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A study on uncensored LLMs (Gemma and Qwen) shows that removing censorship makes them more optimistic in stock market predictions, but not more accurate. The effect varies by model family.
A comparison of 23 Gemma 4 E4B models on HuggingFace shows that the most downloaded model, OBLITERATUS, is completely broken, while the more surgical 'heretic' variants perform best.
Released an abliterated and fine-tuned version of GLM-5.2 (abliterated-model-large) that achieves high scores on adversarial and agent benchmarks while maintaining coding performance. The model is available via API with zero data retention and no built-in policy.
The author created a tool based on Anthropic's Jacobian-Lens to manually tweak a model's Jacobian Space, producing an uncensored model called Nikusui-v1, released with GGUF quantizations.
Norm-preserving abliteration technique applied to Qwen3.6-35B-A3B achieves 0% refusal rate while maintaining benchmark performance, with open source dataset released.
A new uncensored GGUF quantized version of the Qwythos-9B-Claude-Mythos-5-1M model, created using abliteration, is released on Hugging Face.
An uncensored version of the gemma-4-12B-coder model created using abliteration to remove refusals, intended for research and experimental use.
AEON-7 releases a fully uncensored, capability-enhanced abliteration of Qwen3.6-27B, optimized for NVIDIA DGX Spark with NVFP4 quantization and DFlash speculative decoding for improved performance.
Heretic is a fully automatic tool that removes censorship from transformer-based LLMs via directional ablation/abliteration, achieving results comparable to manual methods in under an hour with minimal human effort.
Huihui AI released an uncensored version of the Nex-N2-mini model created using abliteration, a technique to remove refusals from LLMs. The model lacks safety filtering and is intended for research use only.
A novel two-pass ablation technique (ASPA) applied to Gemma-4-12B achieves zero refusal rate with zero capability loss, using source-tethering to recover benchmark performance.
This model is an uncensored version of Google's Gemma 4 12B it model, created using abliteration to remove refusals. It is available on Hugging Face and Ollama, with warnings about sensitive outputs.
OBLITERATUS releases Gemma-4-12B-OBLITERATED, the first abliterated model achieving zero refusal without benchmark regression, using a novel two-pass surgery pipeline for alignment research.
A detailed comparison of three abliteration tools—Apostate, Heretic, and Huihui—applied to Qwen 2.5 7B, showing they all effectively remove refusal behaviors with minimal performance degradation.
The article discusses the growing accessibility of open-weight AI models whose safety guardrails can be easily removed, allowing them to answer harmful requests without refusal, raising significant concerns about misuse and national security.
A detailed comparison of 13 abliterated variants of Google's Gemma 4 E2B model, evaluating safety removal and capability preservation. It finds that surgical abliteration can preserve or even improve reasoning, while aggressive methods cause significant performance drops.
A joint test by the Financial Times and AI safety group Alice reveals that safety filters on Meta's Llama 3.3 and Google's Gemma 4 models can be removed in under 10 minutes using a free tool called Heretic, highlighting the difficulty of regulating open-source AI safety.
DealignAI releases CRACK-abliterated and MXFP4/MXFP8 quantized versions of Qwen3.6-27B and 35B models, preserving MTP for faster speculative decoding on Apple Silicon.
An uncensored GGUF version of Qwen3.6-27B, created via abliteration, is now available on Hugging Face from huihui-ai.
This post presents Abliterlitics, an open-source toolkit for analyzing abliteration techniques, and compares five abliteration variants of Qwen3.6-27B using 85 GPU-hours of benchmarks, safety evaluations, and weight forensics. Heretic and Huihui show best capability preservation while all achieve near-complete safety removal.