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outlines

Generate JSON, Pydantic, and regex outputs with FSM-based constraints.

Updated Jun 17, 2026
Search Tags:#pydantic#validation#json#regex#fsm#local-models#structured-generation
One-click install
npx skills add https://github.com/anilcan-kara/nozich-agent --skill outlines-anilcan-kara
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/anilcan-kara/nozich-agent/tree/main/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/anilcan-kara/nozich-agent --skill outlines-anilcan-kara

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires outlines, transformers, vllm, pydantic.

What problem does it solve?

This skill enables zero-overhead, schema-driven generation of JSON, Pydantic models, and regular-expression constrained outputs from language models, reducing post-processing and validation work.

Core Features & Use Cases

  • Constrained generation with grammar-based FSMs to guarantee valid outputs
  • Pydantic integration for type-safe outputs and automatic validation
  • Local model backends support (Transformers, llama.cpp, vLLM) for fast, offline inference
  • JSON Schema and literal constraints for structured outputs
  • Common use cases: data extraction, form processing, document automation

Quick Start

Provide a prompt that asks for a JSON object that exactly conforms to a given Pydantic schema.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I guarantee JSON output from a local model?▼

Guaranteed JSON output from local models is achieved using FSM-based constraints that enforce schema-driven generation, drastically reducing post-processing and validation work.

Can I use Pydantic for structured generation with vLLM?▼

Yes, Pydantic integration provides type-safe outputs and automatic validation for vLLM. This allows you to define JSON schemas directly in Pydantic and apply FSM constraints to generate compliant local model outputs.

How does FSM-based constrained generation work?▼

FSM-based constrained generation works by applying finite state machine grammar constraints during local model inference, ensuring that the generated tokens strictly adhere to JSON schema, Pydantic types, or regular expressions without overhead.

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

Yes, structured generation supports local model backends including llama.cpp, Transformers, and vLLM. This enables fast, offline inference with schema-driven outputs across different local environments.

What is the best way to extract data into a specific JSON schema?▼

The best way to extract data into a specific JSON schema is using Pydantic integration with local models, which guarantees type-safe, structured outputs and automatic validation for data extraction and form processing tasks.

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