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Rheasilvia
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outlines

Constrain token sampling to match JSON schemas, regex patterns, or Pydantic models.

Updated Jun 25, 2026
Search Tags:#pydantic#json schema#inference#structured generation#local models#constrained sampling
One-click install
npx skills add https://github.com/Rheasilvia/hermes-desktop --skill outlines-rheasilvia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/Rheasilvia/hermes-desktop/tree/main/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/Rheasilvia/hermes-desktop --skill outlines-rheasilvia

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the challenge of unreliable LLM output formats by enforcing strict structural constraints at the token level, ensuring that generated text always conforms to your required JSON, regex, or Pydantic schemas.

Core Features & Use Cases

  • Type-Safe Generation: Use Pydantic models to guarantee that LLM outputs match your application's data structures.
  • Constrained Sampling: Prevent hallucinations and formatting errors by filtering invalid tokens during the generation process.
  • Use Case: Automatically extract structured user profiles, invoice data, or classification labels from unstructured text with 100% schema compliance.

Quick Start

Use the outlines skill to generate a structured JSON object from the provided text using the User Pydantic model.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I guarantee structured LLM output generation from local models?▼

Guarantee structured LLM output generation by enforcing strict structural constraints at the token level, ensuring generated text always conforms to required JSON schemas, regex patterns, or Pydantic models.

What is the best way to ensure LLM JSON schema compliance without post-generation validation?▼

The best way to ensure JSON schema compliance is constrained sampling, which filters invalid tokens during the generation process to guarantee valid output formats at the logit level, eliminating the need for post-generation validation.

How do I use Pydantic models for type-safe text generation with vLLM?▼

Use Pydantic models for type-safe text generation with vLLM by constraining token sampling to match your application's data structures, guaranteeing that LLM outputs match your required data formats across supported inference backends.

Does constrained token sampling work with Transformers and llama.cpp backends?▼

Constrained token sampling works with Transformers, vLLM, and llama.cpp backends, supporting high-throughput local model deployment while enforcing strict JSON, regex, or Pydantic schema constraints.

Why does my LLM output fail Pydantic model validation during structured generation?▼

LLM output fails Pydantic model validation due to unconstrained generation producing hallucinations or formatting errors, which is solved by filtering invalid tokens during the generation process to guarantee schema compliance.

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