React-like Declarative DSL for building synthetic LLM datasets
We were frustrated with dataset generation DX: ad‑hoc scripts and JSON templates break as flows branch, tool‑calls drift, and reproducibility is hard. So we built Torque: a schema‑first, declarative, fully typesafe DSL to generate conversational datasets. What it is: Declarative DSL - Compose conversations like React components Fully Typesafe - Zod schemas with complete type inference Provider Agnostic - Generate with any AI SDK provider (OpenAI, Anthropic, DeepSeek, vLLM, LLaMA.cpp etc.) AI-Powered Content - Generate realistic varied datasets automatically without complicated scripts Faker…
React-like Declarative DSL for building synthetic LLM datasets launched on November 3, 2025 with 10 votes, #1,014 of 9,795 launches that month and more than 75% of that year's launches.
In plain words
React-like Declarative DSL for building synthetic LLM datasets is a tool that helps developers create synthetic datasets for training large language models using a declarative syntax similar to React. It simplifies the process of generating training data by providing a familiar component-based approach. The tool is designed for machine learning engineers and researchers who need to build and manage LLM training datasets efficiently without writing complex procedural code.
written from the facts on this page · September 2026
From the sources
Torque is a Declarative, typesafe DSL for building synthetic LLM datasets — compose conversations like React components - qforge-dev/torque
In the maker’s words, at launch
We were frustrated with dataset generation DX: ad‑hoc scripts and JSON templates break as flows branch, tool‑calls drift, and reproducibility is hard. So we built Torque: a schema‑first, declarative, fully typesafe DSL to generate conversational datasets. What it is: Declarative DSL - Compose conversations like React…
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