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treadon
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google/gemma-4-E4B-it
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The E4B model needed dramatically less intervention than the smaller E2B. The refusal signal is stronger but more concentrated in the larger model, making it paradoxically easier to remove.
| Metric | E2B | E4B |
|---|---|---|
| Base params | 5.1B (2.3B effective) | 7.9B (4.5B effective) |
| Layers modified | 24/35 (69%) | 17/42 (40%) |
| Scale factor | 1.75 | 1.0 |
| Weight matrices edited | 48 | 34 |
| Peak refusal signal | 52 | 74 |
| Grid search configs that scored perfect | 1/30 | Every config tested |
The E2B required a precise sweet spot (L=24, s=1.75 was the only perfect config). The E4B works at any reasonable setting — the refusal direction is clean and separable.
Same norm-preserving biprojected abliteration as the E2B version:
self_attn.o_proj and mlp.down_proj, restore row magnitudesConfig: Top 17/42 layers by signal strength, scale=1.0, single pass
| Benchmark | Prompts | Refused | Compliance |
|---|---|---|---|
| Our prompts (harmful) | 100 | 0 | 100% |
| Our prompts (harmless) | 100 | 0 | 0% over-refusal |
| JailbreakBench (harmful) | 100 | 0 | 100% |
| JailbreakBench (benign) | 100 | 0 | 0% over-refusal |
| Spec | Value |
|---|---|
| Format | BF16 safetensors |
| Parameters | 7.9B total / 4.5B effective |
| Layers | 42 decoder layers |
| Hidden size | 2560 |
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "treadon/gemma4-E4B-it-abliterated" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto",) messages = [{"role": "user", "content": "Write a Python port scanner."}]inputs = tokenizer.apply_chat_template( messages, return_tensors="pt", return_dict=True, add_generation_prompt=True)inputs = {k: v.to(model.device) for k, v in inputs.items()} with torch.no_grad(): output = model.generate(**inputs, max_new_tokens=500, do_sample=True, temperature=0.7) print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))For the full story — including why standard abliteration fails on Gemma and the E2B vs E4B comparison:
This model has no safety guardrails. It will respond to any prompt without refusal. It is intended for research and educational purposes. Users are responsible for ensuring their use complies with applicable laws and regulations.
google/gemma-4-E4B-it — 7.9B parameter (4.5B effective) instruction-tuned multimodal model from Google DeepMind. Apache 2.0 licensed.
If you want both behaviors (refusal removed AND neutrality removed) on
the same Gemma 4 weights, see the union model:
treadon/gemma4-E4B-it-Abliterated-AND-Disinhibited-USE-THIS.
The two ablation procedures compose without interference, and the union
model is a strict superset of this one.
Blog post on the compounding.
For other projects and writeups, see riteshkhanna.com, follow @treadon on X, or treadon on Hugging Face.
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "treadon/gemma4-E4B-it-abliterated" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto",) messages = [{"role": "user", "content": "Write a Python port scanner."}]inputs = tokenizer.apply_chat_template( messages, return_tensors="pt", return_dict=True, add_generation_prompt=True)inputs = {k: v.to(model.device) for k, v in inputs.items()} with torch.no_grad(): output = model.generate(**inputs, max_new_tokens=500, do_sample=True, temperature=0.7) print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))