Qwen3-VL-30B-A3B-Instruct-Android-Control-84k

4
1 language
llama-factory
by
OfficerChul
Image Model
OTHER
30B params
New
4 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
68GB+ RAM
Mobile
Laptop
Server
Quick Summary

Qwen3-VL-30B-A3B-Instruct Android Control LoRA Fine-tuned Model Model Overview This model is a fine-tuned version of Qwen's `Qwen3-VL-30B-A3B-Instruct` base mo...

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
28GB+ RAM

Code Examples

Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)
Usagepythontransformers
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image

model_path = "OfficerChul/Qwen3-VL-30B-Android-Control"
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True,
    device_map="auto"
)

# Prepare your UI screenshot
image = Image.open("path/to/screenshot.png")
instruction = "Click on the Settings button"

# Prepare conversation
messages = [
    {
        "role": "system",
        "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
    },
    {
        "role": "user",
        "content": f"<image>{instruction}"
    }
]

# Process and generate
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)

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