 | Main reason is, that qunatization quality directly affects models performance and stability and this results in real usefullness. Even though GRM-2.6-Plus is in benchmarks better than qwen3.6 27b model from which it derives, it gives worse results than autoround Q2_K_mixed quant of qwen3.6 27b which is practically same in size. This is just one example, most of the quants i tested suffer from same problems and only few of them mostly with different quantization mechanism are usefull below Q5. I want to advocate for autoround quantization as standard for lower quants Q1-Q4, also apex was performing quite well, but size is larger, maybe you know of other alternative methods that give consistent results, because standard quants like Q4_K_M dont provide adequate results and often results in bugged behavior overall (looping, halucinations, inconsistency). Prompt: Create svg image of a pelican riding a bicycle Multiple examples of different quant results https://www.reddit.com/r/LocalLLaMA/comments/1szp96f/comment/oj3r4b1/ Autoround Q2_K_Mixed https://huggingface.co/sphaela/Qwen3.6-27B-AutoRound-GGUF Regular llama.cpp Q4_K_M https://huggingface.co/morikomorizz/GRM-2.6-Plus-GGUF This is just one example and the output quality is consistently worse, when i ask it tricky questions, how much it hallucinates, loops etc. Community should understand, that typical quantization under Q5-6 is inadequate for qwen models unless you tinker with it through some more intelligent mechanism like intel autoround does. Looping from my experience is for example direct symptom of broken quantization, occasional syntactic errors in agentic coding another. [link] [comments] |
Llama.cpp quantization is broken
Reddit r/LocalLLaMA / 5/4/2026
💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisTools & Practical Usage
Key Points
- The post argues that quantization quality in llama.cpp heavily impacts real-world model performance and stability, not just benchmark numbers.
- It claims that several standard low-bit quantizations (roughly Q1–Q4, and even some Q4 variants) produce consistently worse outputs, including hallucinations, looping, and other “bugged” behaviors.
- As an example, it compares GRM-2.6-Plus with an AutoRound-derived Q2_K_Mixed version of Qwen3.6-27B, stating that AutoRound performs better in practice despite similar size.
- The author advocates adopting AutoRound quantization as the default approach for lower quants, and suggests that approaches like Intel AutoRound (and possibly alternatives) yield more consistent results.
- They note that many Q5-6 quantizations may also be inadequate for Qwen models unless more intelligent quantization mechanisms are used, and they request other methods that maintain consistent behavior.
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