Constrain LLM token sampling to JSON schemas, regex patterns, or Pydantic models.
npx skills add https://github.com/zangjeicy/Hermes --skill outlines-zangjeicy
Please help me install this Agent Skill. Skill: outlines Source: https://github.com/zangjeicy/Hermes/tree/main/optional-skills/mlops/inference/outlines Command: npx skills add https://github.com/zangjeicy/Hermes --skill outlines-zangjeicy
This skill solves the common issue of LLMs producing inconsistent or invalid output formats, ensuring that generated text strictly adheres to required JSON, regex, or Pydantic schemas.
Use the outlines skill to generate a structured JSON object from the provided text using the User Pydantic model.
High-intent search queries and answers about installing and using this skill.
To guarantee structured JSON output, you must constrain LLM token sampling using Finite State Machines. This filters logits at the generation level, making invalid JSON or schema violations impossible during inference.
Structured generation enforces Pydantic models by applying grammar-based constraints via Finite State Machines. It filters tokens at the logit level during generation, ensuring the output strictly matches your defined data structures.
Yes, structured generation supports high-throughput inference backends including vLLM, Transformers, and llama.cpp. This ensures zero-overhead, type-safe, and deterministic output generation in local model environments.
The best way to extract data using regex patterns is to constrain token sampling to specific regex schemas. This enforces deterministic output generation, ensuring the LLM returns valid text matching your exact pattern requirements.
Yes, you can use Pydantic models to enforce strict data structures during the token generation process. This guarantees the LLM returns a valid JSON object matching your schema for downstream database insertion.
Constraining token sampling requires compatible local model environments and inference backends like vLLM or Transformers. It is strictly for type-safe, deterministic output generation and requires predefined JSON, regex, or Pydantic schemas.
VYRE-StudiosConstrain token generation to produce valid JSON, XML, or code structures.
promptranksExport validated PSV samples to SQL or JSON for database import.
RheasilviaConstrain token sampling to match JSON schemas, regex patterns, or Pydantic models.
iani-kuliEnforce strict structural constraints on LLM text generation using Finite State Machines and Pydantic models.
