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

# Evaluate the model def evaluate(model, device, loader, criterion): model.eval() total_loss = 0 with torch.no_grad(): for batch in loader: input_seq = batch['input'].to(device) output_seq = batch['output'].to(device) output = model(input_seq) loss = criterion(output, output_seq) total_loss += loss.item() return total_loss / len(loader)

A large language model is a type of neural network that is trained on vast amounts of text data to learn the patterns and structures of language. These models are typically transformer-based architectures that use self-attention mechanisms to weigh the importance of different input elements relative to each other. The goal of a language model is to predict the next word in a sequence of text, given the context of the previous words. build a large language model from scratch pdf

# Create dataset and data loader dataset = LanguageModelDataset(text_data, vocab) loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 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() import torch import torch

def __getitem__(self, idx): text = self.text_data[idx] input_seq = [] output_seq = [] for i in range(len(text) - 1): input_seq.append(self.vocab[text[i]]) output_seq.append(self.vocab[text[i + 1]]) return { 'input': torch.tensor(input_seq), 'output': torch.tensor(output_seq) }

def __len__(self): return len(self.text_data) # Create dataset and data loader dataset =

# Define a dataset class for our language model class LanguageModelDataset(Dataset): def __init__(self, text_data, vocab): self.text_data = text_data self.vocab = vocab