Constrain token generation to enforce JSON, XML, and code schema compliance.
npx skills add https://github.com/graniet/kheish --skill outlines-graniet
Please help me install this Agent Skill. Skill: outlines Source: https://github.com/graniet/kheish/tree/main/skills/mlops/inference/outlines Command: npx skills add https://github.com/graniet/kheish --skill outlines-graniet
Generating structured data like JSON, XML, or code from large language models often produces invalid, malformed, or schema-non-compliant outputs that cause failed integrations, require endless retry loops, and demand hours of manual post-processing to correct.
Use the outlines skill to extract a customer's full name, email address, and support ticket category from the unstructured support message provided and return the data as a valid Pydantic SupportTicket model instance.
High-intent search queries and answers about installing and using this skill.
You can guarantee valid JSON schema outputs by enforcing schema constraints directly at the token level using finite state machines. This ensures all generated text from local LLMs perfectly matches your Pydantic models, eliminating malformed results without post-processing.
The best way to generate structured data using vLLM is applying grammar-based token sampling control. This constrains model outputs during inference, allowing you to extract type-safe data like customer support tickets directly into valid schemas with zero overhead.
Grammar-based generation does not slow down inference speed on local model backends. It applies zero-overhead logit-level constraints during token sampling, ensuring production-grade performance while maintaining guaranteed schema compliance across Transformers, llama.cpp, and vLLM.
Yes, you can use Pydantic models to enforce constraints on llama.cpp generated outputs. The system natively integrates with local backends like llama.cpp to map Pydantic models into finite state machines for reliable, syntax-constrained text generation.
To extract type-safe API request payloads from unstructured documents using Transformers, apply schema constraints during generation. This maps your desired payload structure into a finite state machine, guaranteeing the model produces syntactically correct and schema-compliant data.
Large language models produce invalid JSON when parsing complex schemas because standard token sampling lacks structural constraints. Without grammar-based generation, the model freely samples tokens that violate schema rules, causing failed integrations and requiring endless retry loops.
InverterNetworkConstrains LLM token sampling to generate structured output via JSON, regex, or Pydantic models.
yash-gargCreate Flutter model classes with fromJson and toJson methods using dart:convert.
ovachieverConstrain LLM outputs to valid JSON, XML, and code using regex and grammar constraints.
PlaidmustacheConstrain LLM outputs to JSON, Pydantic schemas, or regex using finite-state machines.
