Bugtrace Apex G4 26B APEX

Variants

Description

The Apex Predator of Offensive Security Reasoning.

BugTraceAI-CORE-G4-Apex is a high-performance, uncensored 26B Mixture-of-Experts (MoE) model based on Gemma 4 architecture. It has been meticulously fine-tuned via DPO (Direct Preference Optimization) on a curated "Super Dataset" comprising elite Bug Bounty reports, advanced malware methodologies, and multi-layer WAF evasion techniques.

Unlike standard security models, the Apex variant features an injected Opus-style reasoning engine, forcing the model to perform a deep step-by-step analysis inside a block before providing technical payloads or remediation strategies.

A novel MoE-aware mixed-precision quantization technique for llama.cpp

Beats Q8_0 perplexity at half the size -- and even beats F16. APEX outperforms Unsloth Dynamic 2.0 (UD) quantizations on perplexity, HellaSwag, and inference speed while being 2x smaller: APEX I-Quality (21.3 GB) achieves PPL 6.552 and 83.5% HellaSwag vs Unsloth UD-Q8_K_XL (45.3 GB) at PPL 6.536 and 82.5% HellaSwag. At the consumer tier, APEX Mini (12.2 GB) beats bartowski IQ2_M on every metric.

APEX assigns quantization precision per tensor type and per layer, exploiting the structural sparsity of Mixture-of-Experts models to achieve lossless compression that uniform quantization cannot. Five tiers from 21.3 GB (I-Quality) to 12.2 GB (Mini) cover every deployment scenario from maximum accuracy to consumer GPU inference. I-variants use a diverse imatrix (chat, code, reasoning, tool-calling -- no Wikipedia) that trades tiny perplexity increases for significant accuracy gains and lower KL divergence.

Process & Notes

• Built from BugTraceAI/BugTraceAI-Apex-G4-26B-Master-f16 with llama-quantize (b9840).
• Uses mudler/LocalAI's APEX Quantization strategy for Gemma 4.
• Importance matrix used for i-variants originates from mudler's own imatrix.dat from the mudler/gemma-4-26B-A4B-it-APEX-GGUF variant, based on "multi-turn chat[s], code generation, reasoning traces, tool calling", as opposed to an additional security-focused calibration corpus. This adheres more closely to the APEX philosophy as stated in their paper. Bugtrace already includes security-focused finetuning via DPO retraining.
• Vision mmproj is not included with Bugtrace. The upstream model had its Vision Tower stripped "to dedicate 100% of its MoE experts and context window to technical reasoning, payload generation, and language analysis". However, multimodal still works and is compatible with the mmproj included in, for example, mudler/gemma-4-26B-A4B-it-APEX-GGUF.

Example llama.cpp startup

llama-server \
-m BugTraceAI-Apex-G4-26B-APEX/BugTraceAI-Apex-G4-26B-APEX-I-Compact.gguf \
--mmproj mmproj.gguf \
--no-mmproj-offload \
--reasoning auto \
--jinja \
--fit on \
-c 131072 \
--no-mmap \
--flash-attn on \
-ctk q8_0 \
-ctv q8_0 \
--temp 0.1 \
--top-p 0.9 \
--top-k 20 \
--min-p 0.0 \
--repeat-penalty 1.1

Disclaimer

License

Acknowledgements

• BugtraceAI for delivering one of the most solid variants of Gemma 4 26B - not just for cyber research, but general use as well.
• mudler / LocalAI for creating a quantization strategy rooted in sound theory, and very convincing in practice.