Notes

Certainly! Here's the modified code with the changes applied as per the previously provided `Trainer` class:

```python
import numpy as np

def create_training_sequences(max_sequence_length, tokenized_training_data):
sequences = []
for i in range(0, len(tokenized_training_data) - max_sequence_length - 1):
sequence_slice = tokenized_training_data[i:i+max_sequence_length + 1]
sequence_tensor = torch.tensor(sequence_slice, dtype=torch.long, device=device)
sequences.append(sequence_tensor)
return sequences

def tokenize_and_pad_training_data(max_sequence_length, tokenizer, training_data):
tokenized_training_data = tokenizer.tokenize(training_data)
for _ in range(max_sequence_length):
tokenized_training_data.insert(0, tokenizer.character_to_token('<pad>'))
return tokenized_training_data

device = get_device() # Assuming you have the get_device() function to get the proper device

tokenizer = Tokenizer()

embedding_dimension = 256
max_sequence_length = 20
number_of_tokens = tokenizer.size()

# Create the model
model = AutoregressiveWrapper(LanguageModel(
embedding_dimension=embedding_dimension,
number_of_tokens=number_of_tokens,
number_of_heads=4,
number_of_layers=3,
dropout_rate=0.1,
max_sequence_length=max_sequence_length
)).to(device)

tokenized_and_padded_training_data = tokenize_and_pad_training_data(max_sequence_length, tokenizer, final_data)
sequences = create_training_sequences(max_sequence_length, tokenized_and_padded_training_data)

# Train the model
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
trainer = Trainer(model, tokenizer, optimizer)
trainer.train(sequences, epochs=100, batch_size=8)
```

Make sure to adjust import statements and other parts of your code as needed.