Agent skill

outlines

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

View SKILL.md on GitHub Repository

Stars 56,643

Forks 7,481

Install this agent skill to your Project

npx add-skill https://github.com/NousResearch/hermes-agent/tree/main/skills/mlops/inference/outlines

Metadata

Additional technical details for this skill

hermes: { "tags": [ "Prompt Engineering", "Outlines", "Structured Generation", "JSON Schema", "Pydantic", "Local Models", "Grammar-Based Generation", "vLLM", "Transformers", "Type Safety" ] }

SKILL.md

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 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:

Convert schema (JSON/Pydantic/regex) to context-free grammar (CFG)
Transform CFG into Finite State Machine (FSM)
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

python

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

python

# 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

python

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

python

# 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

python

# 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)

python

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

python

# 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)

python

# 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)

python

# 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

python

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

python

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

python

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

python

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

python

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

classifier = outlines.generate.json(model, Classification)
result = classifier("Review: This product is okay, nothing special")

Pattern 3: Structured Forms

python

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

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

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

profile = generator(prompt)
print(profile.full_name)
print(profile.interests)  # ["hiking", "photography", "cooking"]

Pattern 4: Multi-Entity Extraction

python

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

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

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

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

result = generator(prompt)
for entity in result.entities:
    print(f"{entity.name} ({entity.type})")

Pattern 5: Code Generation

python

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

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

prompt = "Generate a Python function to calculate factorial"
func = generator(prompt)

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

Pattern 6: Batch Processing

python

def batch_extract(texts: list[str], schema: type[BaseModel]):
    """Extract structured data from multiple texts."""
    model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
    generator = outlines.generate.json(model, schema)

    results = []
    for text in texts:
        result = generator(f"Extract from: {text}")
        results.append(result)

    return results

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

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

people = batch_extract(texts, Person)
for person in people:
    print(f"{person.name}: {person.age}")

Backend Configuration

Transformers

python

import outlines

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

# GPU configuration
model = outlines.models.transformers(
    "microsoft/Phi-3-mini-4k-instruct",
    device="cuda",
    model_kwargs={"torch_dtype": "float16"}
)

# Popular models
model = outlines.models.transformers("meta-llama/Llama-3.1-8B-Instruct")
model = outlines.models.transformers("mistralai/Mistral-7B-Instruct-v0.3")
model = outlines.models.transformers("Qwen/Qwen2.5-7B-Instruct")

llama.cpp

python

# Load GGUF model
model = outlines.models.llamacpp(
    "./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
)

# Full GPU offload
model = outlines.models.llamacpp(
    "./models/model.gguf",
    n_gpu_layers=-1  # All layers on GPU
)

vLLM (Production)

python

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

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

# With quantization
model = outlines.models.vllm(
    "meta-llama/Llama-3.1-8B-Instruct",
    quantization="awq"  # Or "gptq"
)

Best Practices

1. Use Specific Types

python

# ✅ 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

python

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

python

# ✅ 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

python

# ✅ 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

python

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

Comparison to Alternatives

Feature	Outlines	Instructor	Guidance	LMQL
Pydantic Support	✅ Native	✅ Native	❌ No	❌ No
JSON Schema	✅ Yes	✅ Yes	⚠️ Limited	✅ Yes
Regex Constraints	✅ Yes	❌ No	✅ Yes	✅ Yes
Local Models	✅ Full	⚠️ Limited	✅ Full	✅ Full
API Models	⚠️ Limited	✅ Full	✅ Full	✅ 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:

FSM compiled once per schema (cached)
Minimal runtime overhead
Efficient with vLLM for high throughput

Accuracy:

100% valid outputs (guaranteed by FSM)
No retry loops needed
Deterministic token filtering

Resources

Documentation: https://outlines-dev.github.io/outlines
GitHub: https://github.com/outlines-dev/outlines (8k+ stars)
Discord: https://discord.gg/R9DSu34mGd
Blog: https://blog.dottxt.co

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Metadata

SKILL.md

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. Structured Generators

Choice Generator

JSON Generator

Regex Generator

Integer/Float Generators

3. Model Backends

Transformers (Hugging Face)

llama.cpp

vLLM (High Throughput)

OpenAI (Limited Support)

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

Comparison to Alternatives

Performance Characteristics

Resources

See Also

Recommended Agent Skills

agentmail

base

solana

one-three-one-rule

fastmcp

qdrant-vector-search