Supposedly, you can increase output quality/"intelligence" of Qwen Flash Next quants somewhat (using Strata) without increase in VRAM, system memory usage or sacrificing prefill/decode speed (when using ple-io=direct)by side-loading a higher quality ngram, in this case Q8. You do this with the following option: --ple-gguf=/path/to/ngram.gguf or, if running Strata in Docker, by adding this envar to the compose file: STRATA_SET: "ple-gguf=/path/to/ngram.gguf" However, if you just feed Strata the original ngram gguf straight from hugging face (in my case unsloth's Q8 ngram while using their IQ4_XS quant), it will error out complaining about architecture mismatch. To fix this, you need to make a simple metadata change just to the ngram gguf file. Either download a pre-edited gguf: https://ciscai-gguf-editor.hf.space/download/unsloth/Qwen3.8-Flash-Next-GGUF/Q8_0/Qwen3.8-Flash-Next-Q8_0-00003-of-00006.gguf?add=%5B%22general.architecture%22,8,%22strata-ple%22%5D or, using gguf-editor-gui from gguf python package, add general.architecture key with STRING type and a value of strata-ple. It will bypass Strata's (unnecessary IMO) checks and successfully load the ngram. I must make a disclaimer that I have not verified whether this actually makes the model smarter myself nor were my speed/resource usage impact checks thorough, but the logic behind using a higher precision ngram seems reasonable, and if you can spare the storage space it seems like a no-brainer decision. You can do the same with an even larger BF16 ngram, but from what I've heard the difference is basically not perceivable and it's not worth wasting 50GBs more of storage.   submitted by   /u/citrusalex [link]   [comments]