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outlines

Generate structured JSON, XML, or code from unstructured text using Pydantic models.

Updated Oct 23, 2024
Search Tags:#type safety#pydantic#vLLM#transformers#structured generation
One-click install
npx skills add https://github.com/lenadlm/docker --skill outlines-lenadlm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/lenadlm/docker/tree/main/hermes-skills/skills/mlops/inference/outlines
Command: npx skills add https://github.com/lenadlm/docker --skill outlines-lenadlm

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?

This Skill ensures the generation of valid JSON/XML/code structures with Pydantic models, supports local models (Transformers, vLLM), and maximizes inference speed.

Core Features & Use Cases

  • Structured Generation: Generates valid JSON/XML/code structures.
  • Type Safety: Uses Pydantic models for type-safe outputs.
  • Local Models: Supports local models like Transformers, vLLM, and llama.cpp.
  • Inference Speed: Optimized for zero-overhead structured generation.
  • Use Case: Use Outlines to generate structured data from unstructured text, like converting interview transcripts into structured information.

Quick Start

Run the following command: outlines generate "Extract user: John Doe, 30 years old, [email protected]" -m transformers -o user.json

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 local models?▼

You can generate valid JSON from unstructured text by using Pydantic models with local models like Transformers and vLLM. This approach ensures type-safe outputs and valid JSON/XML structures during text generation.

Does structured generation work with vLLM and Transformers for high-throughput inference?▼

Yes, structured generation works with vLLM and Transformers to provide optimized high-throughput and low-latency inference. It ensures zero-overhead structured generation while maintaining type safety using Pydantic.

How do I enforce type safety on LLM outputs?▼

You enforce type safety on LLM outputs by applying Pydantic models to constrain the generation process. This guarantees the model produces valid JSON, XML, or code structures that match your defined schemas.

What is the best way to extract structured information from interview transcripts?▼

The best way to extract structured information from transcripts is using structured generation to convert unstructured text into valid JSON formats. This method leverages Pydantic models to ensure type-safe outputs from local models.

Do I need Pydantic to generate structured data with Outlines?▼

Yes, you need Pydantic to define type-safe schemas for structured data generation. The framework relies on Pydantic models alongside dependencies like Transformers and vLLM to ensure valid outputs.

Can I use llama.cpp for type-safe text generation?▼

Yes, you can use llama.cpp alongside Transformers and vLLM for type-safe text generation. These local models are supported to ensure valid JSON and XML outputs with optimized inference speed.

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