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TOON addresses a number of significant difficulties inherent in JSON when utilised with LLMs. JSON's verbosity and redundant syntax, characterized by recurring keys and extensive punctuation, bring on inefficient tokenization and inflated token counts. For instance, within an variety of objects, keys like "id" and "name" are recurring for every entry, consuming valuable context window House.
Indeed, LLMs can have an understanding of TOON format just like regular JSON. The structure maintains the exact same composition and data, just in a far more compact illustration. It is possible to contain a key mapping at the start if using shorter keys.
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Cuando pides a un LLM que extraiga información y la devuelva en formato estructurado, TOON es excellent porque su sintaxis explícita ayuda al modelo a generar salidas correctamente formateadas desde el primer intento.
The CLI lets you swap delimiters over the fly. If you're handling tabular facts, switching to the tab delimiter could make the output search cleaner and procedure far more successfully.
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.
The @toon-format/cli is more than simply a file converter; This is a utility belt for your LLM period. By transferring knowledge processing for JSON TO TOON the command line, you are able to iterate faster, visualize optimizations straight away, and combine token-effective formats into your present engineering workflows.
Regardless if you are debugging a prompt, getting ready a dataset, or just interested in the amount of revenue you may save on tokens, the @toon-format/toon CLI is your new best friend.
Duplicate or Download: The resulting TOON output can then be effortlessly copied for quick use in applications or for more processing.
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