outlines

Generate structured JSON outputs from local LLMs by using Outlines to constrain model generation to match your provided JSON schema. This ensures valid, predictable structured data extraction from semi-structured text inputs.

Yes, you can use Pydantic models for structured generation with vLLM. Outlines supports Pydantic model inference alongside transformers and llama.cpp, enabling high-throughput structured data validation and generation.

The best way to force an LLM to follow a regex pattern is using Outlines for regex pattern matching. This constrains the local model's output to strictly adhere to your specified regex, ensuring valid and predictable text generation.

Yes, structured generation works with local models like transformers and llama.cpp. Outlines is optimized for local LLMs and vLLM, providing fast inference while ensuring outputs conform to JSON, regex, or Pydantic schemas.

You need Pydantic and outlines dependencies to define data schemas and constrain LLM generation. Outlines handles the structured generation logic while Pydantic provides the model inference schemas required for valid data extraction.