outlines

Kheish Compatibility

This skill is repo-local and stays inactive until explicitly activated.

When the original instructions refer to legacy tool names, use these Kheish mappings:

• terminal => bash
• web_extract => web_fetch, plus web_search when discovery is needed
• search_files => grep_search and glob_search
• browser_* tools require a browser-capable surfaced tool or MCP; if none is available, use the closest available surface and say so explicitly

When the instructions mention local helper files, resolve them from ${KHEISH_SKILL_DIR}.

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

# 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

# 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

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, [email protected]"
generator = outlines.generate.json(model, User)
user = generator(prompt)

print(user.name)   # "John Doe"
print(user.age)    # 30
print(user.email)  # "[email protected]"