Introduction to Large Language Models (LLMs): What I Learned

In my exploration of Large Language Models (LLMs), I discovered how these neural networks are trained on massive text datasets to learn language patterns and predict the next token. The core idea is simple: based on the context before it, the model estimates the probability of various potential next tokens and selects one, then repeats the process to generate a longer response.

LLMs have evolved from earlier language models like N-gram models, which struggled with unseen combinations and limited context. Advances in recurrent neural networks (RNNs) and LSTMs improved the ability to retain information, but processing long sequences remained challenging. It was with the introduction of the Transformer architecture that LLMs truly flourished.

Unlike traditional models, Transformers use attention mechanisms to process relationships between tokens effectively, enabling the training of much larger language models.

Text must first be converted into numerical representations for LLMs to process it. This process, called tokenization, involves breaking text down into smaller units. Subword tokenization is commonly used, as it balances vocabulary size and the ability to represent unfamiliar words. Each token is then assigned a unique numerical ID, which serves as the input for the model to process.

The model's internal workings involve embeddings, which are numerical vectors representing tokens. These embeddings help the model learn relationships between words and their surrounding context. The context window is another crucial concept, determining how many tokens the model can process together. If the input exceeds the model's context limit, we may need to truncate or divide the text into chunks.

Decoder-only Transformers are often used in chat-oriented LLMs. These models convert text into token IDs, embeddings, and incorporate positional information. They then process the representations using masked self-attention and feed-forward layers. The model produces scores for possible next tokens, which are converted into probabilities using softmax. The model then generates the next token based on these probabilities, following a causal mask that prevents it from seeing future tokens during training.

LLMs learn from large text datasets through pretraining, primarily using autoregressive language modeling and masked language modeling. Autoregressive language modeling involves predicting the next token based on preceding context, while masked language modeling hides some tokens and requires the model to predict them from the surrounding text. Different model families, such as BERT, GPT, and T5, have distinct architectures and training objectives, making the choice of model dependent on the specific task at hand.

Through this journey of learning about LLMs, I gained a deeper understanding of their inner workings, from how they generate text to how we can run open-weight models using Python and Hugging Face.