outlines

Name: outlines
Availability: InStock
Author: Khyro

Generate structured outputs constrained by Pydantic models, JSON Schema, and regex.

Updated Apr 2, 2026

Search Tags:#pydantic #json schema #transformers #structured generation #vllm #llamacpp

Ready to run

npx skills add https://github.com/JKhyro/HERMES-AGENT --skill outlines-jkhyro

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Outlines removes the need to repair or re-ask for malformed model output by generating text that already matches a schema, pattern, or typed object from the start.

Core Features & Use Cases

It supports Pydantic models, JSON Schema, enums, literals, regex-constrained text, nested structures, and high-throughput structured generation for extraction, classification, form parsing, SQL drafting, and code scaffolding. It is especially useful when you need reliable JSON or object outputs from local models such as Transformers, llama.cpp, or vLLM, while keeping inference fast and avoiding brittle post-processing.

Quick Start

Ask the model to extract your target fields into a structured JSON object that matches a Pydantic model.

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 language model without post-processing?▼

To generate valid JSON without post-processing, you constrain the model's output using Pydantic models or JSON Schema. This structured generation prevents malformed outputs by ensuring the generated text matches your target schema from the start.

What is structured generation and when do I need it for LLMs?▼

Structured generation forces language models to produce text that matches a specific schema, regex, or typed object. You need it when extracting data, parsing nested forms, or drafting code, ensuring reliable outputs while avoiding brittle repairs or re-asking.

Can I use Pydantic models for structured output with vLLM and llama.cpp?▼

Yes, you can use Pydantic models with vLLM and llama.cpp. The Skill supports backend-specific configurations for these platforms, allowing you to enforce structured outputs across local models while maintaining fast inference speeds.

What's the best way to enforce regex constraints during transformer inference?▼

The best way to enforce regex constraints during transformer inference is to apply structured generation directly within the backend. This guarantees the generated text conforms to your pattern, eliminating invalid outputs and avoiding slow post-processing.

Why does my language model output invalid JSON when doing high-throughput batch generation?▼

Models output invalid JSON during high-throughput batch generation because they lack inherent structural constraints. By applying JSON Schema or Pydantic constraints before generation, you prevent malformed text and ensure valid structured outputs across all batches.

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

Structured generation works with both OpenAI API backends and local models. It supports backend-specific configurations for Transformers, llama.cpp, vLLM, and OpenAI, allowing you to enforce valid structured outputs across different inference environments.