正确的代码是怎么样的

以下是一个简单的ChitGPT模型代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

class ChitGPT(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers):
        super(ChitGPT, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, vocab_size)

    def forward(self, x, prev_state):
        embed = self.embedding(x)
        output, state = self.lstm(embed, prev_state)
        logits = self.fc(output)
        return logits, state

    def init_state(self, batch_size):
        return (torch.zeros(self.num_layers, batch_size, self.hidden_dim), 
                torch.zeros(self.num_layers, batch_size, self.hidden_dim))

# Define hyperparameters
vocab_size = 10000
embedding_dim = 256
hidden_dim = 512
num_layers = 2
batch_size = 32
seq_len = 20

# Instantiate model
model = ChitGPT(vocab_size, embedding_dim, hidden_dim, num_layers)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Generate input and target sequences
inputs = torch.randint(0, vocab_size, (batch_size, seq_len))
targets = torch.randint(0, vocab_size, (batch_size, seq_len))

# Train model
for epoch in range(100):
    epoch_loss = 0
    state = model.init_state(batch_size)
    for i in range(seq_len):
        optimizer.zero_grad()
        logits, state = model(inputs[:, i], state)
        loss = criterion(logits.view(-1, vocab_size), targets[:, i])
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
    print(f'Epoch {epoch+1} loss: {epoch_loss/seq_len}')

请注意，这只是一个简单的示例，用于说明ChitGPT模型的基本结构和训练过程。实际的模型可能会更复杂，并且需要更长时间的训练才能够产生有意义的结果。