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 (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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 serve -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 serve -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

OpenClaw new

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

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "XCurOS/XCurOS-OCR-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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 / README.md

XCurOS

Upload XCurOS-OCR F16 GGUF (decoder + mmproj) + card

77a4b50 verified about 1 month ago

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	---
	license: mit
	library_name: llama.cpp
	pipeline_tag: image-text-to-text
	tags:
	- ocr
	- gguf
	- llama.cpp
	- vision-language-model
	- document-understanding
	- image-text-to-text
	language:
	- en
	- zh
	- multilingual
	---

	# 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](https://github.com/ggml-org/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`](https://huggingface.co/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

	```bash
	# 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 \|

	<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.

	---
	license: mit
	library_name: llama.cpp
	pipeline_tag: image-text-to-text
	tags:
	- ocr
	- gguf
	- llama.cpp
	- vision-language-model
	- document-understanding
	- image-text-to-text
	language:
	- en
	- zh
	- multilingual
	---

	# 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](https://github.com/ggml-org/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`](https://huggingface.co/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

	```bash
	# 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 \|

	<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.