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from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
from peft import PeftModel
import json
import os
class EndpointHandler():
def __init__(self, path=""):
base_model_path = json.load(open(os.path.join(path, "training_params.json")))["model"]
model = AutoModelForCausalLM.from_pretrained(
base_model_path,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
trust_remote_code=True,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model.resize_token_embeddings(len(tokenizer))
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 |