Training a character-level language model equipped with a Gated Recurrent Unit (GRU) follows the exact same procedure as training one with a simple Recurrent Neural Network (RNN). A GRU architecture instance is instantiated and provided as the core recurrent module to a generic language model wrapper. The combined model is then trained on a sequence dataset over multiple epochs, applying a specified gradient clipping value to prevent gradients from exploding and to stabilize the parameter updates.

Claude

To train a character-level language model from scratch, an `RNNLMScratch` instance is initialized with a recurrent module (such as `RNNScratch`) and the dataset's vocabulary size. The model is then trained on a sequential dataset (like The Time Machine corpus) using a training utility class. During this execution phase, the trainer must be configured with a gradient clipping value (e.g., `gradient_clip_val=1`) to ensure gradients are clipped before parameter updates.

```python
data = d2l.TimeMachine(batch_size=1024, num_steps=32)
rnn = RNNScratch(num_inputs=len(data.vocab), num_hiddens=32)
model = RNNLMScratch(rnn, vocab_size=len(data.vocab), lr=1)
trainer = d2l.Trainer(max_epochs=100, gradient_clip_val=1, num_gpus=1)
trainer.fit(model, data)
```

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