outlines by davila7/claude-code-templates
npx skills add https://github.com/davila7/claude-code-templates --skill outlines在以下情况下使用 Outlines:
GitHub Stars : 8,000+ | 来自 : dottxt.ai(原 .txt)
# 基础安装
pip install outlines
# 安装特定后端
pip install outlines transformers # Hugging Face 模型
pip install outlines llama-cpp-python # llama.cpp
pip install outlines vllm # vLLM 用于高吞吐量
import outlines
from typing import Literal
# 加载模型
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# 使用类型约束生成
prompt = "Sentiment of 'This product is amazing!': "
generator = outlines.generate.choice(model, ["positive", "negative", "neutral"])
sentiment = generator(prompt)
print(sentiment) # "positive"(保证是其中之一)
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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")
# 生成结构化输出
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"
Outlines 使用有限状态机(FSM)在 logit 级别约束 Token 生成。
工作原理:
优势:
import outlines
# Pydantic 模型 -> JSON 模式 -> CFG -> FSM
class Person(BaseModel):
name: str
age: int
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# 幕后过程:
# 1. Person -> JSON 模式
# 2. JSON 模式 -> CFG
# 3. CFG -> FSM
# 4. FSM 在生成过程中过滤 Token
generator = outlines.generate.json(model, Person)
result = generator("Generate person: Alice, 25")
Outlines 为不同的输出类型提供专门的生成器。
# 多项选择
generator = outlines.generate.choice(
model,
["positive", "negative", "neutral"]
)
sentiment = generator("Review: This is great!")
# 结果:三个选项之一
from pydantic import BaseModel
class Product(BaseModel):
name: str
price: float
in_stock: bool
# 生成符合模式的有效 JSON
generator = outlines.generate.json(model, Product)
product = generator("Extract: iPhone 15, $999, available")
# 保证是有效的 Product 实例
print(type(product)) # <class '__main__.Product'>
# 生成匹配正则表达式的文本
generator = outlines.generate.regex(
model,
r"[0-9]{3}-[0-9]{3}-[0-9]{4}" # 电话号码模式
)
phone = generator("Generate phone number:")
# 结果:"555-123-4567"(保证匹配模式)
# 生成特定数值类型
int_generator = outlines.generate.integer(model)
age = int_generator("Person's age:") # 保证是整数
float_generator = outlines.generate.float(model)
price = float_generator("Product price:") # 保证是浮点数
Outlines 支持多个本地和基于 API 的后端。
import outlines
# 从 Hugging Face 加载
model = outlines.models.transformers(
"microsoft/Phi-3-mini-4k-instruct",
device="cuda" # 或 "cpu"
)
# 与任何生成器一起使用
generator = outlines.generate.json(model, YourModel)
# 加载 GGUF 模型
model = outlines.models.llamacpp(
"./models/llama-3.1-8b-instruct.Q4_K_M.gguf",
n_gpu_layers=35
)
generator = outlines.generate.json(model, YourModel)
# 用于生产部署
model = outlines.models.vllm(
"meta-llama/Llama-3.1-8B-Instruct",
tensor_parallel_size=2 # 多 GPU
)
generator = outlines.generate.json(model, YourModel)
# 基础 OpenAI 支持
model = outlines.models.openai(
"gpt-4o-mini",
api_key="your-api-key"
)
# 注意:API 模型的一些功能有限
generator = outlines.generate.json(model, YourModel)
Outlines 具有一流的 Pydantic 支持,可自动进行模式转换。
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) # 保证 > 0
class Address(BaseModel):
street: str
city: str
country: str
class Person(BaseModel):
name: str
age: int
address: Address # 嵌套模型
generator = outlines.generate.json(model, Person)
person = generator("Generate person in New York")
print(person.address.city) # "New York"
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 # 必须是枚举值之一
priority: Literal["low", "medium", "high"] # 必须是字面量之一
generator = outlines.generate.json(model, Application)
app = generator("Generate application")
print(app.status) # Status.PENDING(或 APPROVED/REJECTED)
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}")
from typing import Literal
import outlines
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# 二元分类
generator = outlines.generate.choice(model, ["spam", "not_spam"])
result = generator("Email: Buy now! 50% off!")
# 多类别分类
categories = ["technology", "business", "sports", "entertainment"]
category_gen = outlines.generate.choice(model, categories)
category = category_gen("Article: Apple announces new iPhone...")
# 带置信度
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")
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"]
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})")
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}")
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}")
import outlines
# 基础用法
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
# GPU 配置
model = outlines.models.transformers(
"microsoft/Phi-3-mini-4k-instruct",
device="cuda",
model_kwargs={"torch_dtype": "float16"}
)
# 热门模型
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")
# 加载 GGUF 模型
model = outlines.models.llamacpp(
"./models/llama-3.1-8b.Q4_K_M.gguf",
n_ctx=4096, # 上下文窗口
n_gpu_layers=35, # GPU 层数
n_threads=8 # CPU 线程数
)
# 完全 GPU 卸载
model = outlines.models.llamacpp(
"./models/model.gguf",
n_gpu_layers=-1 # 所有层都在 GPU 上
)
# 单 GPU
model = outlines.models.vllm("meta-llama/Llama-3.1-8B-Instruct")
# 多 GPU
model = outlines.models.vllm(
"meta-llama/Llama-3.1-70B-Instruct",
tensor_parallel_size=4 # 4 个 GPU
)
# 使用量化
model = outlines.models.vllm(
"meta-llama/Llama-3.1-8B-Instruct",
quantization="awq" # 或 "gptq"
)
# ✅ 好:具体类型
class Product(BaseModel):
name: str
price: float # 不是 str
quantity: int # 不是 str
in_stock: bool # 不是 str
# ❌ 不好:所有字段都是字符串
class Product(BaseModel):
name: str
price: str # 应该是 float
quantity: str # 应该是 int
from pydantic import Field
# ✅ 好:带约束
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+$")
# ❌ 不好:无约束
class User(BaseModel):
name: str
age: int
email: str
# ✅ 好:对固定集合使用枚举
class Priority(str, Enum):
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
class Task(BaseModel):
title: str
priority: Priority
# ❌ 不好:自由格式字符串
class Task(BaseModel):
title: str
priority: str # 可以是任何内容
# ✅ 好:清晰的上下文
prompt = """
Extract product information from the following text.
Text: iPhone 15 Pro costs $999 and is currently in stock.
Product:
"""
# ❌ 不好:最少的上下文
prompt = "iPhone 15 Pro costs $999 and is currently in stock."
from typing import Optional
# ✅ 好:为不完整数据设置可选字段
class Article(BaseModel):
title: str # 必需
author: Optional[str] = None # 可选
date: Optional[str] = None # 可选
tags: list[str] = [] # 默认空列表
# 即使缺少作者/日期也能成功
| 功能 | Outlines | Instructor | Guidance | LMQL |
|---|---|---|---|---|
| Pydantic 支持 | ✅ 原生 | ✅ 原生 | ❌ 无 | ❌ 无 |
| JSON 模式 | ✅ 是 | ✅ 是 | ⚠️ 有限 | ✅ 是 |
| 正则表达式约束 | ✅ 是 | ❌ 无 | ✅ 是 | ✅ 是 |
| 本地模型 | ✅ 完整 | ⚠️ 有限 | ✅ 完整 | ✅ 完整 |
| API 模型 | ⚠️ 有限 | ✅ 完整 | ✅ 完整 | ✅ 完整 |
| 零开销 | ✅ 是 | ❌ 无 | ⚠️ 部分 | ✅ 是 |
| 自动重试 | ❌ 无 | ✅ 是 | ❌ 无 | ❌ 无 |
| 学习曲线 | 低 | 低 | 低 | 高 |
何时选择 Outlines:
何时选择其他方案:
速度:
内存:
准确性:
references/json_generation.md - 全面的 JSON 和 Pydantic 模式references/backends.md - 后端特定配置references/examples.md - 生产就绪示例每周安装量
168
仓库
GitHub Stars
22.6K
首次出现
Jan 21, 2026
安全审计
安装于
claude-code133
opencode130
gemini-cli123
cursor116
codex110
antigravity105
Use Outlines when you need to:
GitHub Stars : 8,000+ | From : dottxt.ai (formerly .txt)
# 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
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)
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"
Outlines uses Finite State Machines (FSM) to constrain token generation at the logit level.
How it works:
Benefits:
Zero overhead : Filtering happens at token level
Speed improvement : Fast-forward through deterministic paths
Guaranteed validity : Invalid outputs impossible
import outlines
class Person(BaseModel): name: str age: int
model = outlines.models.transformers("microsoft/Phi-3-mini-4k-instruct")
generator = outlines.generate.json(model, Person) result = generator("Generate person: Alice, 25")
Outlines provides specialized generators for different output types.
# Multiple choice selection
generator = outlines.generate.choice(
model,
["positive", "negative", "neutral"]
)
sentiment = generator("Review: This is great!")
# Result: One of the three choices
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'>
# 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)
# 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
Outlines supports multiple local and API-based backends.
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)
# 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)
# 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)
# 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)
Outlines has first-class Pydantic support with automatic schema translation.
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
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"
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)
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}")
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")
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"]
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})")
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}")
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}")
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")
# 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
)
# 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"
)
# ✅ 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
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
# ✅ 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
# ✅ 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."
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
| 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 |
When to choose Outlines:
When to choose alternatives:
Speed:
Memory:
Accuracy:
references/json_generation.md - Comprehensive JSON and Pydantic patternsreferences/backends.md - Backend-specific configurationreferences/examples.md - Production-ready examplesWeekly Installs
168
Repository
GitHub Stars
22.6K
First Seen
Jan 21, 2026
Security Audits
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Installed on
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47,800 周安装
| ⚠️ Partial |
| ✅ Yes |
| Automatic Retrying | ❌ No | ✅ Yes | ❌ No | ❌ No |
| Learning Curve | Low | Low | Low | High |