Nick Pario

Full 27B-class reasoning in ternary transformer weights, for llama.cpp (CUDA, Metal, CPU) - \~5.9 GB language model (down from \~54 GB FP16) — full 27B-class reasoning on a standard laptop or a single GPU - 98.2% of FP16 intelligence retained: 84.78 average across 14 thinking-mode benchmarks — far above the conventional IQ2XXS build (72.59) at less than two-thirds of its footprint, and within 0.4 points of UD-Q4KXL at three times the footprint - Retains thinking, reasoning, and agentic behavior deep in the sub-4-bit regime, where conventional low-bit representations collapse: math within half a point of full precision (96.57), coding level with the baseline (89.42), agentic tool calling at…

This repo contains some APEX quantizations of Naphula/Goetia-26B-A4B-v1.7, created using bartowski's imatrix found here. As explained by its creators, APEX is "a novel MoE-aware mixed-precision quantization technique for llama.cpp". I suggest reading their information on the how and why of the method. Quantized using llama.cpp #1692f9e50 For roleplaying, I personally strongly recommend nohurry's SillyTavern presets for Gemma 4 models. My only contribution is compute. This is neither my merge, nor my quantization methodology. Have fun (within the constraints of the license). Google - the base model. Naphula - the Mergelord. Everyone whose finetunes were included in the merge! mudler - for…