Token Limit, every LLM Developer should Know !
Tokens in Large Language Models (LLMs) like GPT-3 and PaLM2 are units of data read in one step, with token limits affecting model performance. Understanding these limits is key to optimizing your use
When it comes to Large Language Models (LLMs) like GPT-3 or PaLM2, there's an important concept we often encounter: ๐ง๐ผ๐ธ๐ฒ๐ป๐. One of the challenges in working with language models is the limitation imposed by the maximum number of tokens they can handle.
๐ช๐ต๐ฎ๐ ๐ฎ๐ฟ๐ฒ ๐ง๐ผ๐ธ๐ฒ๐ป๐ ?
A token, in the context of LLMs, is a unit of data the model reads in one step. Depending on the model's tokenization method, a token can be a single character, a word, or even a subword. Here are some helpful rules of thumb for understanding tokens in terms of lengths:
ย โขย 1 token ~= 4 chars in English
ย โขย 1 token ~= ยพ words
ย โขย 100 tokens ~= 75 words
๐ช๐ต๐ฎ๐ ๐ฎ๐ฟ๐ฒ ๐ง๐ผ๐ธ๐ฒ๐ป ๐๐ถ๐บ๐ถ๐๐ ?
The number of tokens a model can handle at once, known as the 'token limit', is a crucial metric. This limit impacts both the length of text the model can consider and the amount of context it can use when generating responses. For instance, ๐๐ฃ๐ง-๐ฏ has a token limit of ๐ฎ๐, while ๐๐ป๐๐ต๐ฟ๐ผ๐ฝ๐ถ๐ฐ'๐ ๐๐น๐ฎ๐๐ฑ๐ฒ tops out at ๐ญ๐ฌ๐ฌ๐.
Check out the bar chart below for a comparison of token limits across popular LLMs.
๐ช๐ต๐ ๐ฑ๐ผ๐ฒ๐ ๐๐ต๐ถ๐ ๐บ๐ฎ๐๐๐ฒ๐ฟ ๐๐ผ ๐๐ผ๐?
Understanding token limits can help you optimize your usage of these models. For example, when you're using a model to generate text, if your input prompt is too long, the model might not have enough tokens left to provide a meaningful response. Conversely, a very short prompt might not give the model enough context to generate a useful reply.
As AI continues to evolve, so too will our understanding of these metrics and their implications. So, keep the concept of tokens in mind when you're working with LLMs - it's more important than you might think!