Guided Generation
A technique that constrains a language model's token outputs in real time during inference. Using finite-state machines or context-free grammars, it masks candidate tokens that violate structural constraints at the sampling level, ensuring that the generated output conforms to a specified schema.
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Apple Foundation Models constrains model output at the framework level via guided generation to necessarily conform to a given type structure, whereas ExecuTorch has no corresponding capability and can only write the schema into the prompt.
Guided generation is a technique that constrains model output tokens in real time during inference, typically implemented via finite state machines or context-free grammars, masking candidate tokens that do not satisfy structural constraints at the sampling layer.
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Apple Foundation Models constrains model output at the framework level via guided generation to necessarily conform to a given type structure, whereas ExecuTorch has no corresponding capability and can only write the schema into the prompt.
50%UnverifiedGuided generation is a technique that constrains model output tokens in real time during inference, typically implemented via finite state machines or context-free grammars, masking candidate tokens that do not satisfy structural constraints at the sampling layer.
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