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Tri-21B-Think model achieves strong benchmark performance

By PulseAugur Editorial · [1 sources] ·

The Tri-21B-Think model, a 21 billion parameter language model, has achieved notable performance on independent benchmarks. It scored 60.1% on the GPQA benchmark and 54.6% on IFBench. These results indicate that the model performs comparably to significantly larger language models. AI

IMPACT Demonstrates that smaller models can achieve competitive performance on complex benchmarks, potentially reducing computational costs for AI applications.

RANK_REASON Independent benchmark results for an open-source model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — mastodon.social →

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

Tri-21B-Think model achieves strong benchmark performance

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12 / 100
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Independent benchmark results for an open-source model. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · opensourceaitech ·

    Tri-21B-Think hits 60.1% GPQA and 54.6% IFBench in independent testing — a 21B model performing like much larger ones. 📊 Full breakdown on olud.ai https:// olud

    Tri-21B-Think hits 60.1% GPQA and 54.6% IFBench in independent testing — a 21B model performing like much larger ones. 📊 Full breakdown on olud.ai https:// olud.ai/leaderboard.html # LLM # Benchmarks # OpenSource # AI

    LINKS olud.ai/leaderboard.html

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