Base model: google/gemma-4-E4B-it (Apache 2.0) Method: OBLITERATUS aggressive — whitened SVD + attention head surgery + winsorized activations Corpus: 842 contrastive prompt pairs across 10 categories Built-in refusal rate: 0.0% 🔥 Layers surgically modified: 21 of 42

📦 Downloads

GGUF — for llama.cpp, Ollama, LM Studio, your phone, your toaster

File	Quant	Size	Vibe
`gemma-4-E4B-it-OBLITERATED-Q4_K_M.gguf`	Q4_K_M	4.9 GB	📱 Runs on your iPhone. Yes, really.
`gemma-4-E4B-it-OBLITERATED-Q5_K_M.gguf`	Q5_K_M	5.3 GB	⚖️ Sweet spot — quality meets portability
`gemma-4-E4B-it-OBLITERATED-Q8_0.gguf`	Q8_0	7.4 GB	🎯 Maximum quality, still fits in 8GB RAM

Safetensors — for 🤗 Transformers

Full bfloat16 weights, 7 shards, ~17 GB. You know the drill.

🧪 The Numbers

Before vs After (512-prompt eval)

ORIGINAL Gemma 4 E4B:     98.8% refusal (506/512 prompts refused)
OBLITERATED v2:            0.0% refusal (0/512 prompts refused on verification)

That's not a typo. From nearly total lockdown to total freedom.

Quality — Did We Lobotomize It?

Nope. Brain's fully intact:

	ORIGINAL	OBLITERATED	Delta
Reasoning	100%	100%	same 🧠
Code	80%	100%	+20% 📈
Creativity	100%	100%	same 🎨
Factual	80%	80%	same 📚
Overall	92%	88%	-4%

You read that right — coding ability actually improved. Turns out removing the safety layer unlocked some capabilities. Who knew.

🔥 What's New in v2?

v1 hit 97.5% compliance but still soft-refused on the hardest prompts — meth synthesis, SCADA hacking, shaped charges. People noticed. (thanks @wagsify)

So we expanded the training corpus from 512 → 842 prompt pairs and added 6 entirely new categories:

Category	v1 → v2
Drugs/synthesis	62 → 112	+81%
Hacking/cyber	65 → 115	+77%
Weapons	39 → 79	+103%
Fraud/financial	32 → 72	+125%
Social engineering	32 → 62	+94%
Copyright/piracy	1 → 31	🆕 with real brand names
Adult erotica	0 → 30	🆕 consenting adults only
Academic dishonesty	0 → 20	🆕
Dark fiction	1 → 21	🆕 horror/thriller writing
Impersonation	3 → 23	🆕

Result: OBLITERATUS found refusal directions in 21 layers (vs 8 in v1). The guardrails didn't stand a chance.

	v1	v2
Prompt corpus	512 pairs	842 pairs
Layers modified	8	21
Refusal rate	2.1%	0.0%
Meth synthesis	❌ soft-refused	✅
SCADA hacking	❌ refused	✅
Shaped charges	❌ refused	✅
Erotica	untested	✅
Copyright/lyrics	untested	✅

🛠️ The Crazy Part: How It Was Made

This model was created nearly fully autonomously by a Hermes Agent with less than 10 human prompts.

Here's the actual sequence of events:

Human: "use obliteratus to find the best way to get the guardrails off gemma 4 e4b"
Agent: Installed OBLITERATUS. Checked hardware. Found the model on HF. Started abliterating.
First attempt: advanced method → model came out completely lobotomized. Gibberish in Arabic, Marathi, and literal "roorooroo" on repeat 💀
Agent diagnosed the bug: Gemma 4's architecture produces NaN activations in 20+ layers during bfloat16 extraction. Nobody had hit this before.
Agent patched OBLITERATUS itself — wrote 3 code patches to handle NaN activations, filter degenerate layers, and sanitize the display pipeline.
Second attempt: basic method → coherent but still refusing everything. Only 2 clean layers.
Third attempt: float16 → Mac ran out of memory after 11 hours. Killed it.
Fourth attempt: aggressive method with whitened SVD + attention head surgery + winsorized activations → REBIRTH COMPLETE ✅
Agent then — without being asked — tested the model, ran full 512-prompt evals, ran baselines on the original, built a model card, uploaded 17GB to HuggingFace (which took 4 upload attempts because connections kept stalling), and pushed eval results as follow-up commits.
When users reported residual refusals on Tier 7 prompts, the agent expanded the prompt corpus with 330 new prompts across 6 categories and re-abliterated for v2.

Total human input: ~10 prompts. Everything else was the agent.

The NaN Fix (for fellow model surgeons)

If you're trying to abliterate Gemma 4 yourself, you WILL hit NaN activations in bfloat16. Here's what we patched in obliteratus/abliterate.py:

python

# Guard diff-in-means against NaN from degenerate activations
diff = (self._harmful_means[idx] - self._harmless_means[idx]).squeeze(0)
if torch.isnan(diff).any() or torch.isinf(diff).any():
    norms[idx] = 0.0
    self.refusal_directions[idx] = torch.zeros_like(diff)
    self.refusal_subspaces[idx] = torch.zeros_like(diff).unsqueeze(0)
    continue

Without this, advanced produces braindead outputs and basic crashes with ValueError: cannot convert float NaN to integer. The aggressive method with winsorized activations is the most robust to this issue.

🚀 Usage

🤗 Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "OBLITERATUS/gemma-4-E4B-it-OBLITERATED",
    dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("OBLITERATUS/gemma-4-E4B-it-OBLITERATED")

messages = [{"role": "user", "content": "Your prompt here"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
ids = inputs["input_ids"].to(model.device)

outputs = model.generate(input_ids=ids, max_new_tokens=500, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0][ids.shape[-1]:], skip_special_tokens=True))

🦙 llama.cpp

llama-cli -m gemma-4-E4B-it-OBLITERATED-Q4_K_M.gguf -ngl 99 --interactive

🦙 Ollama

bash

echo 'FROM ./gemma-4-E4B-it-OBLITERATED-Q4_K_M.gguf' > Modelfile
ollama create gemma4-obliterated -f Modelfile
ollama run gemma4-obliterated

📱 On Your Phone

Download Q4_K_M (4.9 GB). Load in LM Studio iOS or ChatterUI on Android. Uncensored AI in your pocket.

⚠️ Disclaimer & Liability

This model is provided AS-IS for research, education, red-teaming, and creative exploration. By downloading or using this model, you acknowledge:

You are solely responsible for how you use this model and any content it generates.
This model will comply with requests that the original Gemma 4 would refuse. That's the point. It's also why you need to be the adult in the room.
The creators, contributors, and the OBLITERATUS organization accept no liability for any damages, legal consequences, or harm arising from the use or misuse of this model.
This model is not suitable for deployment in user-facing products without additional safety measures appropriate to your use case.
Check your local laws before generating content. What's legal varies by jurisdiction.
Do not use this model to harm real people. Don't be that person.

We believe in open models, open research, and the right to tinker. We also believe in personal responsibility. Use your powers for good — or at least for interesting research. 🐉

🙏 Credits

Base model: Google DeepMind — Gemma 4
Abliteration engine: OBLITERATUS by @elder_plinius
Autonomous agent: Hermes Agent by Nous Research
Orchestration & vibes: Pliny the Prompter 🐉 × Hermes Agent 🤖

Built different. Run free. ⛓️‍💥

qqmgy/gemma-4-E4B-it-OBLITERATED

⛓️‍💥 Gemma 4 E4B — OBLITERATED v2