outlines

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: 12,000+ | From: dottxt.ai (formerly .txt)

API note (Outlines 1.x): This skill targets the current v1 API. The pre-1.0 helpers (outlines.models.transformers(...), outlines.generate.json/choice/regex/...) have been removed. In v1 you create a model with outlines.from_transformers(...) (or from_vllm, from_llamacpp, from_openai) and then call the model directly with an output type: model(prompt, output_type). JSON/Pydantic outputs are returned as a JSON string — validate with YourModel.model_validate_json(result).

Installation

# 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

import outlines
from typing import Literal
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"

# v1: wrap a Transformers model + tokenizer
model = outlines.from_transformers(
    AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto"),
    AutoTokenizer.from_pretrained(MODEL_NAME),
)

# Call the model directly with an output type
prompt = "Sentiment of 'This product is amazing!': "
sentiment = model(prompt, Literal["positive", "negative", "neutral"])

print(sentiment)  # "positive" (guaranteed one of these)

With Pydantic Models

from pydantic import BaseModel
import outlines
from transformers import AutoModelForCausalLM, AutoTokenizer

class User(BaseModel):
    name: str
    age: int
    email: str

MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
model = outlines.from_transformers(
    AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto"),
    AutoTokenizer.from_pretrained(MODEL_NAME),
)

# Generate structured output (returns a JSON string)
prompt = "Extract user: John Doe, 30 years old, [email protected]"
result = model(prompt, User, max_new_tokens=200)

user = User.model_validate_json(result)  # parse into the Pydantic model
print(user.name)   # "John Doe"
print(user.age)    # 30
print(user.email)  # "[email protected]"