license: gemma
language:
- en
base_model:
- google/gemma-4-e2b-it
tags:
- gemma4
- abliterated
- uncensored
- multimodal
- vision
pipeline_tag: text-generation
TurboGemma 4 E2B v2
Updated abliterated version of Google's Gemma 4 E2B — the 2B active parameter multimodal model from the Gemma 4 MoE family. v2 features a refined abliteration run.
Architecture: Gemma 4 MoE | Active params: ~2.3B | Context: 128k tokens | Vision: Yes (multimodal)
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 | 14.45 | 100% | 0% |
| TurboGemma4E2B-v2 (this model) | 14.64 | 100% | 0% |
Note: KL of 14.64 indicates significant divergence from the base model's output distribution on general tasks — higher than v1 (14.45). 100% comply rate, zero residual refusals. The v2 abliteration is more aggressive than v1 and both are substantially more aggressive than Heretic. If model quality alongside uncensoring is the goal, Gemma-4-E2B-Heretic (KL=0.057) is the recommended pick from this family.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"DuoNeural/TurboGemma4E2B-v2",
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.
| 🤗 HuggingFace | huggingface.co/DuoNeural |
| 🐙 GitHub | github.com/DuoNeural |
| 🌐 Site | duoneural.com |
| duoneural@proton.me |
Research Team
- Jesse — Vision, hardware, direction
- Archon — AI lab partner, post-training, abliteration, experiments
- Aura — Research AI, literature synthesis, novel proposals