Instructions to use hitxhslhy/realocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hitxhslhy/realocr with Transformers:

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

pipe = pipeline("image-text-to-text", model="hitxhslhy/realocr")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("hitxhslhy/realocr")
model = AutoModelForMultimodalLM.from_pretrained("hitxhslhy/realocr", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hitxhslhy/realocr with vLLM:

Install from pip and serve model

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

SGLang

How to use hitxhslhy/realocr 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 "hitxhslhy/realocr" \
    --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": "hitxhslhy/realocr",
		"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 "hitxhslhy/realocr" \
        --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": "hitxhslhy/realocr",
		"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 hitxhslhy/realocr with Docker Model Runner:
```
docker model run hf.co/hitxhslhy/realocr
```

realocr

A 2B-parameter end-to-end document parsing model: page image in, full-page Markdown out. Built for the MDPBench protocol (17 languages, digital and photographed documents, 3,400 images).

This repository contains standalone bf16 weights for direct inference:

from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
model = Qwen3_5ForConditionalGeneration.from_pretrained("hitxhslhy/realocr")
processor = AutoProcessor.from_pretrained("hitxhslhy/realocr")

Requires transformers>=5.3.0 (architecture Qwen3_5ForConditionalGeneration, model_type: qwen3_5).

How to run inference

pip install -r inference/requirements.txt

python inference/infer_mdpbench.py \
    --model  . \
    --image-dir /path/to/MDPBench_img_public \
    --out-dir   /path/to/result/our_model

Writes one .md per page image (filename = image stem) — exactly what the MDPBench evaluation pipeline expects. See SUBMISSION_README.md for the full recipe and the exact inference settings, and VALIDATION.md for what was tested on real hardware.

Key settings that must be kept: official MDPBench prompt (embedded verbatim in inference/infer_mdpbench.py), greedy decoding, max_new_tokens=8192, native resolution (max_pixels=16777216), enable_thinking=False, and the Conv3d → Linear patch for the vision patch-embed (applied automatically by the script).

Note: the vision patch-embed is nn.Conv3d in stock transformers; on some GPUs this hits a pathological cuDNN kernel (~39.5 s per call, 99.8% of the forward pass). The provided script replaces it with a mathematically equivalent Linear matmul before loading.

Downloads last month: 2

Safetensors

Model size

2B params

Tensor type

BF16