Instructions to use prithivMLmods/OvisOCR2-F32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/OvisOCR2-F32-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/OvisOCR2-F32-GGUF", 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?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/OvisOCR2-F32-GGUF", dtype="auto", device_map="auto")

llama-cpp-python

How to use prithivMLmods/OvisOCR2-F32-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/OvisOCR2-F32-GGUF",
	filename="OvisOCR2.BF16.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 prithivMLmods/OvisOCR2-F32-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 prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

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 prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

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 prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/OvisOCR2-F32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/OvisOCR2-F32-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": "prithivMLmods/OvisOCR2-F32-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/prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/OvisOCR2-F32-GGUF 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 "prithivMLmods/OvisOCR2-F32-GGUF" \
    --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": "prithivMLmods/OvisOCR2-F32-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 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 "prithivMLmods/OvisOCR2-F32-GGUF" \
        --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": "prithivMLmods/OvisOCR2-F32-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/OvisOCR2-F32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/OvisOCR2-F32-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 prithivMLmods/OvisOCR2-F32-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 prithivMLmods/OvisOCR2-F32-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/OvisOCR2-F32-GGUF to start chatting

How to use prithivMLmods/OvisOCR2-F32-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

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": "prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/OvisOCR2-F32-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 prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

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 prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use prithivMLmods/OvisOCR2-F32-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

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 "prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M" \
  --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 prithivMLmods/OvisOCR2-F32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/OvisOCR2-F32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/OvisOCR2-F32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OvisOCR2-F32-GGUF-Q4_K_M

List all available models

lemonade list

OvisOCR2-F32-GGUF

OvisOCR2, developed by the ATH-MaaS team, is a compact 0.8B vision-language model for end-to-end page-level document parsing, built by post-training Qwen3.5-0.8B to directly convert document images into structured Markdown while preserving the natural reading order. It accurately extracts plain text, complex tables (as HTML), mathematical formulas (as LaTeX), and visual regions, making it suitable for OCR, document understanding, and digitization workflows. Despite its small size, OvisOCR2 achieves state-of-the-art performance with an overall score of 96.58 on OmniDocBench v1.6, becoming the first end-to-end model to surpass traditional pipeline-based approaches on the benchmark, while also leading PureDocBench with an Avg3 score of 75.06. Trained using a combination of real-world and synthetic data with a multi-stage recipe incorporating Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and On-Policy Distillation (OPD), OvisOCR2 delivers high-quality document parsing with a lightweight deployment footprint and native support for efficient inference through vLLM.

Model Files

File Name	Quant Type	File Size	File Link
OvisOCR2.BF16.gguf	BF16	1.52 GB	Download
OvisOCR2.F16.gguf	F16	1.52 GB	Download
OvisOCR2.F32.gguf	F32	3.02 GB	Download
OvisOCR2.Q3_K_L.gguf	Q3_K_L	491 MB	Download
OvisOCR2.Q3_K_M.gguf	Q3_K_M	466 MB	Download
OvisOCR2.Q3_K_S.gguf	Q3_K_S	435 MB	Download
OvisOCR2.Q4_K_M.gguf	Q4_K_M	529 MB	Download
OvisOCR2.Q4_K_S.gguf	Q4_K_S	505 MB	Download
OvisOCR2.Q5_K_M.gguf	Q5_K_M	578 MB	Download
OvisOCR2.Q5_K_S.gguf	Q5_K_S	564 MB	Download
OvisOCR2.Q8_0.gguf	Q8_0	812 MB	Download
OvisOCR2.mmproj-bf16.gguf	mmproj-bf16	207 MB	Download
OvisOCR2.mmproj-f16.gguf	mmproj-f16	207 MB	Download
OvisOCR2.mmproj-q8_0.gguf	mmproj-q8_0	116 MB	Download