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import torch
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
class EndpointHandler:
def __init__(self, path=""):
# load the model
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-128k-instruct",
trust_remote_code=True
)
# create inference pipeline
self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
inputs = data.pop("inputs", data)
for key in ['stop_sequences', 'watermark', 'stop']:
if key in inputs:
del inputs[key]
parameters = data.pop("parameters", None)
# pass inputs with all kwargs in data
if parameters is not None:
prediction = self.pipeline(inputs, **parameters)
else:
prediction = self.pipeline(inputs)
# postprocess the prediction
return prediction |