Build A Large — Language Model -from Scratch- Pdf -2021

This guide is widely considered the gold standard for learning how LLMs work by actually coding one from the ground up. It covers:

def __getitem__(self, idx): x = self.tokens[idx:idx+self.seq_len] y = self.tokens[idx+1:idx+self.seq_len+1] return torch.tensor(x), torch.tensor(y)

Large language models have revolutionized the field of natural language processing (NLP) in recent years. These models have achieved state-of-the-art results in various NLP tasks, including language translation, text summarization, and text generation. However, most existing large language models are built using pre-trained models and fine-tuned on specific tasks. In this paper, we propose a comprehensive approach to building a large language model from scratch. We describe the architecture, training objectives, and training procedures for building a large language model with a focus on performance, efficiency, and scalability. Our proposed model, dubbed "LLaMA," is trained on a large corpus of text data and achieves competitive results on various NLP tasks. Build A Large Language Model -from Scratch- Pdf -2021

class LargeLanguageModel(nn.Module): def __init__(self, vocab_size, hidden_size, num_layers): super(LargeLanguageModel, self).__init__() self.embedding = nn.Embedding(vocab_size, hidden_size) self.transformer = nn.Transformer(num_layers, hidden_size) self.fc = nn.Linear(hidden_size, vocab_size)

def generate(model, prompt, tokenizer, max_tokens=100, temperature=1.0): model.eval() tokens = tokenizer.encode(prompt) for _ in range(max_tokens): logits = model(torch.tensor([tokens])) next_logits = logits[0, -1, :] / temperature probs = torch.softmax(next_logits, dim=-1) next_token = torch.multinomial(probs, num_samples=1) tokens.append(next_token.item()) if next_token == tokenizer.eos_token_id: break return tokenizer.decode(tokens) This guide is widely considered the gold standard

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Training a language model requires massive, diverse text data. In 2021, common sources included: However, most existing large language models are built

class CausalSelfAttention(nn.Module): def __init__(self, config): super().__init__() self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd) # Mask initialization self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)) .view(1, 1, config.block_size, config.block_size)) def forward(self, x): # ... Q, K, V projection, attention score, apply mask, softmax