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Structured JSON generation from local models is enforced by applying finite-state machines for in-flight token filtering and Pydantic validation. This ensures outputs strictly match your defined JSON schema with zero overhead.

Zero-overhead structured generation works by using finite-state machines to filter tokens in-flight during generation. This guarantees local model outputs conform strictly to JSON schemas without adding processing latency.

Yes, Pydantic validation works with llama.cpp and vLLM backends. The system translates Pydantic models into JSON schemas automatically, ensuring safe and deterministic structured results across supported local environments.

You can generate validated XML or code from unstructured text by loading a local model and applying schema constraints. The system filters tokens in-flight to ensure outputs match your specified formats safely.

No, structured generation with finite-state machines does not slow down inference. It applies in-flight token filtering that guarantees valid JSON, XML, or code outputs with zero overhead relative to unconstrained generation.

JSON schemas are required for local model structured generation to define the exact output structure. They enable the finite-state machine to filter invalid tokens during generation, ensuring deterministic and safe results.