# Train the model for epoch in range(10): optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() print(f'Epoch epoch+1, Loss: loss.item()')
between embedding and output layer. Rotary positional embeddings (though post‑2021). Checkpointing to trade compute for memory. Build A Large Language Model -from Scratch- Pdf -2021