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from transformers import GenerationConfig, AutoProcessor, AutoTokenizer, AutoModelForImageTextToText, Qwen2_5_VLForConditionalGeneration |
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from qwen_vl_utils import process_vision_info |
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model_name = "Qwen/Qwen2.5-VL-7B-Instruct" |
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class EndpointHandler: |
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def __init__(self): |
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self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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model_name, torch_dtype="auto", device_map="cuda" |
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) |
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self.processor = AutoProcessor.from_pretrained(model_name) |
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async def __call__(self, data): |
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messages = data.get("messages") |
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gen_cfg = GenerationConfig( |
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max_new_tokens=2048, |
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no_repeat_ngram_size=3, |
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repeat_penalty=1.2, |
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early_stopping=True, |
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) |
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text = self.processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = self.processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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generated_ids = self.model.generate(**inputs, generation_config=gen_cfg) |
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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 = self.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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return output_text[0] |
