Build A Large Language Model -from Scratch- Pdf -2021 -

# Initialize the model, optimizer, and loss function model = LargeLanguageModel(vocab_size, hidden_size, num_layers) optimizer = optim.Adam(model.parameters(), lr=1e-4) criterion = nn.CrossEntropyLoss()

def forward(self, input_ids): embeddings = self.embedding(input_ids) outputs = self.transformer(embeddings) outputs = self.fc(outputs) return outputs Build A Large Language Model -from Scratch- Pdf -2021

import torch import torch.nn as nn import torch.optim as optim # Initialize the model, optimizer, and loss function

# Train the model for epoch in range(10): model.train() total_loss = 0 for batch in range(batch_size): input_ids = torch.randint(0, vocab_size, (32, 512)) labels = torch.randint(0, vocab_size, (32, 512)) outputs = model(input_ids) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() total_loss += loss.item() print(f'Epoch {epoch+1}, Loss: {total_loss / batch_size:.4f}') This code snippet demonstrates a simple LLM with a transformer architecture. You can modify and extend this code to build more complex models. # Initialize the model