed (1) hide show

README.md +9 -6

README.md CHANGED Viewed

@@ -50,6 +50,15 @@ preserved. It is a 4.5B-parameter vision-language model fine-tuned from
 [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) for 17 languages across
 Latin and non-Latin scripts, with particular strength in Korean and Thai.
 ## What it outputs
 One page image in, one Markdown document out:
@@ -216,12 +225,6 @@ PepperOCR-VL is a fine-tune of Qwen3.5-4B (Apache-2.0, Alibaba Cloud); the
 optional orientation classifier is PP-LCNet_x1_0_doc_ori from PaddleOCR
 (Apache-2.0). Their notices are preserved in `NOTICE`.
-## About
-Built by [Sionic AI](https://sionic.ai), a Seoul-based AI company building
-document and language models for enterprise use. For commercial licensing,
-larger deployments or hosted inference, contact us through the website.
 ## Citation
 ```bibtex

 [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) for 17 languages across
 Latin and non-Latin scripts, with particular strength in Korean and Thai.
+## About Sionic AI
+PepperOCR-VL is built by [Sionic AI](https://sionic.ai), a Seoul-based AI
+company building document and language models for enterprise use. If you are
+building agentic document parsing, large-scale document processing pipelines,
+or want to use PepperOCR in a commercial product, we would like to hear from
+you: contact us through the website. Commercial licensing, larger deployments
+and hosted inference are available.
 ## What it outputs
 One page image in, one Markdown document out:
 optional orientation classifier is PP-LCNet_x1_0_doc_ori from PaddleOCR
 (Apache-2.0). Their notices are preserved in `NOTICE`.
 ## Citation
 ```bibtex