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Quantiles enables no-code AI evaluations with Hugging Face datasets

Quantiles has introduced a new feature allowing users to create AI evaluations without writing code, utilizing a configuration-based approach. This system supports deterministic scoring styles like exact match and multiple choice, enabling users to build evaluations using datasets from sources such as Hugging Face. The process involves defining the evaluation type, dataset, prompt template, and style, with an example provided for the MMLU-Pro dataset. AI

IMPACT Simplifies the process of creating and managing AI evaluations, potentially lowering the barrier to entry for benchmarking.

RANK_REASON The item describes a new feature for an existing product that simplifies AI evaluation, rather than a core AI model release or significant industry-wide event.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quantiles enables no-code AI evaluations with Hugging Face datasets

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  1. dev.to — LLM tag TIER_1 English(EN) · Quantiles.io ·

    Build an AI Evaluation from a Hugging Face Dataset Without Writing Python

    <p>AI benchmarks and evaluations have varying datasets, prompts, measurement techniques, and more, but core execution logic rarely changes. In most cases, maintaining multiple custom implementations of the same evaluation pattern increases complexity and maintenance overhead.</p>…