handler.py

from typing import Dict, Any
import logging

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch.cuda

device = "cuda" if torch.cuda.is_available() else "cpu"
LOGGER = logging.getLogger(__name__)
class EndpointHandler():
    def __init__(self, path=""):
        self.model = AutoModelForCausalLM.from_pretrained("Ozgur98/pushed_model_mosaic_small", trust_remote_code=True).to(device='cuda:0', dtype=torch.bfloat16)
        self.tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
        # Load the Lora model
        
    def __call__(self, data):
        """
        Args:
            data (Dict): The payload with the text prompt and generation parameters.
        """
        LOGGER.info(data)
        # Forward
        LOGGER.info(f"Start generation.")
        tokenized_example = self.tokenizer(data, return_tensors='pt')
        outputs = self.model.generate(tokenized_example['input_ids'].to('cuda:0'), max_new_tokens=100, do_sample=True, top_k=10, top_p = 0.95)
        
        # Postprocess
        answer = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
        prompt = answer[0].rstrip()
        return prompt