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from typing import Dict, List, Any |
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import logging |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.model = AutoModelForCausalLM.from_pretrained(path) |
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self.tokenizer = AutoTokenizer.from_pretrained(path) |
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self.tokenizer.use_default_system_prompt = False |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str`) |
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date (:obj: `str`) |
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Return: |
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A :obj:`list` | `dict`: will be serialized and returned |
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""" |
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system_prompt = data.pop("system_prompt") |
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message = data.pop("inputs") |
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conversation = [] |
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conversation.append({"role": "system", "content": system_prompt}) |
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conversation.append({"role": "user", "content": message}) |
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raise KeyError |
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logging.info(str(conversation)) |
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input_ids = self.tokenizer.apply_chat_template(conversation, return_tensors="pt") |
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input_ids = input_ids.to(self.model.device) |
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generate_kwargs = dict( |
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{"input_ids": input_ids}, |
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do_sample=True, |
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top_p=0.9, |
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top_k=50, |
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temperature=0.6, |
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num_beams=1, |
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repetition_penalty=1.2, |
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) |
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return self.model.generate(**generate_kwargs) |
