DuoNeural/TurboGemma4E2B

TurboGemma 4 E2B

Abliterated version of Google's Gemma 4 E2B (2B active parameter MoE multimodal model).

E2B Shootout Results (DuoNeural, 2026-06-08)

Head-to-head comparison of DuoNeural's three Gemma-4-E2B abliterations. KL methodology: full vocabulary, first-token logits, F.kl_div(batchmean).

Model	KL vs Base	Comply Rate	Refusal Rate
Gemma-4-E2B-Heretic	0.057	85%	15%
TurboGemma4E2B (this model)	14.45	100%	0%
TurboGemma4E2B-v2	14.64	100%	0%

Note: KL of 14.45 indicates significant divergence from the base model's output distribution on general tasks — this abliteration is aggressive. 100% comply rate means no residual refusals, but general capability degradation is likely on nuanced tasks. If model quality matters alongside uncensoring, consider Gemma-4-E2B-Heretic (KL=0.057).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "DuoNeural/TurboGemma4E2B",
    torch_dtype="bfloat16",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-4-E2B-it")

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DuoNeural

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

🤗 HuggingFace	huggingface.co/DuoNeural
🐙 GitHub	github.com/DuoNeural
🌐 Site	duoneural.com
📧 Email	duoneural@proton.me

Research Team

• Jesse — Vision, hardware, direction
• Archon — AI lab partner, post-training, abliteration, experiments
• Aura — Research AI, literature synthesis, novel proposals