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

Libraries

How to use rednote-hilab/dots.ocr 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", 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", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rednote-hilab/dots.ocr 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"
# 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",
		"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

SGLang

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

Add OlmOCRBench evaluation results

#45

by nielsr HF Staff - opened Feb 19

base: refs/heads/main

←

from: refs/pr/45

Discussion Files changed

+64

-0

Files changed (1) hide show

.eval_results/olmocrbench.yaml +64 -0

.eval_results/olmocrbench.yaml ADDED Viewed

	@@ -0,0 +1,64 @@

+- dataset:
+    id: allenai/olmOCR-bench
+    task_id: overall
+  value: 79.1
+  source:
+    url: https://huggingface.co/papers/2512.02498
+    name: dots.ocr technical report
+    user: nielsr
+- dataset:
+    id: allenai/olmOCR-bench
+    task_id: arxiv_math
+  value: 82.1
+  source:
+    url: https://huggingface.co/papers/2512.02498
+    name: dots.ocr technical report
+    user: nielsr
+- dataset:
+    id: allenai/olmOCR-bench
+    task_id: old_scans_math
+  value: 64.2
+  source:
+    url: https://huggingface.co/papers/2512.02498
+    name: dots.ocr technical report
+    user: nielsr
+- dataset:
+    id: allenai/olmOCR-bench
+    task_id: table_tests
+  value: 88.3
+  source:
+    url: https://huggingface.co/papers/2512.02498
+    name: dots.ocr technical report
+    user: nielsr
+- dataset:
+    id: allenai/olmOCR-bench
+    task_id: old_scans
+  value: 40.9
+  source:
+    url: https://huggingface.co/papers/2512.02498
+    name: dots.ocr technical report
+    user: nielsr
+- dataset:
+    id: allenai/olmOCR-bench
+    task_id: headers_footers
+  value: 94.1
+  source:
+    url: https://huggingface.co/papers/2512.02498
+    name: dots.ocr technical report
+    user: nielsr
+- dataset:
+    id: allenai/olmOCR-bench
+    task_id: multi_column
+  value: 82.4
+  source:
+    url: https://huggingface.co/papers/2512.02498
+    name: dots.ocr technical report
+    user: nielsr
+- dataset:
+    id: allenai/olmOCR-bench
+    task_id: long_tiny_text
+  value: 81.2
+  source:
+    url: https://huggingface.co/papers/2512.02498
+    name: dots.ocr technical report
+    user: nielsr