How to load a huggingface pretrained transformer model directly to GPU?

Huggingface is a prominent open-source platform for machine learning and natural language processing developers and researchers. It provides resources like models, datasets, etc. for application and research. The transformer library in Huggingface is powerful for natural language processing tasks. It enables users to import and use pretrained transformer models easily. 

When we call the transformer using this " model = AutoModelForCausalLM.from_pretrained("bert-base-uncased")" method, It will automatically load the model into the CPU. We need to call 'Cuda' for loading the model into the GPU.

Solution:

Huggingface acceleration could help move the model to GPU before it's fully loaded in the CPU, so it worked when
GPU memory > model size > CPU memory
by usingdevice_map = 'cuda'

!pip install accelerate

then use

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bert-base-uncased", device_map = 'cuda')

You can easily load the huggingface pretrained transfer model directly into the GPU by following these steps. It is helpful for faster and more efficient processing of NLP tasks. Hugging Face's Transformers library allows you to use advanced models easily. It gets significantly more efficient when used in integrated GPU acceleration. These advanced models can be used for various types of applications.

Thank you for reading the article.