Image-Text-to-Text
Transformers
Safetensors
qwen3_5
ocr
document-parsing
multimodal
markdown
tables
formulas
key-information-extraction
figure-captioning
vllm
conversational
Instructions to use sekkit/OvisOCR3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sekkit/OvisOCR3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="sekkit/OvisOCR3") 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("sekkit/OvisOCR3") model = AutoModelForMultimodalLM.from_pretrained("sekkit/OvisOCR3", 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 sekkit/OvisOCR3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sekkit/OvisOCR3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sekkit/OvisOCR3", "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/sekkit/OvisOCR3
- SGLang
How to use sekkit/OvisOCR3 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 "sekkit/OvisOCR3" \ --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": "sekkit/OvisOCR3", "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 "sekkit/OvisOCR3" \ --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": "sekkit/OvisOCR3", "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 sekkit/OvisOCR3 with Docker Model Runner:
docker model run hf.co/sekkit/OvisOCR3
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00f7cea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | Analyze the document structure and extract text content. Return a single JSON array in natural reading order.
Each element follows this schema:
`{"bbox": [x_min, y_min, x_max, y_max], "category": "string", "content": "string"}`
Rules:
1. The bbox coordinates are integers scaled to [0, 1000).
2. The category is exactly one of:
["abstract", "algorithm", "aside_text", "chart", "content", "display_formula", "doc_title", "figure_title", "footer", "footer_image", "footnote", "formula_number", "header", "header_image", "image", "inline_formula", "number", "paragraph_title", "reference_content", "seal", "table", "text", "vertical_text", "vision_footnote"].
3. The content formatting depends on category:
- For "footer_image", "header_image", "image", or "chart": write as an HTML image tag <img src="images/bbox_{x_min}_{y_min}_{x_max}_{y_max}.jpg" />.
- For "display_formula" or "inline_formula": write in LaTeX.
- For "table": write as a standard HTML `<table>...</table>`.
- Otherwise: write in Markdown.
4. Output must be strictly valid JSON. Properly escape quotes and special characters inside content. |