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To guarantee valid JSON generation from local models, you provide a Pydantic model or JSON schema alongside your prompt, and the tool converts it into a deterministic grammar to enforce strict schema adherence.

Yes, you can use vLLM or transformers for offline structured generation. The tool supports these local-model backends to provide high-throughput inference in offline production workflows.

Schema-constrained output works for data extraction by converting your schemas into finite-state machines. This mechanism forces the model to generate machine-checkable outputs that strictly conform to your target structure.

The best way to enforce type-safe results with local LLMs is by integrating Pydantic models. This approach automatically translates your defined schemas into grammar constraints, ensuring type safety without relying on external API dependencies.

Yes, structured generation with grammar constraints supports XML and code outputs. It transforms schemas into deterministic grammars to ensure the validity of JSON, XML, and code generation.

You need finite-state machines for reproducible LLM inference because they enforce strict grammar constraints during decoding. This guarantees that every generated output conforms exactly to the defined schema, yielding consistent results.