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Structured Generation Backends

Outlines relies on a structured generation backend to control text generation for steerable models such thah they conform to the output type provided. One of those backends is of course outlines-core, but you also have access to two other libraries that fulfill the same purpose: llguidance and xgrammar.

Overview

To select the backend to use for your generation, provide a value for the backend argument when calling a model or a generator.

For instance:

from typing import Literal
import outlines
from transformers import AutoModelForCausalLM, AutoTokenizer

output_type = Literal["Paris", "London", "Rome", "Berlin"]

model = outlines.from_transformers(
    AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct"),
    AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
)

result = model("What is the capital of France?", output_type, backend="llguidance")
print(result) # 'Paris'

generator = outlines.Generaor(model, output_type)
result = generator("What is the capital of France?", backend="xgrammar")
print(result) # 'Paris'

If you do not provide a value for the backend argument, the default value will be used. The default value depends on the type of output type:

  • JSON schema: outlines_core
  • Regex: outlines_core
  • Context-free grammar: llguidance
  • Interegular FSM: outlines_core

Features matrix

As mentioned previously, selecting the structured generation backend is only applicable to steerable models, so Transformers, LlmaCpp and MLXLM. Additionaly, some backends do not support some models within those or some output types.

outlines_core llguidance xgrammar
Models
Transformers ✅ ✅ ✅
LlamaCpp ✅ ✅ ❌
MLXLM ✅ ✅ ❌
Output Types
JSON Schema ✅ ✅ ✅
Regex ✅ ✅ ✅
Grammar ✅ ✅ ✅
FSM ✅ ❌ ❌