Large Language Models (LLMs) like GPT-4, Llama, and Claude have revolutionized natural language processing. While many practitioners use these models via APIs, few understand their inner workings from first principles. This PDF guide takes you from zero to a working LLM—covering tokenization, transformer architecture, pretraining, finetuning, and efficient deployment. No black boxes, no proprietary libraries: only Python, PyTorch, and fundamental mathematics.
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, num_layers=1, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) build a large language model %28from scratch%29 pdf
After training, generate text: