--- license: apache-2.0 --- # Qwen3-VL-2B-Sono It's a huggingface repository of paper "A Multimodal Instruction Dataset and Benchmark for Ultrasound Understanding". ### 🚀 Quick Start ```python from transformers import Qwen3VLForConditionalGeneration, AutoProcessor import torch model_path = "Ssdaizi/Qwen3-VL-2B-Sono" # Load the model model = Qwen3VLForConditionalGeneration.from_pretrained( model_path, dtype=torch.bfloat16, device_map="auto", ) processor = AutoProcessor.from_pretrained(model_path) messages = [ { "role": "user", "content": [ { "type": "image", "image": "test1.png", }, { "type": "text", "text": "Is this a benign lesion or a malignant lesion?", } ], } ] # Preparation for inference inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", ) inputs = inputs.to(model.device) # Generate response generated_ids = model.generate( **inputs, max_new_tokens=512, top_p=0.8, top_k=20, temperature=0.7, repetition_penalty=1.0 ) # Remove input tokens from generated output generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False, ) print(output_text[0]) ```
