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from typing import Dict, List, Any |
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from modelscope import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.tokenizer =AutoTokenizer.from_pretrained(path) |
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self.model = AutoModelForCausalLM.from_pretrained(path, device_map='auto') |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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sys_prompt=data["prompt"] |
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list=data["inputs"] |
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prompt=f"<|im_start|>system\n{sys_prompt}.<|im_end|>\n" |
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for item in list: |
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if item["role"]=="assistant": |
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content=item["content"] |
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prompt+=f"<|im_start|>assistant\n{content}<|im_end|>\n" |
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else: |
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content=item["content"] |
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prompt+=f"<|im_start|>user\n{content}<|im_end|>\n" |
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prompt+="<|im_start|>assistant\n" |
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encodeds = self.tokenizer.encode(prompt, return_tensors="pt") |
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model_inputs = encodeds.to("cuda") |
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self.model.to("cuda") |
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generated_ids = self.model.generate(model_inputs, max_new_tokens=1000, do_sample=True) |
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decoded = self.tokenizer.decode(generated_ids[0]) |
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return decoded |
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