outlines

Generate structured JSON, regex, and Pydantic model outputs from unstructured text.

1|Updated May 21, 2026

Search Tags:#pydantic #json schema #structured generation #local models #grammar-based

Ready to run

npx skills add https://github.com/blueskies1818/hermesALIone --skill outlines-blueskies1818

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/blueskies1818/hermesALIone/tree/main/Agent/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/blueskies1818/hermesALIone --skill outlines-blueskies1818

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires outlines, transformers, vllm, pydantic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Outlines addresses the challenge of generating valid JSON, regex, and Pydantic model outputs from prompts, ensuring structured data generation for applications like code, schema, and configuration file generation.

Core Features & Use Cases

Guaranteed Validity: Ensures generated JSON adheres to provided schemas.
Pydantic Model Support: Generates outputs with type safety.
Local Models: Integrates with local models for performance.
Use Cases: Ideal for applications requiring structured output from unstructured text, such as generating configuration files, data extraction, and schema validation.

Quick Start

Use the Outlines skill to generate a JSON object from the prompt: "Create a user: John Doe, 30, [email protected]".

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 unstructured text using a local LLM?▼

Structured generation enforces grammar-based constraints to ensure local LLMs produce valid JSON, regex, or Pydantic model outputs from unstructured text. This guarantees schema adherence and eliminates formatting errors during generation.

Can I use Pydantic models to enforce structured outputs from local models?▼

Yes, Pydantic models enforce structured outputs from local models by validating generated data against defined schemas. This provides type safety and guarantees the LLM produces configuration or code matching your exact model constraints.

Does structured generation work with vLLM and Transformers?▼

Structured generation works directly with vLLM and Transformers. It integrates with these local model platforms to constrain text generation, ensuring outputs match specific JSON schemas or regex patterns reliably.

What is the best way to generate configuration files from unstructured prompts?▼

The best way to generate configuration files from unstructured prompts is using grammar-based structured generation. By mapping prompts to JSON schemas or Pydantic models, you guarantee the resulting configuration data is valid and correctly formatted.

Why do my LLM outputs fail JSON schema validation?▼

LLM outputs fail JSON schema validation because standard generation lacks grammar constraints. Applying structured generation forces the model to produce tokens that strictly follow your schema, guaranteeing valid JSON or Pydantic outputs.

Do I need Pydantic to generate structured JSON with local models?▼

You do not need Pydantic to generate structured JSON with local models. You can use raw JSON schemas or regex patterns directly, though Pydantic provides helpful type safety for data validation workflows.