| I am already tired of this (unsloth and others) approach of "let's be the first cause we know we have people starving for new models" while otherwise never caring to prove - like most of the other quants creators - if their quants are any good like checking PPL for catastrophic faults like "NaN" and/or measure and publish PPL and KLD figures. Latest proof of this rush is their "UD-Q4_K_XL" of MiniMax-M2.7-GGUF where a simple PPL measuring shows the model to be broken. For the people asking what is "NaN" in quant PPL measurement that would normally point out the existence of numerical issues with the backend kernels or the quant itself, it's about a rushed in / never checked quant error. I have checked similar quants from other HF providers (aessedai/MiniMax-M2.7-Q5_K_M --> 157.226 GiB (5.906 BPW) and ubergarm/MiniMax-M2.7-IQ5_K --> 157.771 GiB (5.926 BPW)) and no such error is present But this is not about backend kernels, nor about unsloth much-hyped "poisoned CUDA 13.2". There are ways to avoid these before publishing quants in a rush (like " Please Unsloth, get in line with QA and abide by the already accepted "GGUF quanting community" on HF and transparently provide PPL and KLD data. At least do it internally as a hygene measure to avoid such flops. Rush it not!
VS
[link] [comments] |
unsloth - MiniMax-M2.7-GGUF in BROKEN (UD-Q4_K_XL) --> avoid usage
Reddit r/LocalLLaMA / 4/13/2026
💬 OpinionSignals & Early TrendsTools & Practical Usage
Key Points
- Reddit投稿者は、unslothが公開した「MiniMax-M2.7-GGUF(UD-Q4_K_XL)」がPPL測定で壊れている(broken)可能性が高いと主張している。
- 投稿者は、NaNを示すような数値的問題は量子化やバックエンドカーネルの“急ぎで未検証の誤り”を示唆するため、公開前に検証すべきだと批判している。
- 同等モデルの他HFプロバイダ(aessedai/MiniMax-M2.7-Q5_K_M、ubergarm/MiniMax-M2.7-IQ5_K)では同種のエラーが見られなかったと比較している。
- 「--validate-quants」等で検証する手順や、GGUF quantingコミュニティで受け入れられているPPL/KLDの透明な提示を求めている。
- 投稿者はunslothの“poisoned CUDA”のような話題よりも、QA(品質保証)と公開前チェックの不足が問題だと位置づけている。
Related Articles

Black Hat USA
AI Business

Black Hat Asia
AI Business

Agentic coding at enterprise scale demands spec-driven development
VentureBeat

How to build effective reward functions with AWS Lambda for Amazon Nova model customization
Amazon AWS AI Blog

How 25 Students Went from Idea to Deployed App in 2 Hours with Google Antigravity
Dev.to