SourceReddit r/LocalLLaMA•Stalecollected in 45m
GGUF Quants MMLU Benchmarks Revealed
#benchmarks#quantization#mmlu-scoresgguf-quantsqwen3.5-27bggufllama.cppmmlu
💡87% MMLU scores from Qwen GGUF quants on 24GB VRAM setups
⚡ 30-Second TL;DR
What Changed
Qwen3.5-27B-UD-Q5_K_XL.gguf: 87.33% (12263/14042)
Why It Matters
Provides quantized model rankings for high-end local inference, aiding selection of efficient LLMs without sacrificing much accuracy.
What To Do Next
Download top Qwen3.5-27B-UD-Q5_K_XL.gguf and benchmark on your MMLU setup.
Who should care:Researchers & Academics
Key Points
- •Qwen3.5-27B-UD-Q5_K_XL.gguf: 87.33% (12263/14042)
- •Qwen3.5-27B-UD-Q4_K_XL.gguf: 87.25% (12252/14042)
- •Tested with ctx 8192, seed 42, fa on in llama.cpp
- •Hardware: 24GB VRAM + 128GB RAM; MMLU DEV+TEST subset
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Original source: Reddit r/LocalLLaMA ↗
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