import torch from typing import Dict, List, Any from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline # testing changes # get dtype dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16 class EndpointHandler: def __init__(self, path=""): # load the model tokenizer = AutoTokenizer.from_pretrained(path) model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", torch_dtype=dtype, trust_remote_code=True) model.to('cuda:0') # create inference pipeline self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device='cuda:0') def __call__(self, data: Any) -> List[List[Dict[str, float]]]: inputs = data.pop("inputs", data) parameters = data.pop("parameters", None) # pass inputs with all kwargs in data if parameters is not None: prediction = self.pipeline(inputs, max_new_tokens=2048, **parameters) else: prediction = self.pipeline(inputs) # postprocess the prediction return prediction