Building a Foundation LLM from Scratch

Introduction

Welcome to my blog series on building a foundation language model from the ground up. In this series, I'll document every step of the process, from understanding the mathematics behind transformers to implementing and training a working model.

Why Build an LLM from Scratch?

There's no better way to understand how these models work than to build one yourself. While libraries like Hugging Face make it easy to use pre-trained models, the underlying mechanics can remain a black box.

What We'll Cover

Tokenization - How text becomes numbers
Embeddings - Representing meaning in vector space
Attention Mechanisms - The heart of the transformer
Training Dynamics - Loss functions, optimizers, and scaling laws
Inference - Generating text from our trained model

The Architecture

PyTorch: Basic Transformer Block▼

import torch
import torch.nn as nn

class TransformerBlock(nn.Module):
    def init(self, d_model, n_heads, d_ff, dropout=0.1):
        super().init()
        self.attention = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.ff = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(d_ff, d_model),
            nn.Dropout(dropout)
        )
def forward(self, x, mask=None):
    # Self-attention with residual connection
    attn_out, _ = self.attention(x, x, x, attn_mask=mask)
    x = self.norm1(x + attn_out)
    
    # Feed-forward with residual connection
    ff_out = self.ff(x)
    x = self.norm2(x + ff_out)
    
    return x</code></pre>



Next Steps
In the next post, we'll dive deep into tokenization strategies and implement a simple BPE tokenizer.
Stay tuned!