outlines

Generate validated structured outputs using JSON schemas and Pydantic models.

Updated May 24, 2026

Search Tags:#pydantic #json schema #transformers #structured generation #vllm #regex constraints #llm outputs

Ready to run

npx skills add https://github.com/anxiety135790/hermes-skills --skill outlines-anxiety135790

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/anxiety135790/hermes-skills/tree/main/mlops/inference/outlines
Command: npx skills add https://github.com/anxiety135790/hermes-skills --skill outlines-anxiety135790

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you produce LLM outputs that are valid by construction, avoiding brittle post-processing, failed parses, and repeated retries when you need strict structure from model responses.

Core Features & Use Cases

Structured generation: Create JSON, regex-matching text, integers, floats, and choice outputs with schema-level constraints.
Pydantic and JSON Schema support: Generate type-safe objects for extraction, classification, forms, and nested data models.
Local and production backends: Work with Transformers, llama.cpp, vLLM, and limited OpenAI support for development or deployment.
Use case: Turn free-form text like resumes, invoices, reviews, or documents into validated records that can be safely consumed by downstream systems.

Quick Start

Use the outlines skill to extract a valid Pydantic object from the given text using a local model backend.

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 outputs from an LLM using Pydantic models?▼

Structured generation avoids brittle post-processing and failed parses by filtering tokens deterministically during LLM generation. It uses JSON schemas, Pydantic models, and regex constraints to guarantee valid outputs for extraction, classification, and document transformation workflows.

Can I use structured generation with vLLM and Transformers for high-throughput extraction?▼

Yes, structured generation works with both vLLM and Transformers backends for high-throughput and local extraction. You can apply schema-level constraints to safely turn free-form text like resumes or invoices into validated records across development and production environments.

How do I constrain LLM outputs to match a specific regex pattern?▼

You can constrain LLM outputs to match a specific regex pattern by applying regex constraints during generation. This allows you to generate validated text, integers, floats, and choice outputs that strictly adhere to your defined schema-level rules.

What is the best way to parse unstructured documents into validated records?▼

The best way to parse unstructured documents into validated records is using schema-level constraints for structured generation. This transforms free-form text like invoices and reviews into type-safe objects that can be safely consumed by downstream systems.

Does structured generation work with local models or only OpenAI backends?▼

Structured generation works with local model backends like Transformers, llama.cpp, and vLLM, alongside limited OpenAI support. This allows you to configure deterministic token filtering and schema validation across both development and production environments.