import os os.environ['HF_ENDPOINT']="https://hf-mirror.com" from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor,AutoConfig from qwen_vl_utils import process_vision_info from quantization import apply_AWQ import torch #load base_model model_path="/root/autodl-tmp/Model/hub/Qwen/Qwen2___5-VL-7B-Instruct" model_name="Qwen/Qwen2.5-VL-7B-Instruct" config=AutoConfig.from_pretrained(model_path) import subprocess #although the bfloat si in the config, but model still initializes based on float32. So it make few minutes to load model to cpu. model =Qwen2_5_VLForConditionalGeneration(config) model.to(torch.bfloat16) # 获取 nvidia-smi 的输出 output = get_nvidia_smi_output() print(output) #print("model has loaded") processor = AutoProcessor.from_pretrained(model_path) #apply AWQ quantization_config_path="./weights/AWQ_config.json" quantization_weight_path="./weights/AWQ_weights.pth" res=apply_AWQ(model,quantization_config_path,quantization_weight_path=quantization_weight_path) # 获取 nvidia-smi 的输出 output = get_nvidia_smi_output() print(output) #inference messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "What does this photo show ?"}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) device="cuda" inputs = inputs.to(device) model.to(device) model.eval() # 获取 nvidia-smi 的输出 output = get_nvidia_smi_output() print(output) # Inference: Generation of the output with torch.no_grad(): generated_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)