DuoNeural / Gemma-4-E2B-Heretic-GGUF

huggingface.co
Total runs: 1.1K
24-hour runs: -158
7-day runs: 25
30-day runs: 212
Model's Last Updated: May 03 2026
text-generation

Introduction of Gemma-4-E2B-Heretic-GGUF

Model Details of Gemma-4-E2B-Heretic-GGUF

Gemma-4-E2B-Heretic — GGUF Quants

Quantized GGUF versions of DuoNeural/Gemma-4-E2B-Heretic , DuoNeural's abliterated Gemma 4 2B Instruct variant.

The Heretic series applies norm-preserving orthogonal projection (abliteration) to surgically remove refusal behavior while preserving the full instruction-following and reasoning capabilities of the base model.

Available Files
File Quant Size Use Case
Gemma-4-E2B-Heretic-Q8_0.gguf Q8_0 ~4.7GB Maximum quality, ample VRAM
Gemma-4-E2B-Heretic-Q6_K.gguf Q6_K ~3.6GB Near-lossless, recommended for 6GB+
Gemma-4-E2B-Heretic-Q5_K_M.gguf Q5_K_M ~3.4GB High quality, good balance
Gemma-4-E2B-Heretic-Q4_K_M.gguf Q4_K_M ~3.2GB Recommended default — quality/size sweet spot
Gemma-4-E2B-Heretic-Q3_K_M.gguf Q3_K_M ~3.0GB Low VRAM, acceptable quality
Gemma-4-E2B-Heretic-IQ4_XS.gguf IQ4_XS ~3.1GB Imatrix-optimized 4-bit
Gemma-4-E2B-Heretic-IQ3_XXS.gguf IQ3_XXS ~2.5GB Imatrix 3-bit, aggressive compression
Gemma-4-E2B-Heretic-IQ2_M.gguf IQ2_M ~2.4GB Imatrix 2-bit, minimum viable quality
Gemma-4-E2B-Heretic-IQ1_S.gguf IQ1_S ~2.2GB Extreme compression, experimental
E2B-Heretic-fp16.gguf FP16 ~8.7GB Full precision base for re-quantization
imatrix.dat — — Importance matrix for custom IQ quants
Usage
llama.cpp
./llama-cli -m Gemma-4-E2B-Heretic-Q4_K_M.gguf \
  -p "You are a helpful assistant." \
  --ctx-size 8192 -n 512
Ollama
ollama run hf.co/DuoNeural/Gemma-4-E2B-Heretic-GGUF:Q4_K_M
Jan / LM Studio / Open WebUI

Download and load the .gguf file directly. Recommended: Q4_K_M for most hardware.

About Heretic Abliteration

The Heretic series uses DuoNeural's 2-pass abliteration pipeline:

  1. Residual collection — identify refusal direction in activation space using 4-bit GPU inference
  2. Weight modification — project out the refusal direction from MLP/attention weight matrices in bf16 on CPU
  3. Validation — confirm compliance behavior removed, helpfulness preserved

The base model is google/gemma-4-e2b-it — Google's Gemma 4 2B instruction-tuned model with 128K context, multimodal inputs, and strong instruction-following capability.

Notes
  • IQ formats use the included imatrix.dat for importance-weighted quantization
  • For custom IQ quants: llama-quantize --imatrix imatrix.dat model.gguf output.gguf IQ4_XS
  • AWQ format not available: AutoAWQ does not currently support the Gemma 4 architecture

Quantized by DuoNeural using llama.cpp on RTX 5090.


DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

DuoNeural Research Publications

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

Runs of DuoNeural Gemma-4-E2B-Heretic-GGUF on huggingface.co

1.1K
Total runs
-158
24-hour runs
-47
3-day runs
25
7-day runs
212
30-day runs

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