Generate schema-constrained JSON or typed objects from LLM prompts.
npx skills add https://github.com/MarbleSodas/Mavis --skill outlines-marblesodas
Please help me install this Agent Skill. Skill: outlines Source: https://github.com/MarbleSodas/Mavis/tree/main/optional-skills/mlops/inference/outlines Command: npx skills add https://github.com/MarbleSodas/Mavis --skill outlines-marblesodas
Outlines removes the pain of getting unreliable, malformed, or unvalidated model outputs by constraining generation to a precise structure so downstream code can trust the result.
Example use case: Extract product details from free-form text into a Pydantic model (name, price, availability) while ensuring fields and constraints (like regexes, numeric ranges, and enum values) are satisfied.
Ask your AI to extract entities from a document and return the result as a validated Pydantic JSON object using Outlines structured generation.
High-intent search queries and answers about installing and using this skill.
To enforce JSON schema constraints during local LLM generation, you apply grammar-constrained decoding to filter tokens. This ensures the model output matches Pydantic models or JSON schemas for valid structured generation.
Yes, you can use Pydantic models for structured output with vLLM. The backend supports grammar-constrained decoding to generate type-safe JSON objects that match your defined schemas during local inference.
The best way to extract typed objects from unstructured text using local inference is schema-constrained generation. It uses finite-state token filtering to guarantee outputs match your Pydantic models or regex constraints.
Yes, grammar-constrained decoding works with Transformers and llama.cpp backends. These compatible local inference engines enforce valid JSON formats at generation time to ensure reliable structured output.
You need finite-state constrained decoding for LLM information extraction to remove malformed or unvalidated model outputs. It enforces valid formats at generation time, ensuring downstream code can trust the extracted JSON results.
matteocervelliGenerate Pydantic schemas with validation and nested models.
ruiyangruiyiExtracts structured JSON from LLM outputs using Pydantic schema validation with automatic retries and optional streaming, supporting OpenAI and Anthropic via Instructor.
Simon-Copilot-StudioEnforce valid JSON, XML, and code structures via grammar-based FSM constraints.
ruiyangruiyiGenerate schema-constrained LLM outputs as JSON, regex matches, or Pydantic instances.
