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To ensure valid JSON from local inference, structured generation applies grammar-based FSM and CFG token filtering to constrain model outputs directly to your Pydantic models or JSON Schema. This guarantees the generated text always matches the required structure and field types.

Yes, constrained structured generation supports multi-backend local inference across Transformers, llama.cpp with GGUF models, and vLLM. It enforces schema compliance natively within these inference backends to maintain production throughput.

LLM outputs are malformed during information extraction because standard generation lacks structural constraints. Applying FSM constrained decoding filters tokens during generation, preventing invalid structures from being produced and guaranteeing valid structured data.

Yes, you can use Pydantic models to enforce field types, nested models, enums, and constraints during LLM generation. The constrained decoding process filters tokens based on your Pydantic schema, ensuring the output strictly adheres to your defined types.

The best way to generate guaranteed-valid structured outputs for form filling is applying grammar-based constrained generation. By using FSM and CFG token filtering with a defined schema or regex, the model is mathematically prevented from producing invalid structures.

FSM constrained decoding for local inference is limited by the need to define schemas or regex prior to generation. Without a predefined Pydantic model or grammar, it cannot dynamically adapt to open-ended text generation outside the specified structure.