Spaces:
Runtime error
Runtime error
File size: 1,988 Bytes
841ff1d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
# Necessary imports
import os
import sys
from dotenv import load_dotenv
from typing import Any
import torch
from transformers import AutoModel, AutoTokenizer, AutoProcessor
# Local imports
from src.logger import logging
from src.exception import CustomExceptionHandling
# Load the Environment Variables from .env file
load_dotenv()
# Access token for using the model
access_token = os.environ.get("ACCESS_TOKEN")
def load_model_tokenizer_and_processor(model_name: str, device: str) -> Any:
"""
Load the model, tokenizer and processor.
Args:
- model_name (str): The name of the model to load.
- device (str): The device to load the model onto.
Returns:
- model: The loaded model.
- tokenizer: The loaded tokenizer.
- processor: The loaded processor.
"""
# try:
# Load the model, tokenizer and processor
model = AutoModel.from_pretrained(
model_name,
# device_map = 'auto',
trust_remote_code=True,
attn_implementation="sdpa",
torch_dtype=torch.bfloat16,
# init_vision=True,
# init_audio=False,
# init_tts=False,
# token=access_token
).to('cuda')
# model = model
tokenizer = AutoTokenizer.from_pretrained(
model_name, trust_remote_code=True,
# token=access_token
)
processor = AutoProcessor.from_pretrained(
model_name, trust_remote_code=True,
# token=access_token
)
# Log the successful loading of the model, tokenizer and processor
logging.info("Model, tokenizer and processor loaded successfully.")
# Return the model, tokenizer and processor
return model, tokenizer, processor
# Handle exceptions that may occur during model, tokenizer and processor loading
# except Exception as e:
# Custom exception handling
# raise CustomExceptionHandling(e, sys) from e
|