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You can enforce structured JSON generation from local models by validating token generation against a predefined schema. This approach uses FSM-based token filtering to guarantee outputs strictly match your JSON Schema or Pydantic models during generation.

The best way to constrain local LLM outputs to valid Pydantic models is through schema-driven generation. This mechanism intercepts token generation, filtering tokens to ensure the final output strictly conforms to the defined Pydantic type structure.

Yes, structured generation works directly with llama.cpp and vLLM backends. It provides zero-overhead, offline local-backend support for Transformers, vLLM, and llama.cpp deployments without requiring external API calls.

You validate XML schema constraints during local model inference by applying FSM-based token filtering. This process guarantees zero-overhead generation by constraining the model's output to produce valid XML structures matching your schema specification.

Local models output malformed JSON during data extraction because they lack inherent schema enforcement. Applying FSM-based token filtering during generation solves this by validating tokens against a JSON Schema, ensuring type-safe, machine-readable results.