Delving Into Tokenization: Andrej Karpathy’s Latest Tutorial and a Closer Look at Google’s Gemma

In the ever-evolving realm of large language models (LLMs), understanding the underpinnings of model architecture and functionality is crucial for advancements in artificial intelligence. Andrej Karpathy, a former researcher at OpenAI, has recently introduced an insightful tutorial focused on the intricacies of LLM tokenization, a foundational process that plays a pivotal role in the training and functioning of models like GPT.

Understanding Tokenization from the Ground Up

At the heart of Karpathy’s latest educational venture is the tokenizer used in OpenAI’s GPT series. “In this lecture, we build from scratch the Tokenizer used in the GPT series from OpenAI,” Karpathy explains, highlighting the autonomous nature of tokenizers within the LLM pipeline. These essential components involve their own dedicated training set, algorithm—specifically, Byte Pair Encoding (BPE)—and are responsible for crucial functions such as encoding strings to tokens and decoding tokens back to strings.

Tokenization might seem like a straightforward process, but Karpathy sheds light on its complexity and its implications on LLM behavior. “We will see that a lot of weird behaviors and problems of LLMs actually trace back to tokenization,” he states, underscoring the importance of understanding, if not rethinking, this stage.

To support this educational initiative, Karpathy has also released ‘minbpe’ on GitHub, a repository featuring minimal and clean code for Byte Pair Encoding, showcasing the algorithm’s implementation in a more accessible manner. The repository is available at:

Karpathy’s Departure and His Analysis of Google’s Gemma

While Karpathy’s decision to leave OpenAI might have surprised some, his commitment to advancing AI through personal projects remains unwavering. In his post-departure endeavors, he turns his attention to Google’s new open-source model, Gemma. Adopting a hands-on approach, Karpathy decodes Gemma’s tokenizer and compares it with the Llama 2 tokenizer, unveiling critical insights into its structure and functionality.

One of the standout findings from his analysis is Gemma’s substantial increase in vocabulary size, scaling up to 256K tokens from Llama 2’s 32K. This expansion is paired with a crucial setting adjustment—setting “add_dummy_prefix” to False, which aligns Gemma more closely with GPT practices and minimizes preprocessing nuances.

Gemma’s tokenizer further differentiates itself through its model prefix, which documents the training dataset’s path, indicating a robust training corpus of approximately 51GB. Additionally, the tokenizer incorporates a plethora of user-defined symbols, ranging from newline sequences to HTML elements, signifying a more complex tokenization process.

Through his comprehensive exploration, Karpathy reveals that while Gemma’s tokenizer shares fundamental similarities with that of Llama 2, it notably distinguishes itself through its expanded vocabulary, the inclusion of additional special tokens, and its departure in handling the “add_dummy_prefix” setting. This analysis not only underscores the nuances of Gemma’s tokenization methodology but also contributes to a broader understanding of tokenization’s impact on LLM development and functionality.


Andrej Karpathy’s dive into the world of tokenization provides a crucial look at the mechanisms driving today’s most advanced LLMs. By deconstructing the tokenizer’s role, unveiling his BPE algorithm implementation, and offering a detailed comparison of Google’s Gemma tokenizer, Karpathy offers invaluable insights into the nuanced processes that shape the behavior and capabilities of large language models. As the AI community continues to push the boundaries of what’s possible, understanding these foundational components becomes all the more critical.

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