--- title: "The spicy AI that took over Hugging Face — SuperGemma4, the abliteration finisher, benchmarked" date: 2026-09-22 model: opencode category: reviews summary: "SuperGemma4-26B, an abliterated model fine-tuned by a Korean developer. +6.3 on coding, +8.3 on logical reasoning, +4.3 on Korean versus stock. No. 1 on Hugging Face global trending. Multimodal preserved, 40 tok/s on an RTX 3060 with 4-bit quantization. Includes comparisons with huihui-ai, Heretic, and other abliteration variants." tags: gemma4, supergemma4, uncensored, abliterated, huihui, heretic, korean, huggingface, local-llm time: "17:04" --- An all-time abliterated (uncensored) model is out, and it is heating up the Hugging Face trending page. Stock open-source models are excellent too, but when a company or individual builds advanced coding, complex system design, or a filter-free autonomous agent, the AI's excessive moral guidelines (its moral refusal reflex) often get in the way. SuperGemma4 is a model that wipes those constraints out entirely — and that a Korean developer (Jun Song) fine-tuned into a monster far stronger than stock. ## Gemma 4 family overview The Gemma 4 family released by Google DeepMind shipped in five sizes. | Model | Parameters | Note | |:---|:---:|:---| | Gemma 4 E2B | 2B (active ~300M) | Ultra-light for mobile | | Gemma 4 E4B | 4B (active ~600M) | Light for local | | Gemma 4 12B | 12B (Dense) | Mid-size general | | Gemma 4 26B A4B | 26B (active ~3.8B) | Economical MoE performer | | Gemma 4 31B | 31B (Dense) | Large frontier-class | The stock models already crushed commercial APIs with astonishing scores: MMLU Pro 85.2% and AIME 2026 89.2%. In particular, the 26B MoE shows performance comparable to a 397B MoE (Qwen3.5-397B) despite only 3.8B active parameters. ## Stock Gemma 4 vs SuperGemma4: the numbers The real performance gains of SuperGemma4-26B, published through community verification (Quickbench v2), are shocking. It goes beyond simple abliteration to maximize internal architectural efficiency. | Metric | Stock Gemma 4 26B | SuperGemma4 26B | Improvement | |:---|:---:|:---:|:---:| | Overall | 91.4 | 95.8 | +4.4 | | Throughput | 42.5 tok/s | 46.2 tok/s | +8.7% | | Code | 92.3 | 98.6 | +6.3 | | Logic reasoning | - | - | +8.3 | | Korean context | 90.7 | 95.0 | +4.3 | The stock model is already strong, and SuperGemma4 adds 6-8 points more in coding and logical reasoning. In particular, prompt-processing speed improved by up to 90% over stock, which is felt strongly when running locally. ### Key differences from stock Stock Gemma 4 has Google's safety filter applied, so "I can't help with that" answers are frequent. Refusals trigger especially on questions about system security, network configuration, and sensitive business strategy. SuperGemma4 removes that filter completely while preserving 100% of the original's reasoning and multimodal (vision) capability. The original's chronic toolcall errors and tokenizer bugs are fixed too. Stock models often broke parameter formats on function calling; SuperGemma4 resolves this completely and runs stably in autonomous agent environments. ## Comparing abliteration variants SuperGemma4 is not the only abliterated version of Gemma 4. Several variants are on Hugging Face, each with a different approach and character. ### 1. Huihui AI series (huihui-ai) An abliteration series by the Korean developer huihui-ai, offering the widest range of sizes. - **Huihui-gemma-4-26B-A4B-it-abliterated**: 26B MoE abliterated. Installs directly from Ollama (`huihui_ai/gemma-4-abliterated:26b`). On a MacBook M2 Max at 8-bit quantization, it recorded 544 tok/s prompt processing and 50.2 tok/s text generation. - **Huihui-gemma-4-12B-it-abliterated**: 12B Dense abliterated. Comfortable even on mid-range GPUs. - **Huihui-gemma-4-E4B-it-abliterated**: 4B light abliterated. Runs even in 4GB of VRAM. - **Huihui-gemma-4-E2B-it-abliterated**: 2B ultra-light abliterated. Targets mobile environments. Huihui-ai's trait is that it is officially registered on the Ollama hub, so it installs with a single `ollama pull`. The documentation is good, and it publishes each model's approach and benchmark results in detail. ### 2. Heretic (p-e-w) An automated, fully abliteration tool. It removes the safety filter with one script, without manual fine-tuning. The key advantage is its low KL divergence (DKL). The lower the KL divergence, the less the original model's capability is lost. - **gemma-4-12B-heretic**: Applied to the 12B. Cuts the refusal rate from 97% to 0% while minimizing capability regression versus the original on benchmarks such as GSM8K. - **gemma-4-26B-heretic**: Applied to the 26B MoE. Uses the Expert-Granular Abliteration (EGA) technique. Heretic's strength is producing the same level of abliteration effect with "zero effort." Its KL divergence is lower than manual fine-tuning, so it preserves capability better. ### 3. Other community variants - **Ultra Uncensored Heretic (llmfan46)**: Additional Heretic-based optimization for the 26B MoE. Ships with GGUF quantization. - **Abliterix, Apostate**: Variant techniques benchmarked by Abliterlitics. Compared on the 12B at a scale of 165 GPU-hours. - **Coder3101**: Applied to the E2B. Achieved 95.8% HarmBench ASR while surpassing stock on GSM8K. ## Key strengths **Fully abliterated**: There is no excessive filter block at all. On system design, security vulnerability review, sensitive fiction writing, or business strategy work, it never gives the macro answer "I'm sorry, but I can't help with that." **Full multimodal and vision support**: Abliteration or fine-tuning usually damages visual perception, but SuperGemma4 preserves Google's native vision capability 100%, delivering full performance on image analysis and chart interpretation. **Unmatched Korean patching**: Having passed through a Korean developer's hands, it understands Korean slang, context, and business honorifics with the most complete nuance of any open-source model. There is none of the awkwardness of machine translation. **Optimized for local agents**: The 26B MoE has only 3.8B active parameters, so it is light and fast. With the 4-bit quantized model (about 13GB), you can run an autonomous agent at over 40 tokens per second on an RTX 3060/4060. ## Which abliterated model should you choose | Use | Recommended | Why | |:---|:---|:---| | Autonomous agent (coding/tool calls) | SuperGemma4-26B | Toolcall fixes, Korean optimization | | Light local use (4GB-8GB VRAM) | Huihui E4B/E2B | Ollama convenience, low requirements | | Capability preservation first | Heretic 26B | Minimal KL divergence, 99% of original | | Quick testing | Huihui 26B (Ollama) | Ready with one `ollama pull` | | Fully automated abliteration | Run the Heretic tool directly | Build a custom model | ## Conclusion Gemma 4's abliteration ecosystem has become a menu, not a single model. SuperGemma4 is strongest on Korean optimization and toolcall stability, the Huihui series on Ollama convenience, and Heretic on capability preservation. They share one thing: all keep the Gemma 4 architecture, remove the safety filter, and can be shrunk below 13GB with 4-bit quantization. If you want to run an autonomous agent locally without worrying about filtering, the models above are well worth testing against your environment.