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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

class ModelHandler:
    def __init__(self):
        self.model = None
        self.tokenizer = None

    def load_model(self):
        # 加载模型和分词器
        self.model = AutoModelForCausalLM.from_pretrained("your-model-path")
        self.tokenizer = AutoTokenizer.from_pretrained("your-model-path")

    def predict(self, inputs):
        # 将输入转换为模型可以处理的格式
        inputs = self.tokenizer(inputs, return_tensors="pt")
        with torch.no_grad():
            outputs = self.model(**inputs)
        return outputs

handler = ModelHandler()
handler.load_model()

def handler(event, context):
    inputs = event["data"]
    outputs = handler.predict(inputs)
    return outputs

	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	class ModelHandler:
	def __init__(self):
	self.model = None
	self.tokenizer = None

	def load_model(self):
	# 加载模型和分词器
	self.model = AutoModelForCausalLM.from_pretrained("your-model-path")
	self.tokenizer = AutoTokenizer.from_pretrained("your-model-path")

	def predict(self, inputs):
	# 将输入转换为模型可以处理的格式
	inputs = self.tokenizer(inputs, return_tensors="pt")
	with torch.no_grad():
	outputs = self.model(**inputs)
	return outputs

	handler = ModelHandler()
	handler.load_model()

	def handler(event, context):
	inputs = event["data"]
	outputs = handler.predict(inputs)
	return outputs