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Open LLMs on-prem Text-to-SQL: Qwen2.5 and Llama-3.x lead, generation beats size

A new benchmark study evaluated the performance of on-premises open LLMs on Text-to-SQL tasks, comparing different model families and sizes. The research found that newer generations of models, such as Qwen2.5-Coder and Llama-3.x, significantly outperform older models like CodeLlama at matched sizes. The study also highlighted that self-correction techniques offer a substantial improvement with minimal computational cost, while schema linking and self-consistency methods showed limited benefits. AI

IMPACT New benchmarks suggest newer model generations and specific techniques like self-correction are key for effective on-premises Text-to-SQL deployments.

RANK_REASON Academic paper evaluating LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

Open LLMs on-prem Text-to-SQL: Qwen2.5 and Llama-3.x lead, generation beats size

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Academic paper evaluating LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    How Far Do On-Prem Open LLMs Get on Text-to-SQL? A Cross-Family Size x Technique Frontier on BIRD

    Organizations that cannot send data to a cloud API increasingly ask: how good is Text-to-SQL if the model must run on-premises on open weights, and which popular accuracy "recipes" are worth their compute? We answer with an honest, fully reproducible benchmark on the BIRD develop…