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80aaa00 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | from typing import Dict, List, Any
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
import torch
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
model = AutoModelForCausalLM.from_pretrained(
path, torch_dtype=torch.float16, load_in_8bit=True, device_map="auto"
)
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.model.eval()
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
'''
Args:
data (:dict:):
The payload with the text prompt and generation parameters.
'''
# process input
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
# preprocess
input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
# pass inputs with all kwargs in data
if parameters is not None:
outputs = self.model.generate(input_ids=input_ids, **parameters)
else:
outputs = self.model.generate(input_ids=input_ids)
# postprocess the prediction
prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return [{"generated_text": prediction}] |