claudegoodies
Skill

outlines

From NousResearch

Outlines: structured JSON/regex/Pydantic LLM generation.

Facts

Repository
NousResearch/hermes-agent
Status
Actively maintained
Last commit
Source file
optional-skills/mlops/inference/outlines/SKILL.md

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The instructions Claude Code reads when this skill runs.

# 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.transforme
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