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To guarantee structured JSON outputs from a local model, enforce Pydantic-based schemas using grammar-driven generation. This applies FSM-based constrained sampling to ensure the output strictly validates against your defined model.

FSM-based constrained sampling is a mechanism that enforces grammar constraints during text generation. It ensures zero-overhead, schema-driven outputs by guiding the model's token selection to perfectly match a defined Pydantic schema.

Yes, you can use local models with Transformers or vLLM for schema-driven data extraction. The system supports local backends for fast, offline inference while guaranteeing outputs conform to your Pydantic schemas.

The best way to generate XML formatted data from local models is using grammar-driven constrained sampling. By enforcing a schema, it guarantees machine-parseable XML outputs without relying on post-generation parsing or validation.

No, constrained generation with Pydantic schemas does not add inference overhead. It uses caching and fast-forward optimizations to provide zero-overhead, high-throughput structured generation across supported backends.

You need grammar-based FSM constraints when you require strict, machine-parseable outputs for tasks like data extraction, form processing, or contract-driven data generation, ensuring outputs validate perfectly against Pydantic models.