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:
Residual collection
— identify refusal direction in activation space using 4-bit GPU inference
Weight modification
— project out the refusal direction from MLP/attention weight matrices in bf16 on CPU
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.
Gemma-4-E2B-Heretic-GGUF huggingface.co is an AI model on huggingface.co that provides Gemma-4-E2B-Heretic-GGUF's model effect (), which can be used instantly with this DuoNeural Gemma-4-E2B-Heretic-GGUF model. huggingface.co supports a free trial of the Gemma-4-E2B-Heretic-GGUF model, and also provides paid use of the Gemma-4-E2B-Heretic-GGUF. Support call Gemma-4-E2B-Heretic-GGUF model through api, including Node.js, Python, http.
Gemma-4-E2B-Heretic-GGUF huggingface.co is an online trial and call api platform, which integrates Gemma-4-E2B-Heretic-GGUF's modeling effects, including api services, and provides a free online trial of Gemma-4-E2B-Heretic-GGUF, you can try Gemma-4-E2B-Heretic-GGUF online for free by clicking the link below.
DuoNeural Gemma-4-E2B-Heretic-GGUF online free url in huggingface.co:
Gemma-4-E2B-Heretic-GGUF is an open source model from GitHub that offers a free installation service, and any user can find Gemma-4-E2B-Heretic-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of Gemma-4-E2B-Heretic-GGUF install, users can directly use Gemma-4-E2B-Heretic-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
Gemma-4-E2B-Heretic-GGUF install url in huggingface.co: