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 9, 2025

Commit

e685922

verified ·

1 Parent(s): fa3dd33

Update README.md

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Files changed (1) hide show

README.md +13 -3

README.md CHANGED Viewed

@@ -66,16 +66,26 @@ Requirements:
 - Docker with GPU support [(NVIDIA Toolkit)](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
 - Pre-downloaded model: [OCRFlux-3B](https://huggingface.co/ChatDOC/OCRFlux-3B)
-To use OCRFlux in a docker container, you can use the following example command:
 ```bash
 docker run -it --gpus all \
   -v /path/to/localworkspace:/localworkspace \
-  -v /path/to/test_pdf_dir:/test_pdf_dir/ \
   -v /path/to/OCRFlux-3B:/OCRFlux-3B \
-  chatdoc/ocrflux:latest /localworkspace --data /test_pdf_dir/* --model /OCRFlux-3B/
 ```
 #### Viewing Results
 Generate the final Markdown files by running the following command. Generated Markdown files will be in `./localworkspace/markdowns/DOCUMENT_NAME` directory.

 - Docker with GPU support [(NVIDIA Toolkit)](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
 - Pre-downloaded model: [OCRFlux-3B](https://huggingface.co/ChatDOC/OCRFlux-3B)
+To use OCRFlux in a docker container, you can use the following example command to start the docker container firstly:
 ```bash
 docker run -it --gpus all \
   -v /path/to/localworkspace:/localworkspace \
+  -v /path/to/test_pdf_dir:/test_pdf_dir \
   -v /path/to/OCRFlux-3B:/OCRFlux-3B \
+  --entrypoint bash \
+  chatdoc/ocrflux:latest
 ```
+and then run the following command on the docker container to parse document files:
+```bash
+python3.12 -m ocrflux.pipeline /localworkspace/ocrflux_results --data /test_pdf_dir/* --model /OCRFlux-3B/
+```
+The parsing results will be stored in `/localworkspace/ocrflux_results` directory.
 #### Viewing Results
 Generate the final Markdown files by running the following command. Generated Markdown files will be in `./localworkspace/markdowns/DOCUMENT_NAME` directory.