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

Libraries

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rednote-hilab/dots.mocr"
# 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.mocr",
		"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.mocr

SGLang

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

[DRAFT] fix: transformers 5.x compat (cache_position + kwargs naming)

by emanuelevivoli - opened 6 days ago

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

+11

-3

emanuelevivoli

6 days ago

Summary

This PR fixes two issues that prevent dots.ocr from working with transformers>=5.0:

1. cache_position TypeError on generation

In transformers 5.x, cache_position is no longer maintained in the generation loop. The current code does cache_position[0] == 0 which crashes with TypeError: 'NoneType' object is not subscriptable.

Fix: Use a combined check compatible with both transformers 4.x and 5.x — fall back to past_key_values is None when cache_position is unavailable.

2. _validate_model_kwargs ValueError for processor outputs

forward() uses **loss_kwargs instead of **kwargs. Transformers 5.x validation only recognizes **kwargs/**model_kwargs as catch-all params, causing processor outputs like mm_token_type_ids to fail validation.

Fix: Rename **loss_kwargs to **kwargs (functionally identical).

Backward compatibility

Both fixes maintain full backward compatibility with transformers 4.x.

fix: transformers 5.x compat (cache_position + kwargs naming)3bc1b7d3

emanuelevivoli changed pull request title from fix: transformers 5.x compat (cache_position + kwargs naming) to [DRAFT] fix: transformers 5.x compat (cache_position + kwargs naming) 1 day ago

emanuelevivoli

1 day ago

Same issues as dots.ocr PR/50

This PR has the same two issues reported on the dots.ocr PR:

DotsVLProcessor.__init__ missing video_processor — causes TypeError with transformers 4.57+ (note: dots.mocr may not trigger this if its config doesn't declare video tokens, but the code pattern is the same)
Predictions differ under transformers 5.x — model produces garbage output (single fullpage bbox) instead of multi-element layout JSON

Current workaround: Running dots.mocr evaluation with transformers 4.57.6.

Marking as draft until resolved. See dots.ocr PR/50 for detailed description.

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