outlines

Outlines: structured JSON/regex/Pydantic LLM generation.

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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, john@example.com"
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)  # "john@example.com"

Core Concepts

1. Constrained Token Sampling

Outlines constrains token generation at the logit level using a compiled automaton derived from your output type.

How it works:

Convert the output type (JSON/Pydantic/regex/Literal) to a schema/grammar
Compile the grammar into a token-level automaton
Filter invalid tokens at each step during generation
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
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer

class Person(BaseModel):
    name: str
    age: int

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

result = model("Generate person: Alice, 25", Person)
person = Person.model_validate_json(result)

2. Output Types

In v1 you pass the desired output type directly as the second argument.

Multiple choice (`Literal`)

from typing import Literal

sentiment = model("Review: This is great!", Literal["positive", "negative", "neutral"])
# Result: one of the three choices

JSON via Pydantic

from pydantic import BaseModel

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

result = model("Extract: iPhone 15, $999, available", Product)
product = Product.model_validate_json(result)  # valid Product instance

Regex (pass a regex string)

# Generate text matching a regex pattern
phone = model("Generate phone number:", r"[0-9]{3}-[0-9]{3}-[0-9]{4}")
# Result: "555-123-4567" (guaranteed to match the pattern)

Numeric types

# Pass the Python type directly
age = model("Person's age:", int)      # guaranteed integer
price = model("Product price:", float)  # guaranteed float

3. Model Backends

Outlines supports multiple local and API-based backends via from_* factories.

Transformers (Hugging Face)

import outlines
from transformers import AutoModelForCausalLM, AutoTokenizer

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

result = model(prompt, YourModel)

llama.cpp

import outlines
from llama_cpp import Llama

llm = Llama("./models/llama-3.1-8b-instruct.Q4_K_M.gguf", n_gpu_layers=35, n_ctx=4096)
model = outlines.from_llamacpp(llm)

result = model(prompt, YourModel)

vLLM (High Throughput)

import outlines
from vllm import LLM

llm = LLM("meta-llama/Llama-3.1-8B-Instruct", tensor_parallel_size=2)
model = outlines.from_vllm(llm)

result = model(prompt, YourModel)

OpenAI (server-side constrained JSON)

import outlines
from openai import OpenAI

client = OpenAI()
model = outlines.from_openai(client, "gpt-4o-mini")

# API backends support JSON-schema style structured output
result = model(prompt, YourModel)

4. Pydantic Integration

Outlines has first-class Pydantic support with automatic schema translation. Generation returns a JSON string; call model_validate_json to get an instance.

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")

result = model("Generate article about AI", Article, max_new_tokens=300)
article = Article.model_validate_json(result)
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

result = model("Generate person in New York", Person)
person = Person.model_validate_json(result)
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

result = model("Generate application", Application)
app = Application.model_validate_json(result)
print(app.status)  # Status.PENDING (or APPROVED/REJECTED)

Common Patterns

Pattern 1: Data Extraction

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

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

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

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 = CompanyInfo.model_validate_json(model(prompt, CompanyInfo, max_new_tokens=200))

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
from pydantic import BaseModel

# Binary classification
result = model("Email: Buy now! 50% off!", Literal["spam", "not_spam"])

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

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

out = model("Review: This product is okay, nothing special", Classification)
result = Classification.model_validate_json(out)

Pattern 3: Structured Forms

class UserProfile(BaseModel):
    full_name: str
    age: int
    email: str
    phone: str
    country: str
    interests: list[str]

prompt = """
Extract user profile from:
Name: Alice Johnson
Age: 28
Email: alice@example.com
Phone: 555-0123
Country: USA
Interests: hiking, photography, cooking
"""

profile = UserProfile.model_validate_json(model(prompt, UserProfile, max_new_tokens=250))
print(profile.full_name)
print(profile.interests)  # ["hiking", "photography", "cooking"]

Pattern 4: Multi-Entity Extraction

from typing import Literal

class Entity(BaseModel):
    name: str
    type: Literal["PERSON", "ORGANIZATION", "LOCATION"]

class DocumentEntities(BaseModel):
    entities: list[Entity]

text = "Tim Cook met with Satya Nadella at Microsoft headquarters in Redmond."
prompt = f"Extract entities from: {text}"

result = DocumentEntities.model_validate_json(model(prompt, DocumentEntities, max_new_tokens=300))
for entity in result.entities:
    print(f"{entity.name} ({entity.type})")

Pattern 5: Code Generation

class PythonFunction(BaseModel):
    function_name: str
    parameters: list[str]
    docstring: str
    body: str

prompt = "Generate a Python function to calculate factorial"
func = PythonFunction.model_validate_json(model(prompt, PythonFunction, max_new_tokens=300))

print(f"def {func.function_name}({', '.join(func.parameters)}):")
print(f'    """{func.docstring}"""')
print(f"    {func.body}")

Pattern 6: Batch Processing

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

class Person(BaseModel):
    name: str
    age: int

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

texts = [
    "John is 30 years old",
    "Alice is 25 years old",
    "Bob is 40 years old",
]

# v1 accepts a list of prompts for batched generation
prompts = [f"Extract from: {t}" for t in texts]
outputs = model(prompts, Person, max_new_tokens=100)
people = [Person.model_validate_json(o) for o in outputs]
for person in people:
    print(f"{person.name}: {person.age}")

Backend Configuration

Transformers

import outlines
from transformers import AutoModelForCausalLM, AutoTokenizer

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

# Basic usage
model = outlines.from_transformers(
    AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="auto"),
    AutoTokenizer.from_pretrained(MODEL_NAME),
)

# GPU + dtype configuration is set on the HF model itself
import torch
model = outlines.from_transformers(
    AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map="cuda", torch_dtype=torch.float16),
    AutoTokenizer.from_pretrained(MODEL_NAME),
)

# Popular models
for name in [
    "meta-llama/Llama-3.1-8B-Instruct",
    "mistralai/Mistral-7B-Instruct-v0.3",
    "Qwen/Qwen2.5-7B-Instruct",
]:
    model = outlines.from_transformers(
        AutoModelForCausalLM.from_pretrained(name, device_map="auto"),
        AutoTokenizer.from_pretrained(name),
    )

llama.cpp

import outlines
from llama_cpp import Llama

# Load GGUF model
llm = Llama(
    "./models/llama-3.1-8b.Q4_K_M.gguf",
    n_ctx=4096,       # Context window
    n_gpu_layers=35,  # GPU layers
    n_threads=8,      # CPU threads
)
model = outlines.from_llamacpp(llm)

# Full GPU offload: set n_gpu_layers=-1 on the Llama object

vLLM (Production)

import outlines
from vllm import LLM

# Single GPU
model = outlines.from_vllm(LLM("meta-llama/Llama-3.1-8B-Instruct"))

# Multi-GPU
model = outlines.from_vllm(LLM("meta-llama/Llama-3.1-70B-Instruct", tensor_parallel_size=4))

# With quantization
model = outlines.from_vllm(LLM("meta-llama/Llama-3.1-8B-Instruct", quantization="awq"))

Best Practices

1. Use Specific Types

# ✅ Good: Specific types
class Product(BaseModel):
    name: str
    price: float  # Not str
    quantity: int  # Not str
    in_stock: bool  # Not str

# ❌ Bad: Everything as string
class Product(BaseModel):
    name: str
    price: str  # Should be float
    quantity: str  # Should be int

2. Add Constraints

from pydantic import Field

# ✅ Good: With constraints
class User(BaseModel):
    name: str = Field(min_length=1, max_length=100)
    age: int = Field(ge=0, le=120)
    email: str = Field(pattern=r"^[\w\.-]+@[\w\.-]+\.\w+$")

# ❌ Bad: No constraints
class User(BaseModel):
    name: str
    age: int
    email: str

3. Use Enums for Categories

# ✅ Good: Enum for fixed set
class Priority(str, Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"

class Task(BaseModel):
    title: str
    priority: Priority

# ❌ Bad: Free-form string
class Task(BaseModel):
    title: str
    priority: str  # Can be anything

4. Provide Context in Prompts

# ✅ Good: Clear context
prompt = """
Extract product information from the following text.
Text: iPhone 15 Pro costs $999 and is currently in stock.
Product:
"""

# ❌ Bad: Minimal context
prompt = "iPhone 15 Pro costs $999 and is currently in stock."

5. Handle Optional Fields

from typing import Optional

# ✅ Good: Optional fields for incomplete data
class Article(BaseModel):
    title: str  # Required
    author: Optional[str] = None  # Optional
    date: Optional[str] = None  # Optional
    tags: list[str] = []  # Default empty list

# Can succeed even if author/date missing

6. Always Validate JSON Output

# v1 returns a JSON string for Pydantic/JSON output types.
result = model(prompt, Article)          # str
article = Article.model_validate_json(result)  # Article instance

Comparison to Alternatives

Feature	Outlines	Instructor	Guidance	LMQL
Pydantic Support	✅ Native	✅ Native	✅ Yes	❌ No
JSON Schema	✅ Yes	✅ Yes	✅ Yes	✅ Yes
Regex Constraints	✅ Yes	❌ No	✅ Yes	✅ Yes
Local Models	✅ Full	⚠️ Limited	✅ Full	✅ Full
API Models	✅ Yes	✅ Full	✅ Yes	✅ Full
Zero Overhead	✅ Yes	❌ No	⚠️ Partial	✅ Yes
Automatic Retrying	❌ No	✅ Yes	❌ No	❌ No
Learning Curve	Low	Low	Low	High

When to choose Outlines:

Using local models (Transformers, llama.cpp, vLLM)
Need maximum inference speed
Want Pydantic model support
Require zero-overhead structured generation
Control token sampling process

When to choose alternatives:

Instructor: Need API models with automatic retrying
Guidance: Need token healing and complex workflows
LMQL: Prefer declarative query syntax

Performance Characteristics

Speed:

Zero overhead: Structured generation as fast as unconstrained
Fast-forward optimization: Skips deterministic tokens
1.2-2x faster than post-generation validation approaches

Memory:

Automaton compiled once per output type (cached)
Minimal runtime overhead
Efficient with vLLM for high throughput

Accuracy:

100% valid outputs (guaranteed by the constrained automaton)
No retry loops needed
Deterministic token filtering

Resources

Documentation: https://dottxt-ai.github.io/outlines/
GitHub: https://github.com/dottxt-ai/outlines (12k+ stars)
Discord: https://discord.gg/R9DSu34mGd
Blog: https://blog.dottxt.co

outlines

Outlines: Structured Text Generation

When to Use This Skill

Installation

Quick Start

Basic Example: Classification

With Pydantic Models

Core Concepts

1. Constrained Token Sampling

2. Output Types

Multiple choice (Literal)

JSON via Pydantic

Regex (pass a regex string)

Numeric types

3. Model Backends

Transformers (Hugging Face)

llama.cpp

vLLM (High Throughput)

OpenAI (server-side constrained JSON)

4. Pydantic Integration

Basic Models

Nested Models

Enums and Literals

Common Patterns

Pattern 1: Data Extraction

Pattern 2: Classification

Pattern 3: Structured Forms

Pattern 4: Multi-Entity Extraction

Pattern 5: Code Generation

Pattern 6: Batch Processing

Backend Configuration

Transformers

llama.cpp

vLLM (Production)

Best Practices

1. Use Specific Types

2. Add Constraints

3. Use Enums for Categories

4. Provide Context in Prompts

5. Handle Optional Fields

6. Always Validate JSON Output

Comparison to Alternatives

Performance Characteristics

Resources

See Also

Is this your skill?

Multiple choice (`Literal`)