Bespoke Labs
  • Welcome
  • BESPOKE CURATOR
    • Getting Started
      • Quick Tour
      • Key Concepts
      • Visualize your dataset with the Bespoke Curator Viewer
      • Automatic recovery and caching
      • Structured Output
    • Save $$$ on LLM inference
      • Using OpenAI for batch inference
      • Using Anthropic for batch inference
      • Using Gemini for batch inference
      • Using Mistral for batch inference
      • Using kluster.ai for batch inference
    • How-to Guides
      • Using vLLM with Curator
      • Using Ollama with Curator
      • Using LiteLLM with curator
      • Handling Multimodal Data in Curator
      • Executing LLM-generated code
      • Using HuggingFace inference providers with Curator
    • Data Curation Recipes
      • Generating a diverse QA dataset
      • Using SimpleStrat block for generating diverse data
      • Curate Reasoning data with Claude-3.7 Sonnet
      • Synthetic Data for function calling
    • Finetuning Examples
      • Aspect based sentiment analysis
      • Finetuning a model to identify features of a product
    • API Reference
  • Models
    • Bespoke MiniCheck
      • Self-Hosting
      • Integrations
      • API Service
    • Bespoke MiniChart
    • OpenThinker
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  1. Models

Bespoke MiniCheck

A state-of-the-art grounded factuality model

“Hallucination-free answers demand verifiable grounding. Bespoke-MiniCheck makes that easy.”

What is grounded factuality?

Grounded factuality (a.k.a. textual entailment) measures whether a claim is supported, refuted, or not verifiable given an explicit context document. The metric is critical for Retrieval-Augmented Generation (RAG): if a claim is not grounded in the retrieved context, the model has hallucinated.

Why Bespoke-MiniCheck?

  • Best-in-class accuracy – Tops the public LLM-AggreFact leaderboard at 77.4 %, surpassing models that are many times larger.

  • Fast – ~200 ms end-to-end latency on a single modern GPU; < 100 ms with optional optimisations.

  • Lightweight – Runs comfortably on consumer laptops (MacBook-class hardware).

  • Easy to integrate – Drop-in HuggingFace model with a single probability output: support score.

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Last updated 19 days ago