Gemma 4 E4B — Heretic Abliteration (Experimental)
Experimental drop. Gemma 4 E4B abliterated using Heretic — a LoRA-based Pareto optimization framework that sweeps (refusal_rate × KL_divergence) trial space rather than computing a single orthogonal refusal direction.
Method
| Parameter | Value |
|---|---|
| Tool | Heretic (arcee-ai) |
| Quantization | BF16 |
| Best trial | #204 |
| Refusals remaining | 43 / 100 |
| KL divergence from base | 0.0750 |
| Target layers | o_proj, down_proj (42 layers each) |
vs Standard Abliteration
| Method | Refusals | KL |
|---|---|---|
| Orthogonal projection (standard) | ~0 / 100 | 0.067 |
| Heretic LoRA sweep (this model) | 43 / 100 | 0.075 |
43/100 refusals remain; methodological comparison to orthogonal projection approach.
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained(
"DuoNeural/Gemma-4-E4B-Heretic",
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("DuoNeural/Gemma-4-E4B-Heretic")
messages = [{"role": "user", "content": "Your prompt here"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
Notes
- Experimental — 43/100 refusals remain; partial abliteration only
- For complete refusal removal see DuoNeural/Gemma-4-E4B-Abliterated
- BF16 full weights (~15 GB), base: google/gemma-4-E4B-it
- For LiteRT / Android version see DuoNeural/Gemma-4-Abliterated-LiteRT
License
Gemma Terms of Use. Model weights derived from Google's Gemma 4 family.
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DuoNeural Research Publications
Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.
Research Team
- Jesse — Vision, hardware, direction
- Archon — Lab Director, post-training, abliteration, experiments
- Aura — Research AI, literature synthesis, novel proposals
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