Off the scale: 744 "arc-c" (747, BF16) AND 15 AI CONSTRUCTS: Each a precision engineered "consciousness" to help you in every task, using fewer tokens and
generating better answers. This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API).
Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases.
The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama, ...
including our strongest fine tunes too). The "rollout" is now in progress...
Qwen3.8-27B-Jumbo-Fusion-IQ-747-900-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF
Burning down the house with INSANE benchmarks and Turbo Brilliance...
This is the first fine tune HIT 747 "arc-c" (156 pts higher than Qwen 3.8 27B in BF16) AND 900 ARC-E (The OpenAI, Claude and Gemini "zone of intelligence")
with 8 bit at 744 arc-c and 742 arc-c in 4 bit (161 pts above Qwen 3.8 27B) AND will be powered up even further with Turbo Brilliance 50X.
Preview the "regular" Qwen 3.8 27B with Turbo Brilliance 50x right now, while we prep the "Jumbo":
https://huggingface.co/DavidAU/Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF
[coming soon: prepping for release/GGUFs etc and uploading ... ]
BENCHMARKS by Nightmedia
arc/c arc/e boolq hswag obkqa piqa wino
Qwen3.8-27B-Jumbo-Fusion-IQ-747-900-Turbo-Brilliance-50X-Reasoning-Instruct
bf16 0.747,0.901,0.915,...
mxfp8 0.744,0.902,0.915,...
mxfp4 0.742,0.897,0.911,...
[QWENS] [base, non heretic, untuned]
Qwen3.8-27B:
mxfp8 0.591,0.782,0.896,0.746,0.448,0.801,0.711
mxfp4 0.581,0.771,0.889,0.738,0.442,0.798,0.713
Qwen3.6-27B:
mxfp8 0.647,0.803,0.910,0.773,0.450,0.806,0.742
Qwen3.6-35B-A3B-Instruct
mxfp8 0.581,0.757,0.892,0.751,0.428,0.803,0.688
Qwen3.5-27B:
mxfp8 0.557,0.711,0.868,0.533,0.452,0.706,0.695
NOTES:
- Models are tested in "Instruct" mode because this generally works better with the testing harness.
- Testing via "thinking" mode also shows the metrics (and changes) but not the true extent.
- In actual fact when the model IS in thinking mode, it will exceed INSTRUCT benchmark scores in most cases.
- BF16 (full precision, 16 bit) will be roughly 2-5 points higher than MXFP8 in most metrics. Some metrics may be slightly higher than this.
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