Instructions to use ChatDOC/OCRFlux-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChatDOC/OCRFlux-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="ChatDOC/OCRFlux-3B")
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("ChatDOC/OCRFlux-3B")
model = AutoModelForImageTextToText.from_pretrained("ChatDOC/OCRFlux-3B")
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 ChatDOC/OCRFlux-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChatDOC/OCRFlux-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChatDOC/OCRFlux-3B",
		"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/ChatDOC/OCRFlux-3B

SGLang

How to use ChatDOC/OCRFlux-3B 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 "ChatDOC/OCRFlux-3B" \
    --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": "ChatDOC/OCRFlux-3B",
		"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 "ChatDOC/OCRFlux-3B" \
        --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": "ChatDOC/OCRFlux-3B",
		"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 ChatDOC/OCRFlux-3B with Docker Model Runner:
```
docker model run hf.co/ChatDOC/OCRFlux-3B
```

ChatDOC commited on Jul 3, 2025

Commit

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@@ -22,6 +22,37 @@ This is a preview release of the OCRFlux-3B model that's fine tuned from Qwen2.5
 Quick links:
 - 🛠️ [Code](https://github.com/chatdoc-com/OCRFlux)
 ## Usage
 The best way to use this model is via the [OCRFlux toolkit](https://github.com/chatdoc-com/OCRFlux).

 Quick links:
 - 🛠️ [Code](https://github.com/chatdoc-com/OCRFlux)
+OCRFlux is a multimodal large language model based toolkit for converting PDFs and images into clean, readable, plain Markdown text. It aims to push the current state-of-the-art to a significantly higher level.
+Try the online demo: https://ocrflux.pdfparser.io/
+# Functions
+## On each page
+Convert into text with a natural reading order, even in the presence of multi-column layouts, figures, and insets
+Support for complicated tables and equations
+Automatically removes headers and footers
+## Cross-page table/paragraph merging
+Cross-page table merging
+Cross-page paragraph merging
+## Key features:
+Superior parsing quality on each page
+It respectively achieves 0.095 higher (from 0.872 to 0.967), 0.109 higher (from 0.858 to 0.967) and 0.187 higher (from 0.780 to 0.967) Edit Distance Similarity (EDS) on our released benchmark OCRFlux-bench-single than the baseline model olmOCR-7B-0225-preview, Nanonets-OCR-s and MonkeyOCR.
+Native support for cross-page table/paragraph merging (to our best this is the first to support this feature in all the open sourced project).
+Based on a 3B parameter VLM, so it can run even on GTX 3090 GPU.
+## News
+Jun 17, 2025 - v0.1.0 - Initial public launch and demo.
 ## Usage
 The best way to use this model is via the [OCRFlux toolkit](https://github.com/chatdoc-com/OCRFlux).