Building a large language model from scratch is a daunting task that requires significant expertise, computational resources, and a large corpus of text data. In recent years, the development of large language models has revolutionized the field of natural language processing (NLP), enabling applications such as language translation, text summarization, and chatbots.
The process of building a large language model from scratch involves several key steps: data collection, data preprocessing, model design, training, and evaluation.
Data Collection
The first step in building a large language model is to collect a large corpus of text data. This corpus should be diverse and representative of the language(s) the model will be trained on. The corpus can be sourced from various places, including books, articles, research papers, and websites. For example, the popular language model, BERT, was trained on a corpus of text that included the entirety of Wikipedia, as well as a large corpus of books and articles.
Data Preprocessing
Once the corpus of text data has been collected, it must be preprocessed to prepare it for training. This involves tokenizing the text into individual words or subwords, removing stop words and punctuation, and converting all text to lowercase. Additionally, the text data may need to be normalized to remove any inconsistencies in formatting or encoding.
Model Design
The next step is to design the architecture of the language model. This typically involves selecting a model architecture, such as a transformer or recurrent neural network (RNN), and configuring the model's hyperparameters, such as the number of layers, hidden size, and attention heads. The transformer architecture has become a popular choice for large language models due to its ability to handle long-range dependencies and parallelize computation.
Training
With the data preprocessed and the model designed, the next step is to train the model. This involves feeding the preprocessed text data into the model and adjusting the model's parameters to minimize a loss function, such as masked language modeling or next sentence prediction. Training a large language model requires significant computational resources, including specialized hardware such as graphics processing units (GPUs) or tensor processing units (TPUs).
Evaluation
Once the model has been trained, it must be evaluated to ensure it is performing well. This involves testing the model on a variety of tasks, such as language translation, text summarization, and question answering. The model's performance can be evaluated using metrics such as perplexity, accuracy, and F1 score.
Building a large language model from scratch requires a significant amount of expertise, computational resources, and data. However, the benefits of having a large language model are numerous, including improved performance on a variety of NLP tasks and the ability to fine-tune the model for specific applications.
For those interested in building a large language model from scratch, there are several resources available, including:
In conclusion, building a large language model from scratch is a complex task that requires significant expertise, computational resources, and data. However, the benefits of having a large language model are numerous, and with the right resources and knowledge, it is possible to build a state-of-the-art language model from scratch.
Here is a simple example of a transformer model in PyTorch: $$ class TransformerModel(nn.Module): def init(self, input_dim, hidden_dim, output_dim, n_heads, dropout): super(TransformerModel, self).init() self.encoder = nn.TransformerEncoderLayer(d_model=input_dim, nhead=n_heads, dim_feedforward=hidden_dim, dropout=dropout) self.decoder = nn.TransformerDecoderLayer(d_model=input_dim, nhead=n_heads, dim_feedforward=hidden_dim, dropout=dropout) self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, src, tgt):
encoded_src = self.encoder(src)
decoded_tgt = self.decoder(tgt, encoded_src)
output = self.fc(decoded_tgt)
return output
$$ This is a simplified example and in practice, you would need to add more functionality, such as padding, masking, and more. build a large language model %28from scratch%29 pdf
You can also use popular libraries like Hugging Face's Transformers to build and fine-tune pre-trained models: $$ from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "bert-base-uncased" model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) $$
The decoder architecture is responsible for generating output text based on the encoder's representation. The decoder typically consists of a stack of layers, each of which applies a transformation to the output embeddings.
Implementation snippet (simplified):
def train_bpe(text, vocab_size):
vocab = chr(i): i for i in range(256) # byte-level base
# ... merging loop ...
return merges, vocab
We will build a tokenizer that handles unknown tokens via bytes.
Transformers are permutation-invariant — without position, “cat sat” = “sat cat”.
Here is where 80% of hobbyist projects crash. You cannot feed raw text into a neural network. You need a tokenizer.
Your PDF will dedicate an entire chapter to tiktoken (the tokenizer used by OpenAI) or sentencepiece (used by Google).
The core code you will write (in Python/PyTorch):
import tiktoken enc = tiktoken.get_encoding("gpt2")
text = "Hello, I am building an LLM." tokens = enc.encode(text) # Output: [15496, 11, 314, 716, 1049, 1040, 13]
Why this matters: A naive "character-level" tokenizer (treating each letter as a token) would require a context window of 10,000 steps for a short paragraph. A sub-word tokenizer reduces that to ~200 steps.
The PDF will force you to build the training dataset loader:
You need to chunk your raw text (Project Gutenberg, FineWeb, or TinyStories) into fixed-context windows. If your context length is 256 tokens, you slide a window across your dataset. This prepares the input tensors (B, T) where B is batch size and T is sequence length.
I hope this helps! Let me know if you have any questions or need further clarification on any of the points mentioned.
Here is the PDF version of this blog post:
Would you like me to provide you with this pdf document ? Building a large language model from scratch is
Also here is python sample code
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
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)
def forward(self, x):
h0 = torch.zeros(1, x.size(0), self.hidden_dim).to(x.device)
out, _ = self.rnn(self.embedding(x), h0)
out = self.fc(out[:, -1, :])
return out
class LanguageModelDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return
'input': self.data[idx],
'label': self.labels[idx]
# Set hyperparameters
vocab_size = 10000
embedding_dim = 128
hidden_dim = 256
output_dim = 10000
batch_size = 32
# Initialize model, dataset, and data loader
model = LanguageModel(vocab_size, embedding_dim, hidden_dim, output_dim)
dataset = LanguageModelDataset(data, labels)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
# Train the model
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(10):
for batch in data_loader:
input = batch['input'].to(device)
label = batch['label'].to(device)
optimizer.zero_grad()
output = model(input)
loss = criterion(output, label)
loss.backward()
optimizer.step()
print(f'Epoch epoch+1, Loss: loss.item()')
Build a Large Language Model (From Scratch) Sebastian Raschka , published by
in October 2024, is a highly-rated practical guide that teaches readers how to construct a GPT-style model using without relying on high-level libraries. Amazon.com Key Highlights Step-by-Step Construction
: Guides you through every major stage: data preparation, coding attention mechanisms, pre-training on a general corpus, and fine-tuning for specific tasks like text classification. Practical & Accessible : Designed to run on a standard modern laptop
, making deep learning education accessible without high-end GPUs. No Black Boxes
: By building each component from the ground up—including tokenization and embeddings—it provides a deep understanding of the internal mechanics of generative AI. Final Output
: Readers evolve their base model into a text classifier and ultimately a functional that follows instructions. Amazon.com Detailed Review Summary Build a Large Language Model (From Scratch) - Goodreads
Build a Large Language Model (From Scratch): A Technical Guide
Building a Large Language Model (LLM) from the ground up is one of the most rewarding journeys in modern AI. This process involves moving beyond simply calling an API to understanding the core mechanics of generative AI. By constructing a model from scratch, you gain deep insights into tokenization, attention mechanisms, and the Transformer architecture that powers models like ChatGPT. 1. Setting the Foundation
Before writing code, you must establish your technical environment. While large-scale production models require massive GPU clusters, educational "from scratch" implementations can often be developed on a standard laptop using frameworks like PyTorch.
Language & Libraries: Most LLM development uses Python. Essential libraries include PyTorch or TensorFlow for neural network construction and NumPy for numerical operations.
Environment: Tools like Google Colab or Jupyter Notebooks are recommended for their interactive coding capabilities. 2. The Data Pipeline: From Raw Text to Vectors
The performance of an LLM is heavily dictated by its training data. The data pipeline transforms human language into a numeric format the model can process. Build a Large Language Model (From Scratch)
The book " Build a Large Language Model (From Scratch) " by Sebastian Raschka, published by Manning Publications, is a comprehensive, hands-on guide designed to demystify the inner workings of generative AI. It is specifically structured for readers with intermediate Python skills who want to understand the foundational systems of LLMs without relying on high-level pre-existing libraries. Key Learning Objectives
The text guides readers through a complete developmental lifecycle of a GPT-style model, covering these essential stages:
Architecture Implementation: Coding every part of an LLM, including attention mechanisms and transformer layers, from the ground up. The Transformer library by Hugging Face: a popular
Data Preparation: Creating and managing datasets suitable for pretraining.
Training & Fine-tuning: Implementing the pretraining process on a general corpus and fine-tuning the model for specific tasks like text classification.
Alignment: Utilizing human feedback and instruction fine-tuning to ensure the model follows conversational prompts. Book Structure and Content Focus Topic 1-2 Understanding LLM foundations and working with text data. 3-4
Implementing attention mechanisms and a GPT model to generate text. 5-7
Pretraining on unlabeled data and fine-tuning for specific tasks or instructions. App. A-E
PyTorch basics, parameter-efficient fine-tuning (LoRA), and advanced training loops. Format and Accessibility
PDF Options: A purchase of the print edition typically includes a free eBook version in PDF and ePub formats directly from Manning Publications.
Companion Resources: The author maintains an official GitHub repository containing code notebooks and a supplemental 170-page "Test Yourself" quiz PDF.
Hardware Requirements: The model developed in the book is optimized to run on a modern laptop, with optional GPU support for faster processing. Availability and Pricing
As of April 2026, the digital version is available for purchase at approximately $49.99 on platforms like the Kindle Store, Google Play, and Barnes & Noble.
rasbt/LLMs-from-scratch: Implement a ChatGPT-like ... - GitHub
After training for 2–24 hours (depending on your GPU), you unchain the beast. You remove the "training" flag and let the model run free. This is auto-regressive generation.
The algorithm:
torch.multinomial to add creativity).The "magic" code:
def generate(model, idx, max_new_tokens):
for _ in range(max_new_tokens):
logits = model(idx) # Get predictions
logits = logits[:, -1, :] # Focus on last timestep
probs = F.softmax(logits, dim=-1) # Convert to probabilities
idx_next = torch.multinomial(probs, num_samples=1) # Sample
idx = torch.cat((idx, idx_next), dim=1) # Append
return idx
If you built a 15-million-parameter model and trained it on the complete works of Jane Austen, the output might start as gibberish ("asdio fjkl qwep") but after 5,000 steps, it will produce real English words. After 50,000 steps, it will write in iambic pentameter.
softmax(QK^T / sqrt(d_k)) * V.input = token_emb + pos_emb.WinGLink is a multidisciplinary software program developed to process, interpret and integrate several geophysical disciplines in a unique interpretation model.
Primarily focused on the processing and modeling of Magnetotelluric data—for which it is the recognized worldwide standard—WinGLink also includes processing and modeling applications for gravity and magnetic data, as well as the capability to post information from vertical or deviated wells on maps and cross–sections to add model constraints.
