Here is a simple example of how you could structure the python code for building a simple language model:
def forward(self, x): embedded = self.embedding(x) output, _ = self.rnn(embedded) output = self.fc(output[:, -1, :]) return output build a large language model from scratch pdf
Most tutorials rely on Hugging Face's transformers library. While efficient, downloading a pre-trained model with model = AutoModel.from_pretrained("gpt2") teaches you nothing about backpropagation, attention mechanisms, or memory optimization. Here is a simple example of how you
class SelfAttention(nn.Module): def __init__(self, embed_size, heads): super(SelfAttention, self).__init__() self.embed_size = embed_size self.heads = heads self.head_dim = embed_size // heads x): embedded = self.embedding(x) output