outlines

Guarantees valid JSON/XML/code structure with Pydantic models. Supports local models and fast inference via structured generation.

• Structured text generation with type-safe outputs using Pydantic
• Supports local models (Transformers, llama.cpp, vLLM)
• Enables constrained token sampling via finite state machines
• Provides generators for choice, JSON, regex, integers, and floats

• Validates output structure via CFG/FSM at token level
• Zero-overhead filtering during generation
• Fast-forward on deterministic paths for speed
• Automatic schema-to-grammar conversion
• Supports multiple backends: Hugging Face, llama.cpp, vLLM
• Pydantic-first with automatic schema translation
• Guarantees valid output without post-processing

1. 1.Install outlines via pip with desired backend (e.g., transformers, llama.cpp, vLLM)
2. 2.Define Pydantic models for structured output
3. 3.Use generate.choice, generate.json, generate.regex, etc. with model and schema
4. 4.Choose appropriate backend based on environment (local vs. cloud)
5. 5.Leverage type constraints to enforce valid output formats