Training a model with billions of parameters exceeds the memory footprint of a single GPU. Distributed training frameworks split the model and workload across clusters. Data Parallelism (FSDP)
If you are looking for a definitive "paper" or guide to building a Large Language Model (LLM) from scratch, the most relevant resource is the technical documentation and book by Sebastian Raschka Build a Large Language Model (From Scratch) While it is a full book published by Manning Publications build a large language model %28from scratch%29 pdf
Garbage in, garbage out. The dataset must be diverse and clean. Training a model with billions of parameters exceeds
Building a Large Language Model (LLM) from scratch is the ultimate way to understand modern artificial intelligence. While using pre-trained models via APIs is sufficient for basic applications, creating a model from the ground up provides deep insight into architecture, data bottlenecks, and optimization mechanics. The dataset must be diverse and clean
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)