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LLM Welcoming: The structure is created to be effortlessly parsed by AI styles though reducing the token count, saving expenditures and context window space.
If you need to convert this back again to total JSON later on, You should utilize the --increase-paths Harmless flag all through decoding to reconstruct the deep object structure correctly.
It simplifies info serialization for AI workflows, making it a crucial asset in the evolving landscape of huge language styles.
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In the event you feed it a .json file, it assumes you want to encode it to TOON. For those who offer a .toon file, it switches to decoding method to provide you with again JSON.
A diferencia de JSON, que fue creado para intercambio de datos entre aplicaciones World-wide-web, TOON está optimizado para reducir el consumo de tokens, lo que se traduce en ahorros significativos tanto en costes de API como en capacidad de contexto.
Sector E-commerce: Una plataforma de comercio electrónico redujo sus costes de API en un forty five% al convertir sus catálogos de productos a TOON antes de TOON procesarlos con GPT-four para generar descripciones optimizadas.
Whether you are debugging a prompt, planning a dataset, or just curious about the amount of money you may preserve on tokens, the @toon-structure/toon CLI is your TOON formater new ally.
Copy or Obtain: The resulting TOON output can then be quickly copied for rapid use in purposes or for more processing.
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The TOON CLI is created to be sensible about what you are trying to try and do. You rarely must explicitly notify it to encode or decode; it looks at your file extensions to make a decision.
TOON format takes advantage of short important abbreviations, gets rid of unnecessary whitespace, and optimizes the JSON construction to reduce token count although preserving all info. This could decrease token use by 30-60% when compared to straightforward JSON.