Constrain LLM token sampling to JSON schemas, regex patterns, or Pydantic models.
npx skills add https://github.com/heysuhas/hermes_cli --skill outlines-heysuhas
Please help me install this Agent Skill. Skill: outlines Source: https://github.com/heysuhas/hermes_cli/tree/main/optional-skills/mlops/inference/outlines Command: npx skills add https://github.com/heysuhas/hermes_cli --skill outlines-heysuhas
This Skill solves the common issue of LLMs generating malformed or unpredictable text when you require strict, machine-readable formats like JSON or specific regex patterns.
Use the outlines skill to generate a structured JSON response matching the User Pydantic model for the provided text input.
High-intent search queries and answers about installing and using this skill.
To guarantee valid JSON output from an LLM, you constrain token sampling at the logit level using Finite State Machines. This enforces structured text generation based on specific JSON schemas, ensuring 100% valid output without post-generation validation loops.
You force an LLM to match a Pydantic model by applying grammar-based control during inference. This constrains the model's token sampling to adhere strictly to the defined Pydantic models, preventing invalid tokens before they are generated.
Yes, structured generation works with vLLM and Transformers for high-throughput inference. It supports these production-grade backends alongside llama.cpp to enforce JSON schemas and regex patterns during large-scale data extraction tasks.
The best way to prevent malformed text in LLM data extraction pipelines is zero-overhead grammar-based generation. By constraining token sampling to specific formats, it eliminates unpredictable text and removes the need for complex retry logic.
An LLM generates invalid JSON schema structures because standard token sampling allows unpredictable text. Implementing grammar-based control with Finite State Machines prevents invalid tokens before generation, ensuring 100% valid structured output.
zangjeicyConstrain LLM token sampling to JSON schemas, regex patterns, or Pydantic models.
matthew-johnsonGenerate JSON, Pydantic models, and regex via CFG/FSM constrained decoding.
cxnaiveEnforce structured LLM text generation via grammar-based token sampling and Pydantic validation.
InverterNetworkConstrains LLM token sampling to generate structured output via JSON, regex, or Pydantic models.
