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