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outlines

Generate structured JSON and Pydantic outputs from local models.

2|Updated Apr 25, 2026
Search Tags:#pydantic#json-schema#outlines#local-models#structured-generation#grammar-based-generation
One-click install
npx skills add https://github.com/john-data-chen/hermes-agent-backup --skill outlines-john-data-chen
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/john-data-chen/hermes-agent-backup/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/john-data-chen/hermes-agent-backup --skill outlines-john-data-chen

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Outlines provides deterministic, structured generation with safeguards to produce valid JSON, Pydantic models, and regex-constrained outputs from local models.

Core Features & Use Cases

  • Structured generation for JSON, regex, and Pydantic outputs
  • Local-model backends (Transformers, llama.cpp, vLLM) with FSM-based constraints
  • JSON schema and Pydantic integration for type-safe results
  • Zero-overhead generation with grammar-based token filtering
  • Use cases include data extraction, form processing, and structured data workflows

Quick Start

Install Outlines, load a local model, and generate a type-safe JSON/Pydantic output from a defined schema.

Frequently Asked Questions about outlines

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate valid JSON from a local model using a Pydantic schema?▼

To generate valid JSON from a local model, apply grammar-based constraints and Pydantic models to filter tokens via FSM-driven logic. This ensures outputs conform strictly to your defined schema with zero overhead during generation.

Does structured generation work with llama.cpp and vLLM backends?▼

Structured generation works with llama.cpp and vLLM backends through FSM-based token filtering. It applies grammar-based constraints across these local-model environments to ensure type-safe results without requiring external API calls.

Why does my local model output invalid JSON during data extraction tasks?▼

Local models output invalid JSON during data extraction due to unconstrained token generation. Applying FSM-driven token filtering with JSON schemas enforces grammar-based constraints, eliminating ambiguity and guaranteeing in-schema validation.

What is the best way to enforce regex patterns on local model outputs?▼

The best way to enforce regex patterns on local model outputs is using FSM-driven token filtering. This grammar-based generation method intercepts and filters tokens to guarantee that results match your defined regex constraints with zero overhead.

Can I use Pydantic models for type-safe form processing with local models?▼

You can use Pydantic models for type-safe form processing with local models by applying structured generation. FSM-based constraints filter tokens dynamically, ensuring the local model's output strictly validates against your Pydantic schema.

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