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BLR2
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Model Details
Model Provider
BLR2
Model Tree
Qwen/Qwen3-VL-4B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom PIL import Image model = Qwen3VLForConditionalGeneration.from_pretrained("BLR2/qwen3-vl-4b-gui-agent-cropped", torch_dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained("BLR2/qwen3-vl-4b-gui-agent-cropped") image = Image.open("screenshot.png") # Should be cropped to 640x840instruction = "Click on the submit button" messages = [{ "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": instruction}, ],}] inputs = processor.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=50)response = processor.decode(outputs[0], skip_special_tokens=True)print(response) # "0.5234 0.7891"bash
vllm serve BLR2/qwen3-vl-4b-gui-agent-cropped --dtype bfloat16Outputs normalized coordinates: x y where both are in [0, 1].
Convert to pixels: px_x = int(x * image_width), px_y = int(y * image_height)