Build A Large Language Model From Scratch Pdf Site

def forward(self, x): embedded = self.embedding(x) output, _ = self.rnn(embedded) output = self.fc(output[:, -1, :]) return output

# Create model, optimizer, and criterion model = LanguageModel(vocab_size, embedding_dim, hidden_dim, output_dim).to(device) optimizer = optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() build a large language model from scratch pdf

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import Dataset, DataLoader def forward(self, x): embedded = self

# Set device device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') x): embedded = self.embedding(x) output

# Train the model def train(model, device, loader, optimizer, criterion): model.train() total_loss = 0 for batch in loader: input_seq = batch['input'].to(device) output_seq = batch['output'].to(device) optimizer.zero_grad() output = model(input_seq) loss = criterion(output, output_seq) loss.backward() optimizer.step() total_loss += loss.item() return total_loss / len(loader)

# Load data text_data = [...] vocab = {...}

# Define a simple language model class LanguageModel(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim): super(LanguageModel, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim)