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).

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")

---

DuoNeural

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

Research Team

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