Qwen3-4B-Hivemind-Instruct-NEO-MAX-Imatrix-GGUF
The Storm is coming...
256k context, off the scale power.
NEO Imatrix with MAX quants (16 bit OT all quants).
This is the one that will make closed source sweat.
This is the one that will make them all sweat.
This is a general purpose model.
BENCHMARKS:
MODEL arc_challenge,arc_easy,boolq,hellaswag,openbookqa,piqa,winogrande
Our 4B Instruct Model 0.613,0.842,0.855,0.748,0.428,0.781,0.709
Qwen3-30B-A3B-Thinking-2507 0.421,0.448,0.682,0.635,0.402,0.771,0.669
WANT POWER (4B/6B/8B) without "the nanny" ?
Special Thanks:
This was a Colab project between Nightmedia and DavidAU.
Nightmedia:
https://huggingface.co/nightmedia
Thanks to the following model makers/tuners (models used in this project):
https://huggingface.co/Gen-Verse/Qwen3-4B-RA-SFT
https://huggingface.co/TeichAI/Qwen3-4B-Instruct-2507-Polaris-Alpha-Distill
https://huggingface.co/TeichAI/Qwen3-4B-Thinking-2507-Gemini-2.5-Flash-Distill
And of course team Qwen:
Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:
In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;
Set the "Smoothing_factor" to 1.5
: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"
: in text-generation-webui -> parameters -> lower right.
: In Silly Tavern this is called: "Smoothing"
NOTE: For "text-generation-webui"
-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)
Source versions (and config files) of my models are here:
OTHER OPTIONS:
Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.
Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers
This a "Class 1" model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here: