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

Libraries

How to use XCurOS/XCurOS-OCR-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XCurOS/XCurOS-OCR-GGUF",
	filename="XCurOS-OCR-F16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use XCurOS/XCurOS-OCR-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XCurOS/XCurOS-OCR-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf XCurOS/XCurOS-OCR-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XCurOS/XCurOS-OCR-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf XCurOS/XCurOS-OCR-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf XCurOS/XCurOS-OCR-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf XCurOS/XCurOS-OCR-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf XCurOS/XCurOS-OCR-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XCurOS/XCurOS-OCR-GGUF:F16

Use Docker

docker model run hf.co/XCurOS/XCurOS-OCR-GGUF:F16

LM Studio
Jan

vLLM

How to use XCurOS/XCurOS-OCR-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XCurOS/XCurOS-OCR-GGUF"
# 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-GGUF",
		"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-GGUF:F16

Ollama
How to use XCurOS/XCurOS-OCR-GGUF with Ollama:
```
ollama run hf.co/XCurOS/XCurOS-OCR-GGUF:F16
```

Unsloth Studio

How to use XCurOS/XCurOS-OCR-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for XCurOS/XCurOS-OCR-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for XCurOS/XCurOS-OCR-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for XCurOS/XCurOS-OCR-GGUF to start chatting

How to use XCurOS/XCurOS-OCR-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf XCurOS/XCurOS-OCR-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "XCurOS/XCurOS-OCR-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use XCurOS/XCurOS-OCR-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf XCurOS/XCurOS-OCR-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default XCurOS/XCurOS-OCR-GGUF:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use XCurOS/XCurOS-OCR-GGUF with Docker Model Runner:
```
docker model run hf.co/XCurOS/XCurOS-OCR-GGUF:F16
```

Lemonade

How to use XCurOS/XCurOS-OCR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XCurOS/XCurOS-OCR-GGUF:F16

Run and chat with the model

lemonade run user.XCurOS-OCR-GGUF-F16

List all available models

lemonade list

XCurOS-OCR · GGUF (F16, no quantization)

GGUF build of XCurOS-OCR, a compact 0.9B-parameter vision-language OCR model — runs locally with llama.cpp on CPU or GPU. Shipped in full precision F16, with no quantization.

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

🤗 Transformers / safetensors version: XCurOS/XCurOS-OCR.

Files

File	Role
`XCurOS-OCR-F16.gguf`	Language decoder (F16)
`mmproj-XCurOS-OCR-F16.gguf`	Vision projector (required for image input)

Quick start

# CPU-only (no GPU)
llama-mtmd-cli -m XCurOS-OCR-F16.gguf --mmproj mmproj-XCurOS-OCR-F16.gguf --image page.png -p "OCR" -ngl 0

# REST API server
llama-server -m XCurOS-OCR-F16.gguf --mmproj mmproj-XCurOS-OCR-F16.gguf -ngl 0

# Or auto-download this repo
llama-server -hf XCurOS/XCurOS-OCR-GGUF

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

_{*Gemini-3-Pro and GPT-5.2 are general-purpose VLMs, shown for reference only.}

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	-

_{XCurOS-OCR is optimized to run on commodity CPUs; it scores marginally below GLM-OCR while requiring no GPU.}

License

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

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GGUF

Model size

0.9B params

Architecture

glm4

Hardware compatibility

16-bit