I Built a Small LLM From Scratch — Here’s What I Finally Understood About Transformers
How a Tiny LLM Works: Building a Language Model From Scratch in PyTorch Have you ever wondered what actually happens inside an LLM when you type: "Hello, how are" and it predicts: "you" We often hear words like Transformer, Attention, Tokens, RoPE, GQA, RMSNorm, and SwiGLU . But what do these things actually do? I recently worked through the implementation of a small language model from scratch,…
Title: Building a Language Model from Scratch to Understand Transformers
I recently worked through the implementation of a small language model from scratch in PyTorch, which helped me gain a deeper understanding of the key components and processes involved in language models. Here's what I learned:
1. A language model learns to predict the next token in a sequence of text. For example, given the input "The cat", the model predicts the next token as "sat". This central idea is used in autoregressive language models.
2. Before a neural network can learn anything, text must be converted into numerical representations. This process, called tokenization, converts characters into IDs. For instance, the character "Hello" can be converted into the ID sequence [20, 43, 50, 50, 53].
3. To teach the model what to predict, training examples are created by shifting the sequence by one position. For example, "To be or not to be" becomes a series of input-target pairs: ("To be", "o"), ("To be", "o"), etc. This process is called next-token prediction.
4. The ID sequence, such as [20, 43, 50, 50, 53], is converted into token embeddings, which are vector representations of each token. In our model, each token becomes a 256-dimensional vector, allowing the neural network to work with useful numerical representations.
5. Transformers are essential for understanding relationships between tokens in a sequence. The most important component of a Transformer is attention. Attention allows the model to focus on different parts of the input sequence when processing a specific token.
6. Attention uses three representations: Queries (Q), Keys (K), and Values (V). Queries represent what information the model is looking for, Keys represent what information the model contains, and Values represent the information that should be provided to the model. The attention mechanism calculates how strongly a Query should attend to different Keys and produces attention outputs.
7. Causal attention is used to ensure that the model doesn't look into the future when generating the next token. A causal mask is applied to prevent the model from attending to future tokens during language generation. This is achieved by creating a triangular mask that allows each token to attend to itself and all previous tokens but not future tokens.
8. Positional information is crucial for the model to understand the order of tokens in a sequence. Since the model doesn't have inherent knowledge of word order, positional encodings are added to the token embeddings, allowing the model to learn the relationships between tokens based on their positions in the sequence.
By breaking down the flow of an LLM step by step, from text to tokens, embeddings, attention, feed-forward networks, and predictions, I gained a better understanding of the fundamental concepts and processes involved in language models and Transformers.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
