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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 |