from transformers import Qwen3VLForConditionalGeneration, AutoProcessor # default: Load the model on the available device(s)model = Qwen3VLForConditionalGeneration.from_pretrained( "Qwen/Qwen3-VL-8B-Instruct", dtype="auto", device_map="auto") # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.# model = Qwen3VLForConditionalGeneration.from_pretrained(# "Qwen/Qwen3-VL-8B-Instruct",# dtype=torch.bfloat16,# attn_implementation="flash_attention_2",# device_map="auto",# ) processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-8B-Instruct") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], }] # Preparation for inferenceinputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt")inputs = inputs.to(model.device) # Inference: Generation of the outputgenerated_ids = model.generate(**inputs, max_new_tokens=128)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)
