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To guarantee valid JSON schema outputs with local models, you can enforce Pydantic models and JSON schemas during generation. This constrains token generation using grammar-based rules to ensure type-safe, valid structured outputs.

Yes, structured generation works with llama.cpp and vLLM backends. It supports local-first backends like Transformers, llama.cpp, and vLLM to provide offline, high-throughput inference while enforcing type-safe outputs.

Grammar-based generation prevents invalid model outputs by using Context-Free Grammars (CFG) and Finite State Machines (FSM) to constrain token generation. This mechanism enforces schemas dynamically, guaranteeing valid JSON, XML, or code outputs.

To extract structured data from unstructured text using Pydantic models, you provide a model and a Pydantic schema to generate typed outputs. This process builds structured data extractors that return deterministic, type-safe results.

You can generate valid XML and code formats in addition to JSON schemas. The grammar-based constraints enforce various structured output formats, ensuring type safety and validity across enterprise workflows beyond just JSON.

The best way to process forms with guaranteed type safety using local models is to enforce Pydantic models during generation. This delivers zero-overhead, structured generation by constraining tokens to conform strictly to your defined schemas.