outlineslisted

# Outlines: Structured Text Generation ## When to Use This Skill Use Outlines when you need to: - **Guarantee valid JSON/XML/code** structure during generation - **Use Pydantic models** for type-safe outputs - **Support local models** (Transformers, llama.cpp, vLLM) - **Maximize inference speed** with zero-overhead structured generation - **Generate against JSON schemas** automatically - **Control token sampling** at the grammar level **GitHub Stars**: 8,000+ | **From**: dottxt.ai (formerly .txt) ## Installation ```bash # Base installation pip install outlines # With specific backends pip install outlines transformers # Hugging Face models pip install outlines llama-cpp-python # llama.cpp pip install outlines vllm # vLLM for high-throughput ``` ## Quick Start ### Basic Example: Classification ```python import outlines from typing import Literal # Load model model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct") # Generate with type constraint prompt = "Sentiment of 'This product is amazing!': " generator = outlines.generate.choice(model, ["positive", "negative", "neutral"]) sentiment = generator(prompt) print(sentiment) # "positive" (guaranteed one of these) ``` ### With Pydantic Models ```python from pydantic import BaseModel import outlines class User(BaseModel): name: str age: int email: str model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct") # Generate structured output prompt = "Extract user: John Do