Instructions to use XCurOS/XCurOS-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XCurOS/XCurOS-OCR with Transformers:

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

pipe = pipeline("image-text-to-text", model="XCurOS/XCurOS-OCR")
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 AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("XCurOS/XCurOS-OCR")
model = AutoModelForMultimodalLM.from_pretrained("XCurOS/XCurOS-OCR")
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 = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XCurOS/XCurOS-OCR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XCurOS/XCurOS-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": "XCurOS/XCurOS-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/XCurOS/XCurOS-OCR

SGLang

How to use XCurOS/XCurOS-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 "XCurOS/XCurOS-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": "XCurOS/XCurOS-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 "XCurOS/XCurOS-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": "XCurOS/XCurOS-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 XCurOS/XCurOS-OCR with Docker Model Runner:
```
docker model run hf.co/XCurOS/XCurOS-OCR
```

XCurOS-OCR / README.md

XCurOS

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	---
	license: mit
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- ocr
	- vision-language-model
	- document-understanding
	- image-text-to-text
	language:
	- en
	- zh
	- multilingual
	---

	# XCurOS-OCR

	XCurOS-OCR is a compact 0.9B-parameter vision-language OCR model. It converts document
	images — invoices, tables, formulas, forms, receipts, seals, handwriting, multi-column layouts —
	into clean Markdown / JSON / LaTeX. Runs on GPU or CPU with Transformers.

	> ✨ Lightweight & CPU-friendly — only 0.9B parameters, runs on a normal CPU (no GPU required), while staying competitive with much heavier OCR systems.

	> ⚡ Prefer a llama.cpp / GGUF build? Use [`XCurOS/XCurOS-OCR-GGUF`](https://huggingface.co/XCurOS/XCurOS-OCR-GGUF).

	## Usage

	```python
	import torch
	from transformers import AutoProcessor, AutoModelForImageTextToText

	MODEL_PATH = "XCurOS/XCurOS-OCR"
	processor = AutoProcessor.from_pretrained(MODEL_PATH)

	# CPU: torch_dtype=torch.float32 \| GPU: torch_dtype="auto", device_map="auto"
	model = AutoModelForImageTextToText.from_pretrained(
	MODEL_PATH, torch_dtype=torch.float32, low_cpu_mem_usage=True
	).to("cpu").eval()

	messages = [{"role": "user", "content": [
	{"type": "image", "url": "page.png"},
	{"type": "text", "text": "OCR:"},
	]}]
	inputs = processor.apply_chat_template(
	messages, tokenize=True, add_generation_prompt=True,
	return_dict=True, return_tensors="pt").to(model.device)
	inputs.pop("token_type_ids", None)
	out = model.generate(**inputs, max_new_tokens=4096)
	print(processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	## Benchmarks

	> XCurOS-OCR (ours) compared against leading OCR systems.
	> Bold = best among specialized OCR VLMs. `-` = not reported.
	> 💡 XCurOS-OCR is a lightweight 0.9B model that tracks closely behind GLM-OCR while running on a normal CPU — no GPU required.

	### Document understanding

	\| Task \| Benchmark \| XCurOS-OCR \| GLM-OCR \| PaddleOCR-VL-1.5 \| Deepseek-OCR2 \| MinerU2.5 \| dots.ocr \| Gemini-3-Pro* \| GPT-5.2* \|
	\|---\|---\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|
	\| Document Parsing \| OmniDocBench v1.5 \| 94.3 \| 94.6 \| 94.5 \| 91.1 \| 90.7 \| 88.4 \| 90.3 \| 85.4 \|
	\| Text Recognition \| OCRBench (Text) \| 93.6 \| 94.0 \| 75.3 \| 34.7 \| 75.3 \| 92.1 \| 91.9 \| 83.7 \|
	\| Formula Recognition \| UniMERNet \| 96.3 \| 96.5 \| 96.1 \| 85.8 \| 96.4 \| 90.0 \| 96.4 \| 90.5 \|
	\| Table Recognition \| PubTabNet \| 84.9 \| 85.2 \| 84.6 \| - \| 88.4 \| 71.0 \| 91.4 \| 84.4 \|
	\| Table Recognition \| TEDS_TEST \| 85.5 \| 86.0 \| 83.3 \| - \| 85.4 \| 62.4 \| 81.8 \| 67.6 \|
	\| Information Extraction \| Nanonets-KIE \| 93.3 \| 93.7 \| - \| - \| - \| - \| 95.2 \| 87.5 \|
	\| Information Extraction \| Handwritten-Forms \| 85.8 \| 86.1 \| - \| - \| - \| - \| 94.5 \| 78.2 \|

	### Capability breakdown

	\| Category \| XCurOS-OCR \| GLM-OCR \| PaddleOCR-VL-1.5 \| Deepseek-OCR2 \| MinerU2.5 \| dots.ocr \| Gemini-3-Pro* \| GPT-5.2* \|
	\|---\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|
	\| Code \| 84.4 \| 84.7 \| 75.8 \| 82.1 \| 82.9 \| 80.8 \| 86.9 \| 84.4 \|
	\| Real-world Table \| 91.0 \| 91.5 \| 86.1 \| - \| 70.8 \| 81.8 \| 90.6 \| 86.7 \|
	\| Handwriting \| 86.8 \| 87.0 \| 87.4 \| 73.8 \| 54.2 \| 71.7 \| 90.0 \| 78.0 \|
	\| Multi-language \| 68.9 \| 69.3 \| 54.8 \| 56.1 \| 27.8 \| 65.1 \| 86.2 \| 70.1 \|
	\| Seal \| 90.2 \| 90.5 \| 42.2 \| 40.4 \| - \| 63.0 \| 91.3 \| 58.8 \|
	\| Receipt (KIE) \| 94.1 \| 94.5 \| - \| - \| - \| - \| 97.3 \| 83.5 \|

	<sub>*Gemini-3-Pro and GPT-5.2 are general-purpose VLMs, shown for reference only.</sub>

	### Throughput

	\| Method \| Image Inputs (Pages/Sec) \| PDF Inputs (Pages/Sec) \|
	\|---\|:--:\|:--:\|
	\| XCurOS-OCR \| 0.66 \| 1.83 \|
	\| GLM-OCR \| 0.67 \| 1.86 \|
	\| PaddleOCR-VL-1.5 \| 0.39 \| 1.22 \|
	\| Deepseek-OCR2 \| 0.32 \| - \|
	\| MinerU2.5 \| 0.18 \| 0.48 \|
	\| dots.ocr \| 0.10 \| - \|

	<sub>XCurOS-OCR is optimized to run on commodity CPUs; it scores marginally below GLM-OCR while requiring no GPU.</sub>


	## License

	Released under the MIT License. See the `LICENSE` file in this repository.