# Define your own parallel model

Author: Zhengda Bian, Yongbin Li

⚠️ We are working on this documentation to make it more detailed. We will introduce the mechanism of different parallelism and how to use them to write a model.

Let's say that you have a huge MLP model with billions of parameters and its extremely large hidden layer size makes it impossible to fit into a single GPU directly. Don't worry, Colossal-AI is here to help you sort things out. With the help of Colossal-AI, you can write your model in the familiar way in which you used to write models for a single GPU, while Colossal-AI automatically splits your model weights and fit them perfectly into a set of GPUs. We give a simple example showing how to write a simple 2D parallel model in the Colossal-AI context.

## Write a simple 2D parallel model​

from colossalai.nn import Linear2Dimport torch.nn as nnclass MLP_2D(nn.Module):    def __init__(self):        super().__init__()        self.linear_1 = Linear2D(in_features=1024, out_features=16384)        self.linear_2 = Linear2D(in_features=16384, out_features=1024)    def forward(self, x):        x = self.linear_1(x)        x = self.linear_2(x)        return x

## Use pre-defined model​

For the sake of your convenience, we kindly provide you in our Model Zoo with some prevalent models such as BERT, ViT, MoE, and GPT. Feel free to customize them into different sizes to fit into your special needs.