handler.py

from typing import Dict, List, Any
import torch
from transformers import pipeline, set_seed

class EndpointHandler:
    def __init__(self, path=""):
        self.pipeline = pipeline(
            "text-generation",
            model="openai-community/gpt2",
            device_map='auto',
            #trust_remote_code=True,
            model_kwargs={
                "load_in_4bit": True
            },
            # batch_size=1,
            )
        # model.generation_config

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
         data args:
              inputs (:obj: `str`)
              parameters (:obj: `Dict`)
        Return:
              A :obj:`list` | `dict`: will be serialized and returned
        """
        # get inputs
        inputs = data.pop("inputs", "")
        # get additional date field
        params = data.pop("parameters", ())
        if not params:
            params = dict()
        set_seed(42)
        # run normal prediction
        generation = self.pipeline(inputs, **params)
        # **generate_kwargs https://huggingface.co/docs/transformers/generation_strategies#customize-text-generation, 
        #                   https://huggingface.co/docs/transformers/generation_strategies#customize-text-generation
        return generation 

# Returns

# A list or a list of list of dict

# Returns one of the following dictionaries (cannot return a combination of both generated_text and generated_token_ids):

# generated_text (str, present when return_text=True) — The generated text.
# generated_token_ids (torch.Tensor or tf.Tensor, present when return_tensors=True) — The token ids of the generated text.