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 commited on 10 days ago

Commit

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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.