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: 8,000+ | From: dottxt.ai (formerly .txt)

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

# 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

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 Doe, 30 years old, john@example.com"
generator = outlines.generate.json(model, User)
user = generator(prompt)

print(user.name)   # "John Doe"
print(user.age)    # 30
print(user.email)  # "john@example.com"

Core Concepts

1. Constrained Token Sampling

Outlines uses Finite State Machines (FSM) to constrain token generation at the logit level.

How it works:

1. Convert schema (JSON/Pydantic/regex) to context-free grammar (CFG)
2. Transform CFG into Finite State Machine (FSM)
3. Filter invalid tokens at each step during generation
4. Fast-forward when only one valid token exists

Benefits:

• Zero overhead: Filtering happens at token level
• Speed improvement: Fast-forward through deterministic paths
• Guaranteed validity: Invalid outputs impossible

import outlines

# Pydantic model -> JSON schema -> CFG -> FSM
class Person(BaseModel):
    name: str
    age: int

model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")

# Behind the scenes:
# 1. Person -> JSON schema
# 2. JSON schema -> CFG
# 3. CFG -> FSM
# 4. FSM filters tokens during generation

generator = outlines.generate.json(model, Person)
result = generator("Generate person: Alice, 25")

2. Structured Generators

Outlines provides specialized generators for different output types.

Choice Generator

# Multiple choice selection
generator = outlines.generate.choice(
    model,
    ["positive", "negative", "neutral"]
)

sentiment = generator("Review: This is great!")
# Result: One of the three choices

JSON Generator

from pydantic import BaseModel

class Product(BaseModel):
    name: str
    price: float
    in_stock: bool

# Generate valid JSON matching schema
generator = outlines.generate.json(model, Product)
product = generator("Extract: iPhone 15, $999, available")

# Guaranteed valid Product instance
print(type(product))  # <class '__main__.Product'>

Regex Generator

# Generate text matching regex
generator = outlines.generate.regex(
    model,
    r"[0-9]{3}-[0-9]{3}-[0-9]{4}"  # Phone number pattern
)

phone = generator("Generate phone number:")
# Result: "555-123-4567" (guaranteed to match pattern)

Integer/Float Generators

# Generate specific numeric types
int_generator = outlines.generate.integer(model)
age = int_generator("Person's age:")  # Guaranteed integer

float_generator = outlines.generate.float(model)
price = float_generator("Product price:")  # Guaranteed float

3. Model Backends

Outlines supports multiple local and API-based backends.

Transformers (Hugging Face)

import outlines

# Load from Hugging Face
model = outlines.models.transformers(
    "microsoft/Phi-3-mini-4k-instruct",
    device="cuda"  # Or "cpu"
)

# Use with any generator
generator = outlines.generate.json(model, YourModel)

llama.cpp

# Load GGUF model
model = outlines.models.llamacpp(
    "./models/llama-3.1-8b-instruct.Q4_K_M.gguf",
    n_gpu_layers=35
)

generator = outlines.generate.json(model, YourModel)

vLLM (High Throughput)

# For production deployments
model = outlines.models.vllm(
    "meta-llama/Llama-3.1-8B-Instruct",
    tensor_parallel_size=2  # Multi-GPU
)

generator = outlines.generate.json(model, YourModel)

OpenAI (Limited Support)

# Basic OpenAI support
model = outlines.models.openai(
    "gpt-4o-mini",
    api_key="your-api-key"
)

# Note: Some features limited with API models
generator = outlines.generate.json(model, YourModel)

4. Pydantic Integration

Outlines has first-class Pydantic support with automatic schema translation.

Basic Models

from pydantic import BaseModel, Field

class Article(BaseModel):
    title: str = Field(description="Article title")
    author: str = Field(description="Author name")
    word_count: int = Field(description="Number of words", gt=0)
    tags: list[str] = Field(description="List of tags")

model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, Article)

article = generator("Generate article about AI")
print(article.title)
print(article.word_count)  # Guaranteed > 0

Nested Models

class Address(BaseModel):
    street: str
    city: str
    country: str

class Person(BaseModel):
    name: str
    age: int
    address: Address  # Nested model

generator = outlines.generate.json(model, Person)
person = generator("Generate person in New York")

print(person.address.city)  # "New York"

Enums and Literals

from enum import Enum
from typing import Literal

class Status(str, Enum):
    PENDING = "pending"
    APPROVED = "approved"
    REJECTED = "rejected"

class Application(BaseModel):
    applicant: str
    status: Status  # Must be one of enum values
    priority: Literal["low", "medium", "high"]  # Must be one of literals

generator = outlines.generate.json(model, Application)
app = generator("Generate application")

print(app.status)  # Status.PENDING (or APPROVED/REJECTED)

Common Patterns

Pattern 1: Data Extraction

from pydantic import BaseModel
import outlines

class CompanyInfo(BaseModel):
    name: str
    founded_year: int
    industry: str
    employees: int

model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, CompanyInfo)

text = """
Apple Inc. was founded in 1976 in the technology industry.
The company employs approximately 164,000 people worldwide.
"""

prompt = f"Extract company information:\n{text}\n\nCompany:"
company = generator(prompt)

print(f"Name: {company.name}")
print(f"Founded: {company.founded_year}")
print(f"Industry: {company.industry}")
print(f"Employees: {company.employees}")

Pattern 2: Classification

from typing import Literal
import outlines

model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")

# Binary classification
generator = outlines.generate.choice(model, ["spam", "not_spam"])
result = generator("Email: Buy now! 50% off!")

# Multi-class classification
categories = ["technology", "business", "sports", "entertainment"]
category_gen = outlines.generate.choice(model, categories)
category = category_gen("Article: Apple announces new iPhone...")

# With confidence
class Classification(BaseModel):
    label: Literal["positive", "negative", "neutral"]
    confidence: float