Instructions to use prithivMLmods/UI-Mate-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/UI-Mate-9B-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/UI-Mate-9B-GGUF")
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/UI-Mate-9B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/UI-Mate-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/UI-Mate-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/UI-Mate-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/UI-Mate-9B-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-GGUF to start chatting

How to use prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/UI-Mate-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/UI-Mate-9B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/UI-Mate-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/UI-Mate-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.UI-Mate-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/UI-Mate-9B-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/UI-Mate-9B-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/UI-Mate-9B-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"

prithivMLmods commited on about 23 hours ago

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@@ -22,6 +22,9 @@ language:
 > **[UI-Mate-9B](https://huggingface.co/tencent/UI-Mate-9B)** is an open-weight foundation GUI agent from Tencent HY Frontier, built on Qwen3.5-9B, designed for long-horizon computer use across applications and operating systems by observing live screenshots and producing structured keyboard and mouse actions rather than relying on coordinate replay. Given a natural-language task instruction, screenshots, and interaction history, it outputs reasoning, a concise action description, and structured computer-use tool calls spanning mouse, keyboard, scrolling, waiting, user-interaction, and task-completion actions compatible with `pyautogui`, trained via supervised fine-tuning followed by online reinforcement learning in executable GUI environments. It's the mid-sized checkpoint in the UI-Mate family (alongside the larger UI-Mate-27B for general use and UI-Mate-democua-27B for demonstration-guided computer use), achieving strong results for its scale — 66.2 average score on OSWorld-Verified, 61.7 on WindowsAgentArena, and 34.00 strict success / 66.55 progress on OSWorkerBench — and is served via an OpenAI-compatible vLLM endpoint (retaining five screenshots of context by default) alongside a reference Python agent harness. Intended for research and development of screenshot-based GUI agents in controlled desktop environments, it requires an external runtime to execute predicted actions, is sensitive to application versions, screen layouts, and UI state changes, and Tencent recommends isolated/disposable environments, human confirmation before sensitive operations, and never treating model-reported success as proof of an achieved outcome; it's released under the Apache License 2.0.
 ## Model Files
    File Name | Quant Type | File Size | File Link |

 > **[UI-Mate-9B](https://huggingface.co/tencent/UI-Mate-9B)** is an open-weight foundation GUI agent from Tencent HY Frontier, built on Qwen3.5-9B, designed for long-horizon computer use across applications and operating systems by observing live screenshots and producing structured keyboard and mouse actions rather than relying on coordinate replay. Given a natural-language task instruction, screenshots, and interaction history, it outputs reasoning, a concise action description, and structured computer-use tool calls spanning mouse, keyboard, scrolling, waiting, user-interaction, and task-completion actions compatible with `pyautogui`, trained via supervised fine-tuning followed by online reinforcement learning in executable GUI environments. It's the mid-sized checkpoint in the UI-Mate family (alongside the larger UI-Mate-27B for general use and UI-Mate-democua-27B for demonstration-guided computer use), achieving strong results for its scale — 66.2 average score on OSWorld-Verified, 61.7 on WindowsAgentArena, and 34.00 strict success / 66.55 progress on OSWorkerBench — and is served via an OpenAI-compatible vLLM endpoint (retaining five screenshots of context by default) alongside a reference Python agent harness. Intended for research and development of screenshot-based GUI agents in controlled desktop environments, it requires an external runtime to execute predicted actions, is sensitive to application versions, screen layouts, and UI state changes, and Tencent recommends isolated/disposable environments, human confirmation before sensitive operations, and never treating model-reported success as proof of an achieved outcome; it's released under the Apache License 2.0.
+> [!NOTE]
+HF Papers: https://huggingface.co/papers/2608.15930
 ## Model Files
    File Name | Quant Type | File Size | File Link |