[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EX08D13J72PXM1Y5QT4XD0":3,"skill-same:01M6EX08D13J72PXM1Y5QT4XD0":95},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":14,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":6,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6EX08D13J72PXM1Y5QT4XD0","outlines",null,"Guarantee valid JSON\u002FXML\u002Fcode 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","henriquescastilho\u002Fmy-claude","henriquescastilho","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153296945?v=4","01M6ESH7EXPXTH9CVQ4K16SRG8","Henrique Scheer","01M6ESH7JHEK2PTNNH4HEH787Z",0,"MIT","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-code-templates\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"Outlines","Grammar-Based Generation","speed","local","structure","schema","use","grammar","Safety","Type Safety","Transformers","safe","Local Models","Pydantic","Structured Generation","library","code","json schema","support","valid","2026-11-10 10:12:30",false,"---\nname: outlines\ndescription: Guarantee valid JSON\u002FXML\u002Fcode 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\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ntags: [Prompt Engineering, Outlines, Structured Generation, JSON Schema, Pydantic, Local Models, Grammar-Based Generation, vLLM, Transformers, Type Safety]\ndependencies: [outlines, transformers, vllm, pydantic]\n---\n\n# Outlines: Structured Text Generation\n\n## When to Use This Skill\n\nUse Outlines when you need to:\n- **Guarantee valid JSON\u002FXML\u002Fcode** structure during generation\n- **Use Pydantic models** for type-safe outputs\n- **Support local models** (Transformers, llama.cpp, vLLM)\n- **Maximize inference speed** with zero-overhead structured generation\n- **Generate against JSON schemas** automatically\n- **Control token sampling** at the grammar level\n\n**GitHub Stars**: 8,000+ | **From**: dottxt.ai (formerly .txt)\n\n## Installation\n\n```bash\n# Base installation\npip install outlines\n\n# With specific backends\npip install outlines transformers  # Hugging Face models\npip install outlines llama-cpp-python  # llama.cpp\npip install outlines vllm  # vLLM for high-throughput\n```\n\n## Quick Start\n\n### Basic Example: Classification\n\n```python\nimport outlines\nfrom typing import Literal\n\n# Load model\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Generate with type constraint\nprompt = \"Sentiment of 'This product is amazing!': \"\ngenerator = outlines.generate.choice(model, [\"positive\", \"negative\", \"neutral\"])\nsentiment = generator(prompt)\n\nprint(sentiment)  # \"positive\" (guaranteed one of these)\n```\n\n### With Pydantic Models\n\n```python\nfrom pydantic import BaseModel\nimport outlines\n\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Generate structured output\nprompt = \"Extract user: John Doe, 30 years old, john@example.com\"\ngenerator = outlines.generate.json(model, User)\nuser = generator(prompt)\n\nprint(user.name)   # \"John Doe\"\nprint(user.age)    # 30\nprint(user.email)  # \"john@example.com\"\n```\n\n## Core Concepts\n\n### 1. Constrained Token Sampling\n\nOutlines uses Finite State Machines (FSM) to constrain token generation at the logit level.\n\n**How it works:**\n1. Convert schema (JSON\u002FPydantic\u002Fregex) to context-free grammar (CFG)\n2. Transform CFG into Finite State Machine (FSM)\n3. Filter invalid tokens at each step during generation\n4. Fast-forward when only one valid token exists\n\n**Benefits:**\n- **Zero overhead**: Filtering happens at token level\n- **Speed improvement**: Fast-forward through deterministic paths\n- **Guaranteed validity**: Invalid outputs impossible\n\n```python\nimport outlines\n\n# Pydantic model -> JSON schema -> CFG -> FSM\nclass Person(BaseModel):\n    name: str\n    age: int\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Behind the scenes:\n# 1. Person -> JSON schema\n# 2. JSON schema -> CFG\n# 3. CFG -> FSM\n# 4. FSM filters tokens during generation\n\ngenerator = outlines.generate.json(model, Person)\nresult = generator(\"Generate person: Alice, 25\")\n```\n\n### 2. Structured Generators\n\nOutlines provides specialized generators for different output types.\n\n#### Choice Generator\n\n```python\n# Multiple choice selection\ngenerator = outlines.generate.choice(\n    model,\n    [\"positive\", \"negative\", \"neutral\"]\n)\n\nsentiment = generator(\"Review: This is great!\")\n# Result: One of the three choices\n```\n\n#### JSON Generator\n\n```python\nfrom pydantic import BaseModel\n\nclass Product(BaseModel):\n    name: str\n    price: float\n    in_stock: bool\n\n# Generate valid JSON matching schema\ngenerator = outlines.generate.json(model, Product)\nproduct = generator(\"Extract: iPhone 15, $999, available\")\n\n# Guaranteed valid Product instance\nprint(type(product))  # \u003Cclass '__main__.Product'>\n```\n\n#### Regex Generator\n\n```python\n# Generate text matching regex\ngenerator = outlines.generate.regex(\n    model,\n    r\"[0-9]{3}-[0-9]{3}-[0-9]{4}\"  # Phone number pattern\n)\n\nphone = generator(\"Generate phone number:\")\n# Result: \"555-123-4567\" (guaranteed to match pattern)\n```\n\n#### Integer\u002FFloat Generators\n\n```python\n# Generate specific numeric types\nint_generator = outlines.generate.integer(model)\nage = int_generator(\"Person's age:\")  # Guaranteed integer\n\nfloat_generator = outlines.generate.float(model)\nprice = float_generator(\"Product price:\")  # Guaranteed float\n```\n\n### 3. Model Backends\n\nOutlines supports multiple local and API-based backends.\n\n#### Transformers (Hugging Face)\n\n```python\nimport outlines\n\n# Load from Hugging Face\nmodel = outlines.models.transformers(\n    \"microsoft\u002FPhi-3-mini-4k-instruct\",\n    device=\"cuda\"  # Or \"cpu\"\n)\n\n# Use with any generator\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### llama.cpp\n\n```python\n# Load GGUF model\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fllama-3.1-8b-instruct.Q4_K_M.gguf\",\n    n_gpu_layers=35\n)\n\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### vLLM (High Throughput)\n\n```python\n# For production deployments\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    tensor_parallel_size=2  # Multi-GPU\n)\n\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### OpenAI (Limited Support)\n\n```python\n# Basic OpenAI support\nmodel = outlines.models.openai(\n    \"gpt-4o-mini\",\n    api_key=\"your-api-key\"\n)\n\n# Note: Some features limited with API models\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n### 4. Pydantic Integration\n\nOutlines has first-class Pydantic support with automatic schema translation.\n\n#### Basic Models\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Article(BaseModel):\n    title: str = Field(description=\"Article title\")\n    author: str = Field(description=\"Author name\")\n    word_count: int = Field(description=\"Number of words\", gt=0)\n    tags: list[str] = Field(description=\"List of tags\")\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, Article)\n\narticle = generator(\"Generate article about AI\")\nprint(article.title)\nprint(article.word_count)  # Guaranteed > 0\n```\n\n#### Nested Models\n\n```python\nclass Address(BaseModel):\n    street: str\n    city: str\n    country: str\n\nclass Person(BaseModel):\n    name: str\n    age: int\n    address: Address  # Nested model\n\ngenerator = outlines.generate.json(model, Person)\nperson = generator(\"Generate person in New York\")\n\nprint(person.address.city)  # \"New York\"\n```\n\n#### Enums and Literals\n\n```python\nfrom enum import Enum\nfrom typing import Literal\n\nclass Status(str, Enum):\n    PENDING = \"pending\"\n    APPROVED = \"approved\"\n    REJECTED = \"rejected\"\n\nclass Application(BaseModel):\n    applicant: str\n    status: Status  # Must be one of enum values\n    priority: Literal[\"low\", \"medium\", \"high\"]  # Must be one of literals\n\ngenerator = outlines.generate.json(model, Application)\napp = generator(\"Generate application\")\n\nprint(app.status)  # Status.PENDING (or APPROVED\u002FREJECTED)\n```\n\n## Common Patterns\n\n### Pattern 1: Data Extraction\n\n```python\nfrom pydantic import BaseModel\nimport outlines\n\nclass CompanyInfo(BaseModel):\n    name: str\n    founded_year: int\n    industry: str\n    employees: int\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, CompanyInfo)\n\ntext = \"\"\"\nApple Inc. was founded in 1976 in the technology industry.\nThe company employs approximately 164,000 people worldwide.\n\"\"\"\n\nprompt = f\"Extract company information:\\n{text}\\n\\nCompany:\"\ncompany = generator(prompt)\n\nprint(f\"Name: {company.name}\")\nprint(f\"Founded: {company.founded_year}\")\nprint(f\"Industry: {company.industry}\")\nprint(f\"Employees: {company.employees}\")\n```\n\n### Pattern 2: Classification\n\n```python\nfrom typing import Literal\nimport outlines\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Binary classification\ngenerator = outlines.generate.choice(model, [\"spam\", \"not_spam\"])\nresult = generator(\"Email: Buy now! 50% off!\")\n\n# Multi-class classification\ncategories = [\"technology\", \"business\", \"sports\", \"entertainment\"]\ncategory_gen = outlines.generate.choice(model, categories)\ncategory = category_gen(\"Article: Apple announces new iPhone...\")\n\n# With confidence\nclass Classification(BaseModel):\n    label: Literal[\"positive\", \"negative\", \"neutral\"]\n    confidence: float\n\nclassifier = outlines.generate.json(model, Classification)\nresult = classifier(\"Review: This product is okay, nothing special\")\n```\n\n### Pattern 3: Structured Forms\n\n```python\nclass UserProfile(BaseModel):\n    full_name: str\n    age: int\n    email: str\n    phone: str\n    country: str\n    interests: list[str]\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, UserProfile)\n\nprompt = \"\"\"\nExtract user profile from:\nName: Alice Johnson\nAge: 28\nEmail: alice@example.com\nPhone: 555-0123\nCountry: USA\nInterests: hiking, photography, cooking\n\"\"\"\n\nprofile = generator(prompt)\nprint(profile.full_name)\nprint(profile.interests)  # [\"hiking\", \"photography\", \"cooking\"]\n```\n\n### Pattern 4: Multi-Entity Extraction\n\n```python\nclass Entity(BaseModel):\n    name: str\n    type: Literal[\"PERSON\", \"ORGANIZATION\", \"LOCATION\"]\n\nclass DocumentEntities(BaseModel):\n    entities: list[Entity]\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, DocumentEntities)\n\ntext = \"Tim Cook met with Satya Nadella at Microsoft headquarters in Redmond.\"\nprompt = f\"Extract entities from: {text}\"\n\nresult = generator(prompt)\nfor entity in result.entities:\n    print(f\"{entity.name} ({entity.type})\")\n```\n\n### Pattern 5: Code Generation\n\n```python\nclass PythonFunction(BaseModel):\n    function_name: str\n    parameters: list[str]\n    docstring: str\n    body: str\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, PythonFunction)\n\nprompt = \"Generate a Python function to calculate factorial\"\nfunc = generator(prompt)\n\nprint(f\"def {func.function_name}({', '.join(func.parameters)}):\")\nprint(f'    \"\"\"{func.docstring}\"\"\"')\nprint(f\"    {func.body}\")\n```\n\n### Pattern 6: Batch Processing\n\n```python\ndef batch_extract(texts: list[str], schema: type[BaseModel]):\n    \"\"\"Extract structured data from multiple texts.\"\"\"\n    model = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n    generator = outlines.generate.json(model, schema)\n\n    results = []\n    for text in texts:\n        result = generator(f\"Extract from: {text}\")\n        results.append(result)\n\n    return results\n\nclass Person(BaseModel):\n    name: str\n    age: int\n\ntexts = [\n    \"John is 30 years old\",\n    \"Alice is 25 years old\",\n    \"Bob is 40 years old\"\n]\n\npeople = batch_extract(texts, Person)\nfor person in people:\n    print(f\"{person.name}: {person.age}\")\n```\n\n## Backend Configuration\n\n### Transformers\n\n```python\nimport outlines\n\n# Basic usage\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# GPU configuration\nmodel = outlines.models.transformers(\n    \"microsoft\u002FPhi-3-mini-4k-instruct\",\n    device=\"cuda\",\n    model_kwargs={\"torch_dtype\": \"float16\"}\n)\n\n# Popular models\nmodel = outlines.models.transformers(\"meta-llama\u002FLlama-3.1-8B-Instruct\")\nmodel = outlines.models.transformers(\"mistralai\u002FMistral-7B-Instruct-v0.3\")\nmodel = outlines.models.transformers(\"Qwen\u002FQwen2.5-7B-Instruct\")\n```\n\n### llama.cpp\n\n```python\n# Load GGUF model\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fllama-3.1-8b.Q4_K_M.gguf\",\n    n_ctx=4096,         # Context window\n    n_gpu_layers=35,    # GPU layers\n    n_threads=8         # CPU threads\n)\n\n# Full GPU offload\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fmodel.gguf\",\n    n_gpu_layers=-1  # All layers on GPU\n)\n```\n\n### vLLM (Production)\n\n```python\n# Single GPU\nmodel = outlines.models.vllm(\"meta-llama\u002FLlama-3.1-8B-Instruct\")\n\n# Multi-GPU\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-70B-Instruct\",\n    tensor_parallel_size=4  # 4 GPUs\n)\n\n# With quantization\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    quantization=\"awq\"  # Or \"gptq\"\n)\n```\n\n## Best Practices\n\n### 1. Use Specific Types\n\n```python\n# ✅ Good: Specific types\nclass Product(BaseModel):\n    name: str\n    price: float  # Not str\n    quantity: int  # Not str\n    in_stock: bool  # Not str\n\n# ❌ Bad: Everything as string\nclass Product(BaseModel):\n    name: str\n    price: str  # Should be float\n    quantity: str  # Should be int\n```\n\n### 2. Add Constraints\n\n```python\nfrom pydantic import Field\n\n# ✅ Good: With constraints\nclass User(BaseModel):\n    name: str = Field(min_length=1, max_length=100)\n    age: int = Field(ge=0, le=120)\n    email: str = Field(pattern=r\"^[\\w\\.-]+@[\\w\\.-]+\\.\\w+$\")\n\n# ❌ Bad: No constraints\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n```\n\n### 3. Use Enums for Categories\n\n```python\n# ✅ Good: Enum for fixed set\nclass Priority(str, Enum):\n    LOW = \"low\"\n    MEDIUM = \"medium\"\n    HIGH = \"high\"\n\nclass Task(BaseModel):\n    title: str\n    priority: Priority\n\n# ❌ Bad: Free-form string\nclass Task(BaseModel):\n    title: str\n    priority: str  # Can be anything\n```\n\n### 4. Provide Context in Prompts\n\n```python\n# ✅ Good: Clear context\nprompt = \"\"\"\nExtract product information from the following text.\nText: iPhone 15 Pro costs $999 and is currently in stock.\nProduct:\n\"\"\"\n\n# ❌ Bad: Minimal context\nprompt = \"iPhone 15 Pro costs $999 and is currently in stock.\"\n```\n\n### 5. Handle Optional Fields\n\n```python\nfrom typing import Optional\n\n# ✅ Good: Optional fields for incomplete data\nclass Article(BaseModel):\n    title: str  # Required\n    author: Optional[str] = None  # Optional\n    date: Optional[str] = None  # Optional\n    tags: list[str] = []  # Default empty list\n\n# Can succeed even if author\u002Fdate missing\n```\n\n## Comparison to Alternatives\n\n| Feature | Outlines | Instructor | Guidance | LMQL |\n|---------|----------|------------|----------|------|\n| Pydantic Support | ✅ Native | ✅ Native | ❌ No | ❌ No |\n| JSON Schema | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes |\n| Regex Constraints | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |\n| Local Models | ✅ Full | ⚠️ Limited | ✅ Full | ✅ Full |\n| API Models | ⚠️ Limited | ✅ Full | ✅ Full | ✅ Full |\n| Zero Overhead | ✅ Yes | ❌ No | ⚠️ Partial | ✅ Yes |\n| Automatic Retrying | ❌ No | ✅ Yes | ❌ No | ❌ No |\n| Learning Curve | Low | Low | Low | High |\n\n**When to choose Outlines:**\n- Using local models (Transformers, llama.cpp, vLLM)\n- Need maximum inference speed\n- Want Pydantic model support\n- Require zero-overhead structured generation\n- Control token sampling process\n\n**When to choose alternatives:**\n- Instructor: Need API models with automatic retrying\n- Guidance: Need token healing and complex workflows\n- LMQL: Prefer declarative query syntax\n\n## Performance Characteristics\n\n**Speed:**\n- **Zero overhead**: Structured generation as fast as unconstrained\n- **Fast-forward optimization**: Skips deterministic tokens\n- **1.2-2x faster** than post-generation validation approaches\n\n**Memory:**\n- FSM compiled once per schema (cached)\n- Minimal runtime overhead\n- Efficient with vLLM for high throughput\n\n**Accuracy:**\n- **100% valid outputs** (guaranteed by FSM)\n- No retry loops needed\n- Deterministic token filtering\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Foutlines-dev.github.io\u002Foutlines\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Foutlines-dev\u002Foutlines (8k+ stars)\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FR9DSu34mGd\n- **Blog**: https:\u002F\u002Fblog.dottxt.co\n\n## See Also\n\n- `references\u002Fjson_generation.md` - Comprehensive JSON and Pydantic patterns\n- `references\u002Fbackends.md` - Backend-specific configuration\n- `references\u002Fexamples.md` - Production-ready examples\n\n\n","# Outlines: Structured Text Generation\n\n## When to Use This Skill\n\nUse Outlines when you need to:\n- **Guarantee valid JSON\u002FXML\u002Fcode** structure during generation\n- **Use Pydantic models** for type-safe outputs\n- **Support local models** (Transformers, llama.cpp, vLLM)\n- **Maximize inference speed** with zero-overhead structured generation\n- **Generate against JSON schemas** automatically\n- **Control token sampling** at the grammar level\n\n**GitHub Stars**: 8,000+ | **From**: dottxt.ai (formerly .txt)\n\n## Installation\n\n```bash\n# Base installation\npip install outlines\n\n# With specific backends\npip install outlines transformers  # Hugging Face models\npip install outlines llama-cpp-python  # llama.cpp\npip install outlines vllm  # vLLM for high-throughput\n```\n\n## Quick Start\n\n### Basic Example: Classification\n\n```python\nimport outlines\nfrom typing import Literal\n\n# Load model\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Generate with type constraint\nprompt = \"Sentiment of 'This product is amazing!': \"\ngenerator = outlines.generate.choice(model, [\"positive\", \"negative\", \"neutral\"])\nsentiment = generator(prompt)\n\nprint(sentiment)  # \"positive\" (guaranteed one of these)\n```\n\n### With Pydantic Models\n\n```python\nfrom pydantic import BaseModel\nimport outlines\n\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Generate structured output\nprompt = \"Extract user: John Doe, 30 years old, john@example.com\"\ngenerator = outlines.generate.json(model, User)\nuser = generator(prompt)\n\nprint(user.name)   # \"John Doe\"\nprint(user.age)    # 30\nprint(user.email)  # \"john@example.com\"\n```\n\n## Core Concepts\n\n### 1. Constrained Token Sampling\n\nOutlines uses Finite State Machines (FSM) to constrain token generation at the logit level.\n\n**How it works:**\n1. Convert schema (JSON\u002FPydantic\u002Fregex) to context-free grammar (CFG)\n2. Transform CFG into Finite State Machine (FSM)\n3. Filter invalid tokens at each step during generation\n4. Fast-forward when only one valid token exists\n\n**Benefits:**\n- **Zero overhead**: Filtering happens at token level\n- **Speed improvement**: Fast-forward through deterministic paths\n- **Guaranteed validity**: Invalid outputs impossible\n\n```python\nimport outlines\n\n# Pydantic model -> JSON schema -> CFG -> FSM\nclass Person(BaseModel):\n    name: str\n    age: int\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Behind the scenes:\n# 1. Person -> JSON schema\n# 2. JSON schema -> CFG\n# 3. CFG -> FSM\n# 4. FSM filters tokens during generation\n\ngenerator = outlines.generate.json(model, Person)\nresult = generator(\"Generate person: Alice, 25\")\n```\n\n### 2. Structured Generators\n\nOutlines provides specialized generators for different output types.\n\n#### Choice Generator\n\n```python\n# Multiple choice selection\ngenerator = outlines.generate.choice(\n    model,\n    [\"positive\", \"negative\", \"neutral\"]\n)\n\nsentiment = generator(\"Review: This is great!\")\n# Result: One of the three choices\n```\n\n#### JSON Generator\n\n```python\nfrom pydantic import BaseModel\n\nclass Product(BaseModel):\n    name: str\n    price: float\n    in_stock: bool\n\n# Generate valid JSON matching schema\ngenerator = outlines.generate.json(model, Product)\nproduct = generator(\"Extract: iPhone 15, $999, available\")\n\n# Guaranteed valid Product instance\nprint(type(product))  # \u003Cclass '__main__.Product'>\n```\n\n#### Regex Generator\n\n```python\n# Generate text matching regex\ngenerator = outlines.generate.regex(\n    model,\n    r\"[0-9]{3}-[0-9]{3}-[0-9]{4}\"  # Phone number pattern\n)\n\nphone = generator(\"Generate phone number:\")\n# Result: \"555-123-4567\" (guaranteed to match pattern)\n```\n\n#### Integer\u002FFloat Generators\n\n```python\n# Generate specific numeric types\nint_generator = outlines.generate.integer(model)\nage = int_generator(\"Person's age:\")  # Guaranteed integer\n\nfloat_generator = outlines.generate.float(model)\nprice = float_generator(\"Product price:\")  # Guaranteed float\n```\n\n### 3. Model Backends\n\nOutlines supports multiple local and API-based backends.\n\n#### Transformers (Hugging Face)\n\n```python\nimport outlines\n\n# Load from Hugging Face\nmodel = outlines.models.transformers(\n    \"microsoft\u002FPhi-3-mini-4k-instruct\",\n    device=\"cuda\"  # Or \"cpu\"\n)\n\n# Use with any generator\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### llama.cpp\n\n```python\n# Load GGUF model\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fllama-3.1-8b-instruct.Q4_K_M.gguf\",\n    n_gpu_layers=35\n)\n\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### vLLM (High Throughput)\n\n```python\n# For production deployments\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    tensor_parallel_size=2  # Multi-GPU\n)\n\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n#### OpenAI (Limited Support)\n\n```python\n# Basic OpenAI support\nmodel = outlines.models.openai(\n    \"gpt-4o-mini\",\n    api_key=\"your-api-key\"\n)\n\n# Note: Some features limited with API models\ngenerator = outlines.generate.json(model, YourModel)\n```\n\n### 4. Pydantic Integration\n\nOutlines has first-class Pydantic support with automatic schema translation.\n\n#### Basic Models\n\n```python\nfrom pydantic import BaseModel, Field\n\nclass Article(BaseModel):\n    title: str = Field(description=\"Article title\")\n    author: str = Field(description=\"Author name\")\n    word_count: int = Field(description=\"Number of words\", gt=0)\n    tags: list[str] = Field(description=\"List of tags\")\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, Article)\n\narticle = generator(\"Generate article about AI\")\nprint(article.title)\nprint(article.word_count)  # Guaranteed > 0\n```\n\n#### Nested Models\n\n```python\nclass Address(BaseModel):\n    street: str\n    city: str\n    country: str\n\nclass Person(BaseModel):\n    name: str\n    age: int\n    address: Address  # Nested model\n\ngenerator = outlines.generate.json(model, Person)\nperson = generator(\"Generate person in New York\")\n\nprint(person.address.city)  # \"New York\"\n```\n\n#### Enums and Literals\n\n```python\nfrom enum import Enum\nfrom typing import Literal\n\nclass Status(str, Enum):\n    PENDING = \"pending\"\n    APPROVED = \"approved\"\n    REJECTED = \"rejected\"\n\nclass Application(BaseModel):\n    applicant: str\n    status: Status  # Must be one of enum values\n    priority: Literal[\"low\", \"medium\", \"high\"]  # Must be one of literals\n\ngenerator = outlines.generate.json(model, Application)\napp = generator(\"Generate application\")\n\nprint(app.status)  # Status.PENDING (or APPROVED\u002FREJECTED)\n```\n\n## Common Patterns\n\n### Pattern 1: Data Extraction\n\n```python\nfrom pydantic import BaseModel\nimport outlines\n\nclass CompanyInfo(BaseModel):\n    name: str\n    founded_year: int\n    industry: str\n    employees: int\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, CompanyInfo)\n\ntext = \"\"\"\nApple Inc. was founded in 1976 in the technology industry.\nThe company employs approximately 164,000 people worldwide.\n\"\"\"\n\nprompt = f\"Extract company information:\\n{text}\\n\\nCompany:\"\ncompany = generator(prompt)\n\nprint(f\"Name: {company.name}\")\nprint(f\"Founded: {company.founded_year}\")\nprint(f\"Industry: {company.industry}\")\nprint(f\"Employees: {company.employees}\")\n```\n\n### Pattern 2: Classification\n\n```python\nfrom typing import Literal\nimport outlines\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# Binary classification\ngenerator = outlines.generate.choice(model, [\"spam\", \"not_spam\"])\nresult = generator(\"Email: Buy now! 50% off!\")\n\n# Multi-class classification\ncategories = [\"technology\", \"business\", \"sports\", \"entertainment\"]\ncategory_gen = outlines.generate.choice(model, categories)\ncategory = category_gen(\"Article: Apple announces new iPhone...\")\n\n# With confidence\nclass Classification(BaseModel):\n    label: Literal[\"positive\", \"negative\", \"neutral\"]\n    confidence: float\n\nclassifier = outlines.generate.json(model, Classification)\nresult = classifier(\"Review: This product is okay, nothing special\")\n```\n\n### Pattern 3: Structured Forms\n\n```python\nclass UserProfile(BaseModel):\n    full_name: str\n    age: int\n    email: str\n    phone: str\n    country: str\n    interests: list[str]\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, UserProfile)\n\nprompt = \"\"\"\nExtract user profile from:\nName: Alice Johnson\nAge: 28\nEmail: alice@example.com\nPhone: 555-0123\nCountry: USA\nInterests: hiking, photography, cooking\n\"\"\"\n\nprofile = generator(prompt)\nprint(profile.full_name)\nprint(profile.interests)  # [\"hiking\", \"photography\", \"cooking\"]\n```\n\n### Pattern 4: Multi-Entity Extraction\n\n```python\nclass Entity(BaseModel):\n    name: str\n    type: Literal[\"PERSON\", \"ORGANIZATION\", \"LOCATION\"]\n\nclass DocumentEntities(BaseModel):\n    entities: list[Entity]\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, DocumentEntities)\n\ntext = \"Tim Cook met with Satya Nadella at Microsoft headquarters in Redmond.\"\nprompt = f\"Extract entities from: {text}\"\n\nresult = generator(prompt)\nfor entity in result.entities:\n    print(f\"{entity.name} ({entity.type})\")\n```\n\n### Pattern 5: Code Generation\n\n```python\nclass PythonFunction(BaseModel):\n    function_name: str\n    parameters: list[str]\n    docstring: str\n    body: str\n\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\ngenerator = outlines.generate.json(model, PythonFunction)\n\nprompt = \"Generate a Python function to calculate factorial\"\nfunc = generator(prompt)\n\nprint(f\"def {func.function_name}({', '.join(func.parameters)}):\")\nprint(f'    \"\"\"{func.docstring}\"\"\"')\nprint(f\"    {func.body}\")\n```\n\n### Pattern 6: Batch Processing\n\n```python\ndef batch_extract(texts: list[str], schema: type[BaseModel]):\n    \"\"\"Extract structured data from multiple texts.\"\"\"\n    model = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n    generator = outlines.generate.json(model, schema)\n\n    results = []\n    for text in texts:\n        result = generator(f\"Extract from: {text}\")\n        results.append(result)\n\n    return results\n\nclass Person(BaseModel):\n    name: str\n    age: int\n\ntexts = [\n    \"John is 30 years old\",\n    \"Alice is 25 years old\",\n    \"Bob is 40 years old\"\n]\n\npeople = batch_extract(texts, Person)\nfor person in people:\n    print(f\"{person.name}: {person.age}\")\n```\n\n## Backend Configuration\n\n### Transformers\n\n```python\nimport outlines\n\n# Basic usage\nmodel = outlines.models.transformers(\"microsoft\u002FPhi-3-mini-4k-instruct\")\n\n# GPU configuration\nmodel = outlines.models.transformers(\n    \"microsoft\u002FPhi-3-mini-4k-instruct\",\n    device=\"cuda\",\n    model_kwargs={\"torch_dtype\": \"float16\"}\n)\n\n# Popular models\nmodel = outlines.models.transformers(\"meta-llama\u002FLlama-3.1-8B-Instruct\")\nmodel = outlines.models.transformers(\"mistralai\u002FMistral-7B-Instruct-v0.3\")\nmodel = outlines.models.transformers(\"Qwen\u002FQwen2.5-7B-Instruct\")\n```\n\n### llama.cpp\n\n```python\n# Load GGUF model\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fllama-3.1-8b.Q4_K_M.gguf\",\n    n_ctx=4096,         # Context window\n    n_gpu_layers=35,    # GPU layers\n    n_threads=8         # CPU threads\n)\n\n# Full GPU offload\nmodel = outlines.models.llamacpp(\n    \".\u002Fmodels\u002Fmodel.gguf\",\n    n_gpu_layers=-1  # All layers on GPU\n)\n```\n\n### vLLM (Production)\n\n```python\n# Single GPU\nmodel = outlines.models.vllm(\"meta-llama\u002FLlama-3.1-8B-Instruct\")\n\n# Multi-GPU\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-70B-Instruct\",\n    tensor_parallel_size=4  # 4 GPUs\n)\n\n# With quantization\nmodel = outlines.models.vllm(\n    \"meta-llama\u002FLlama-3.1-8B-Instruct\",\n    quantization=\"awq\"  # Or \"gptq\"\n)\n```\n\n## Best Practices\n\n### 1. Use Specific Types\n\n```python\n# ✅ Good: Specific types\nclass Product(BaseModel):\n    name: str\n    price: float  # Not str\n    quantity: int  # Not str\n    in_stock: bool  # Not str\n\n# ❌ Bad: Everything as string\nclass Product(BaseModel):\n    name: str\n    price: str  # Should be float\n    quantity: str  # Should be int\n```\n\n### 2. Add Constraints\n\n```python\nfrom pydantic import Field\n\n# ✅ Good: With constraints\nclass User(BaseModel):\n    name: str = Field(min_length=1, max_length=100)\n    age: int = Field(ge=0, le=120)\n    email: str = Field(pattern=r\"^[\\w\\.-]+@[\\w\\.-]+\\.\\w+$\")\n\n# ❌ Bad: No constraints\nclass User(BaseModel):\n    name: str\n    age: int\n    email: str\n```\n\n### 3. Use Enums for Categories\n\n```python\n# ✅ Good: Enum for fixed set\nclass Priority(str, Enum):\n    LOW = \"low\"\n    MEDIUM = \"medium\"\n    HIGH = \"high\"\n\nclass Task(BaseModel):\n    title: str\n    priority: Priority\n\n# ❌ Bad: Free-form string\nclass Task(BaseModel):\n    title: str\n    priority: str  # Can be anything\n```\n\n### 4. Provide Context in Prompts\n\n```python\n# ✅ Good: Clear context\nprompt = \"\"\"\nExtract product information from the following text.\nText: iPhone 15 Pro costs $999 and is currently in stock.\nProduct:\n\"\"\"\n\n# ❌ Bad: Minimal context\nprompt = \"iPhone 15 Pro costs $999 and is currently in stock.\"\n```\n\n### 5. Handle Optional Fields\n\n```python\nfrom typing import Optional\n\n# ✅ Good: Optional fields for incomplete data\nclass Article(BaseModel):\n    title: str  # Required\n    author: Optional[str] = None  # Optional\n    date: Optional[str] = None  # Optional\n    tags: list[str] = []  # Default empty list\n\n# Can succeed even if author\u002Fdate missing\n```\n\n## Comparison to Alternatives\n\n| Feature | Outlines | Instructor | Guidance | LMQL |\n|---------|----------|------------|----------|------|\n| Pydantic Support | ✅ Native | ✅ Native | ❌ No | ❌ No |\n| JSON Schema | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes |\n| Regex Constraints | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes |\n| Local Models | ✅ Full | ⚠️ Limited | ✅ Full | ✅ Full |\n| API Models | ⚠️ Limited | ✅ Full | ✅ Full | ✅ Full |\n| Zero Overhead | ✅ Yes | ❌ No | ⚠️ Partial | ✅ Yes |\n| Automatic Retrying | ❌ No | ✅ Yes | ❌ No | ❌ No |\n| Learning Curve | Low | Low | Low | High |\n\n**When to choose Outlines:**\n- Using local models (Transformers, llama.cpp, vLLM)\n- Need maximum inference speed\n- Want Pydantic model support\n- Require zero-overhead structured generation\n- Control token sampling process\n\n**When to choose alternatives:**\n- Instructor: Need API models with automatic retrying\n- Guidance: Need token healing and complex workflows\n- LMQL: Prefer declarative query syntax\n\n## Performance Characteristics\n\n**Speed:**\n- **Zero overhead**: Structured generation as fast as unconstrained\n- **Fast-forward optimization**: Skips deterministic tokens\n- **1.2-2x faster** than post-generation validation approaches\n\n**Memory:**\n- FSM compiled once per schema (cached)\n- Minimal runtime overhead\n- Efficient with vLLM for high throughput\n\n**Accuracy:**\n- **100% valid outputs** (guaranteed by FSM)\n- No retry loops needed\n- Deterministic token filtering\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Foutlines-dev.github.io\u002Foutlines\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Foutlines-dev\u002Foutlines (8k+ stars)\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002FR9DSu34mGd\n- **Blog**: https:\u002F\u002Fblog.dottxt.co\n\n## See Also\n\n- `references\u002Fjson_generation.md` - Comprehensive JSON and Pydantic patterns\n- `references\u002Fbackends.md` - Backend-specific configuration\n- `references\u002Fexamples.md` - Production-ready examples\n\n\n",{"name":5,"tags":49,"author":53,"license":15,"version":54,"description":7,"dependencies":55},[50,24,38,51,37,36,25,52,34,33],"Prompt Engineering","JSON Schema","vLLM","Orchestra Research","1.0.0",[5,56,57,58],"transformers","vllm","pydantic","main","7925716f065257435fdb12e97f7e52842147d5ad",".codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-code-templates\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude","https:\u002F\u002Fclaude.com\u002Fclaude-code","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho","Software · Applied AI · Quantitative Modeling · Building with Claude.","2026-06-10 12:24:46",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add henriquescastilho\u002Fmy-claude --skill outlines",{"tool":73,"command":74},"bunx","bunx skills add henriquescastilho\u002Fmy-claude --skill outlines",{"tool":76,"command":77},"pnpm","pnpm dlx skills add henriquescastilho\u002Fmy-claude --skill outlines","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-code-templates\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002FSKILL.md",true,[81,87,91],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fbackends.md","REFERENCE","参考文档",14407,"https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-code-templates\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002Freferences\u002Fbackends.md",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fexamples.md",21231,"https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-code-templates\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002Freferences\u002Fexamples.md",{"path":92,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fjson_generation.md",14927,"https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-code-templates\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fprompt-engineering-outlines\u002Freferences\u002Fjson_generation.md",[96,136,145,154,176,202],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":116,"publishedAt":44},"01M6F0B6XPQ65AX2X0DWK8KT3E","markitdown","Convert files and office documents to Markdown. 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Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fwhat-if-oracle\u002FSKILL.md",[182],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[184,185,186,187,188,189,190,191,192,193,194,128,129,195,196,197,198,199,200,201],"oracle","what if oracle","go","run","ux","futures","analysis","explore","decision","branch","question","idea","exploration","options","stress-test","strategies","scenario","stress",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":218,"publishedAt":44},"01M6EZZCXAEB49RXGQCJX5E8GB","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Fhenriquescastilho\u002Fmy-claude\u002Fblob\u002Fmain\u002F.codex\u002Fvendor_imports\u002Fclaude\u002Fmarketplaces\u002Fclaude-scientific-skills\u002Fscientific-skills\u002Fvenue-templates\u002FSKILL.md",[208,211,214,215],{"categoryNo":209,"categoryName":210,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":212,"categoryName":213,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":216,"categoryName":217,"taxonomyType":114,"taxonomyTypeDesc":115},"01JBSEEDCAT000000000000036","内容创作者",[219,220,221,222,223,224,225,226,125,227,128,129,130,228,229,230,231,232,175,233],"templates","venue templates","research","guidelines","NeurIPS","ICML","LaTeX","science","access","proposals","Academic","requirements","formatting","journal","chi"]