mudler/gemma-4-26B-A4B-it-heretic-APEX-GGUF

Gemma 4 26B-A4B Heretic (Abliterated) APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of gemma-4-26B-A4B-it-heretic — an abliterated (uncensored) version of Gemma 4, created with the Heretic tool (v1.2.0) using Arbitrary-Rank Ablation (ARA) on layers 10-30 to reduce refusals while preserving capabilities (KL divergence 0.0499 from original).

Brought to you by the LocalAI team | APEX Project | Technical Report

Benchmark Results

Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.

Available Files

File	Profile	Size	Best For
gemma-4-26B-A4B-heretic-APEX-I-Balanced.gguf	I-Balanced	~19 GB	Best overall quality/size ratio
gemma-4-26B-A4B-heretic-APEX-I-Quality.gguf	I-Quality	~20 GB	Highest quality with imatrix
gemma-4-26B-A4B-heretic-APEX-Quality.gguf	Quality	~20 GB	Highest quality standard
gemma-4-26B-A4B-heretic-APEX-Balanced.gguf	Balanced	~19 GB	General purpose
gemma-4-26B-A4B-heretic-APEX-I-Compact.gguf	I-Compact	~15 GB	Consumer GPUs, best quality/size
gemma-4-26B-A4B-heretic-APEX-Compact.gguf	Compact	~15 GB	Consumer GPUs
gemma-4-26B-A4B-heretic-APEX-I-Mini.gguf	I-Mini	~13 GB	Smallest viable, fastest inference
mmproj.gguf	Vision projector	~1.2 GB	Required for image understanding

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

See the APEX project for full details, technical report, and scripts.

Architecture

• Model: gemma-4-26B-A4B-it-heretic (same architecture as gemma-4-26B-A4B-it)
• Layers: 30
• Experts: 128 routed (8 active per token)
• Total Parameters: 26B
• Active Parameters: ~4B per token
• Vision: Built-in vision encoder (mmproj included)
• APEX Config: 5+5 symmetric edge gradient across 30 layers
• Calibration: v1.3 diverse dataset

Run with LocalAI

local-ai run mudler/gemma-4-26B-A4B-it-heretic-APEX-GGUF@gemma-4-26B-A4B-heretic-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.