# Main function def main(): # Set hyperparameters vocab_size = 10000 embedding_dim = 128 hidden_dim = 256 output_dim = vocab_size batch_size = 32 epochs = 10
# Define a dataset class for our language model class LanguageModelDataset(Dataset): def __init__(self, text_data, vocab): self.text_data = text_data self.vocab = vocab build a large language model from scratch pdf
# Set device device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Main function def main(): # Set hyperparameters
def forward(self, x): embedded = self.embedding(x) output, _ = self.rnn(embedded) output = self.fc(output[:, -1, :]) return output x): embedded = self.embedding(x) output
def __len__(self): return len(self.text_data)
# Train the model def train(model, device, loader, optimizer, criterion): model.train() total_loss = 0 for batch in loader: input_seq = batch['input'].to(device) output_seq = batch['output'].to(device) optimizer.zero_grad() output = model(input_seq) loss = criterion(output, output_seq) loss.backward() optimizer.step() total_loss += loss.item() return total_loss / len(loader)
# Load data text_data = [...] vocab = {...}