Instructions to use rednote-hilab/dots.ocr.base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rednote-hilab/dots.ocr.base with Transformers:

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

pipe = pipeline("image-text-to-text", model="rednote-hilab/dots.ocr.base", trust_remote_code=True)
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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("rednote-hilab/dots.ocr.base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rednote-hilab/dots.ocr.base with vLLM:

Install from pip and serve model

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

SGLang

How to use rednote-hilab/dots.ocr.base 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 "rednote-hilab/dots.ocr.base" \
    --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": "rednote-hilab/dots.ocr.base",
		"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 "rednote-hilab/dots.ocr.base" \
        --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": "rednote-hilab/dots.ocr.base",
		"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 rednote-hilab/dots.ocr.base with Docker Model Runner:
```
docker model run hf.co/rednote-hilab/dots.ocr.base
```

Update model card: Correct `library_name`, add paper/code/project links, and sync with GitHub README

by nielsr HF Staff - opened Dec 3, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+29

-24

nielsr

Dec 3, 2025

This PR significantly improves the model card for rednote-hilab/dots.ocr by:

Updating library_name: Changed the library_name in the metadata from dots_ocr to transformers. This is crucial as the model uses transformers.AutoModelForCausalLM and transformers.AutoProcessor, enabling the "How to use" widget on the Hub for easier adoption.
Adding prominent links: Introduced new badges at the top for the paper, GitHub repository, and live demo (project page) for better discoverability. The existing live demo link in the text has been replaced by the badge. The X (Twitter) link from the GitHub README has also been added.
Syncing content with GitHub README:
- Updated the "News" section with the latest release information.
- Revised the "Download Model Weights" section to include the ModelScope option.
- Refreshed the "vLLM inference" instructions under "Deployment" to reflect official vLLM integration (v0.11.0+) and simplified usage.
- Added a new "Huggingface inference with CPU" section.
- Updated the "Document Parse" section with the correct --num_thread argument and instructions for Transformers-based parsing.

These changes ensure the model card is up-to-date, more accurate, and more user-friendly, providing clearer guidance for researchers and users.

Update model card: Correct `library_name`, add paper/code/project links, and sync with GitHub READMEe6a6435d

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