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 about 1 month ago

Commit

c5d66ce

verified ·

1 Parent(s): 7284b34

Model card: add logo, badges and About

Browse files

Files changed (1) hide show

README.md +11 -1

README.md CHANGED Viewed

@@ -35,6 +35,14 @@ language:
 # PepperOCR-VL
 PepperOCR-VL turns a page image into clean Markdown. Point it at a scanned or
 photographed document, an invoice, a paper, a textbook page or a slide, and it
 returns the text in reading order with headings, lists, tables and formulas
@@ -210,7 +218,9 @@ optional orientation classifier is PP-LCNet_x1_0_doc_ori from PaddleOCR
 ## About
-Built by [Sionic AI](https://sionic.ai). <!-- TODO: one line about the company and, if applicable, the hosted or larger PepperOCR offering with a link -->
 ## Citation

 # PepperOCR-VL
+<div align="center">
+  <img src="assets/sionic_ai_logo.webp" alt="Sionic AI" width="40%"/>
+  <p>
+    <a href="https://sionic.ai"><img src="https://img.shields.io/badge/Website-sionic.ai-0f766e?style=for-the-badge" alt="Website"/></a>
+    <a href="https://huggingface.co/sionic-ai"><img src="https://img.shields.io/badge/Hugging_Face-sionic--ai-f59e0b?style=for-the-badge" alt="Hugging Face"/></a>
+  </p>
+</div>
 PepperOCR-VL turns a page image into clean Markdown. Point it at a scanned or
 photographed document, an invoice, a paper, a textbook page or a slide, and it
 returns the text in reading order with headings, lists, tables and formulas
 ## 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