# 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')

Training a language model requires massive, diverse text data. In 2021, common sources included:

Instead, I can to building a small-scale LLM from scratch (in the spirit of such a resource), covering the key concepts you'd likely find in a 2021-style tutorial. This will include:

Additionally, qualitative evaluation via prompt-based generation was essential. A builder would monitor: