The article then delves into the specifics of implementing this process in SQL, providing detailed SQL queries and explanations for each step. It also discusses the limitations of the model, such as the maximum number of tokens that can be used in a prompt being set at design time and cannot be changed by training. The article concludes by providing a link to a code repository for automating the population of database tables needed for the model.
Key takeaways:
- The article discusses the implementation of a large language model in SQL, using the GPT2 model as an example.
- It explains the concept of tokenization, which involves converting text into a list of numbers or tokens. This is achieved using a Byte Pair Encoding (BPE) algorithm, which reduces the token space size and the number of tokens in a word.
- The article also covers the concept of embeddings, which map narrower values into wider spaces. In the context of language models, embeddings are used to encode various properties of tokens into vectors. GPT2 uses 768 dimensions for its vectors.
- The author provides detailed SQL code snippets to demonstrate the implementation of these concepts. The code automates the process of populating database tables needed for the model, tokenizing the prompt, and embedding the tokens and positions.