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Mininglamp-2718/Mano-CUA-2.0-4B

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face
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Mano-CUA-2.0-4B

Mano-CUA is the Computer Use Agent model under the Mano open-source model series. It is a GUI-VLA (Visual Language Agent) model designed specifically for edge devices, capable of autonomously completing complex desktop GUI operations through visual understanding.

This is the fp16 full-precision version. For the MLX 8-bit quantized version optimized for Apple Silicon, see Mano-CUA-2.0-4B-MLX-8bit.

Main Capabilities

  • —Complex GUI Automation: Autonomously complete complex interface operations containing hundreds of interactive elements
  • —Cross-System Data Integration: Extract and integrate multi-source data through pure visual interaction without API interfaces
  • —Long-Task Planning Execution: Support enterprise-level business process automation of dozens to hundreds of steps
  • —Intelligent Report Generation: Automatically generate structured documents such as data analysis reports and work summaries

Technical Background

Mano-CUA builds upon the complete technical framework of the Mano project (see Mano Technical Report), employing the Mano-Action bidirectional self-reinforcement learning method, three-stage progressive training (SFT → Offline Reinforcement Learning → Online Reinforcement Learning), "think-act-verify" loop reasoning mechanism, and a closed-loop data circulation system to achieve high-precision GUI understanding and operation capabilities. The edge version is optimized through mixed-precision quantization, visual token pruning, and edge inference adaptation, enabling large-scale parameter models to run efficiently on edge devices like Mac mini/MacBook/computing sticks.

Quick Start

Requirements

  • —macOS with Apple Silicon (M1+)
  • —Python >= 3.12

Installation

bash
pip install transformers torch torchvision qwen-vl-utils

Single-Step Demo

python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
from PIL import Image

# 1. Load model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "Mininglamp-2718/Mano-CUA-2.0-4B",
    torch_dtype="auto",
    device_map="auto",
)
processor = AutoProcessor.from_pretrained("Mininglamp-2718/Mano-CUA-2.0-4B")

# 2. Load a screenshot
img = Image.open("screenshot.png")
ratio = 1280 / img.width
img = img.resize((1280, int(img.height * ratio)), Image.LANCZOS)

# 3. Build prompt
task = "Click the search bar and type hello"

prompt_text = f"""You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task.

## Output Format
<action>action</action>

## Action Space
open_app(app_name='') # Open an application by name.
open_url(url='') # Open a URL in the browser.
click(start_box='<|box_start|>(x1,y1)<|box_end|>')
type(content='') # type the content.
hotkey(key='') # Trigger a keyboard shortcut.
scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', direction='down or up or right or left', amount='scroll_amount')
drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>')
wait(duration='') # Sleep for specified duration (in seconds).
finish() # The task is completed.
stop(reason='') # If the item can not found in the image, give the reason

## User Instruction
{task}"""

messages = [
    {{"role": "system", "content": "You are a helpful assistant."}},
    {{"role": "user", "content": [
        {{"type": "image", "image": img}},
        {{"type": "text", "text": prompt_text}},
    ]}},
]

# 4. Run inference
text_input = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text_input], images=image_inputs, videos=video_inputs,
    padding=True, return_tensors="pt",
).to(model.device)

output_ids = model.generate(**inputs, max_new_tokens=512, temperature=0.0, do_sample=False)
output_ids = output_ids[:, inputs.input_ids.shape[1]:]
output = processor.batch_decode(output_ids, skip_special_tokens=True)[0]

print(output)

Output Format

The model outputs structured XML:

xml
<think>The search bar is at the top of the page...</think>
<action_desp>Click the search bar to focus it</action_desp>
<action>click(start_box='<|box_start|>(500,38)<|box_end|>')</action>

Coordinates are normalized to [0, 1000] range. To convert to pixel coordinates:

python
pixel_x = int(x / 1000 * screen_width)
pixel_y = int(y / 1000 * screen_height)

Full Action Space

ActionSyntaxDescription
open_appopen_app(app_name='')Open an application
open_urlopen_url(url='')Open a URL
click`click(start_box='<\box_start\>(x,y)<\box_end\>')`Left click
doubleclick`doubleclick(start_box='<\box_start\>(x,y)<\box_end\>')`Double click
triple_click`tripleclick(startbox='<\box_start\>(x,y)<\box_end\>')`Triple click (select line)
right_single`rightsingle(startbox='<\box_start\>(x,y)<\box_end\>')`Right click
hover`hover(start_box='<\box_start\>(x,y)<\box_end\>')`Mouse hover
typetype(content='text')Type text
hotkeyhotkey(key='cmd+c')Keyboard shortcut
hotkey_click`hotkeyclick(startbox='<\box_start\>(x,y)<\box_end\>', key='shift')`Modifier + click
scroll`scroll(start_box='<\box_start\>(x,y)<\box_end\>', direction='down', amount='3')`Scroll
drag`drag(start_box='<\box_start\>(x1,y1)<\box_end\>', end_box='<\box_start\>(x2,y2)<\box_end\>')`Drag and drop
waitwait(duration='2')Wait (seconds)
finishfinish()Task completed
stopstop(reason='...')Task infeasible
call_usercall_user()Request human help

Other Versions

VersionRepoDescription
fp16 (this)Mano-CUA-2.0-4BFull precision, for archival / re-quantization / GPU inference
MLX-8bitMano-CUA-2.0-4B-MLX-8bitMLX 8-bit quantized, recommended for Apple Silicon local inference

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