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FSM-based structured generation enforces valid JSON structure during local model generation by constraining the output token by token. This approach uses Pydantic models for type-safe outputs and supports local backends like Transformers and vLLM.

Using Pydantic models to define expected schemas is the best way to generate structured outputs for data extraction pipelines. This ensures artifacts strictly adhere to JSON or XML schemas, minimizing post-processing errors and enabling reliable downstream processing.

Yes, you can use vLLM for structured generation with private local deployments. The system explicitly supports local backends like vLLM and Transformers, allowing you to maximize inference speed while maintaining type-safe structured outputs in isolated environments.

Yes, Pydantic model integration works for generating XML schemas. The system ensures generated artifacts adhere to specified XML schemas or code structures, automating the production of strictly structured outputs for deterministic formatting and downstream automation.

FSM-based structured generation maximizes inference speed by using a finite state machine to constrain valid tokens during the generation process. This prevents invalid sequences from being computed, accelerating inference while guaranteeing valid JSON or XML outputs.