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from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel
import os
class EndpointHandler():
def __init__(self, path=""):
HF_TOKEN = os.getenv("HF_TOKEN")
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-3.1-8B-Instruct",
torch_dtype=None,
device_map="auto",
token=HF_TOKEN
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct", trust_remote_code=True, token=HF_TOKEN)
model = PeftModel.from_pretrained(model, path)
model = model.merge_and_unload()
self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
if parameters is not None:
prediction = self.pipeline(inputs, **parameters)
else:
prediction = self.pipeline(inputs)
return prediction |