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Stable version 1.0.0 (Compatible with OutSystems 11)
Uploaded on 20 Jun by 
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The GPT family of models process text using tokens, which are common sequences of characters found in text. The models understand the statistical relationships between these tokens, and excel at producing the next token in a sequence of tokens.
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GPT and embeddings models process text in chunks called tokens. Tokens represent commonly occurring sequences of characters. For example, the string " tokenization" is decomposed as " token" and "ization", while a short and common word like " the" is represented as a single token. Note that in a sentence, the first token of each word typically starts with a space character. Check out our tokenizer tool to test specific strings and see how they are translated into tokens. As a rough rule of thumb, 1 token is approximately 4 characters or 0.75 words for English text.

One limitation to keep in mind is that for a GPT model the prompt and the generated output combined must be no more than the model's maximum context length. For embeddings models (which do not output tokens), the input must be shorter than the model's maximum context length. The maximum context lengths for each GPT and embeddings model can be found in the model index.

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