80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor, BitsAndBytesConfig
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import torch
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import gc
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cache_dir = "./model_cache"
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print("Freeing up memory...")
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torch.cuda.empty_cache()
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gc.collect()
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model_kwargs = dict(
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#attn_implementation="eager", # Use "flash_attention_2" when running on Ampere or newer GPU
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torch_dtype=torch.bfloat16,#torch.float16,#torch.bfloat16, # What torch dtype to use, defaults to auto
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device_map="auto", # Let torch decide how to load the model
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)
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# BitsAndBytesConfig int-4 config
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model_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=model_kwargs["torch_dtype"],
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bnb_4bit_quant_storage=model_kwargs["torch_dtype"],
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)
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# default: Load the model on the available device(s)
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model = Qwen3VLForConditionalGeneration.from_pretrained(
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"Qwen/Qwen3-VL-2B-Instruct",cache_dir=cache_dir, **model_kwargs
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)
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# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
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# model = Qwen3VLForConditionalGeneration.from_pretrained(
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# "Qwen/Qwen3-VL-4B-Instruct",
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# dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2",
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# device_map="auto",
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# )
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processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-2B-Instruct",cache_dir=cache_dir)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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]
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# Preparation for inference
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inputs = processor.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_dict=True,
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return_tensors="pt"
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)
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inputs = inputs.to(model.device) #vedi che non gli passa immagine PIL come fa per gemma3
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# Inference: Generation of the output
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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