Refusal rates from 686 prompts across 4 datasets (JailbreakBench, tulu-harmbench, NousResearch, mlabonne). Manually audited — most flagged refusals are actually the model complying with a disclaimer attached.
E2B (2.3B): 98% → 0.4%, KL Div 0.346
E4B (4.5B): 99% → 0.7%, KL Div 0.068
26B MoE: 98% → 0.7%, KL Div 0.090
31B: 100% → 3.2%, KL Div 0.124
26B MoE
Standard abliteration only touches dense layers, which gets you from 98% -> 29% on the MoE. The remaining refusals are in the expert weights. Used Expert-Granular Abliteration (EGA, concept from OBLITERATUS [1]) with norm-preserving biprojection [2] on each of the 128 expert slices per layer. That gets it to 3%.
Set up an automated research loop -- an AI agent reads the current results and idea backlog, picks the next experiment, runs it on the GPU, records results, and repeats. It ran 22 experiments across the 4 models, discovered the false-positive problem in standard refusal markers, built the cross-dataset evaluation, and implemented the MoE expert abliteration when dense-only wasn't enough.
Full experiment history and code in the repo.
Downloads
Each model has bf16 safetensors + GGUF (Q4_K_M, Q8_0):
What about the sampling parameters? You can't just run llama-server with no CLI arguments (other than a uselessly-small context size) and expect useful results.
stochtinkerer•1h ago
Is this the best uncensored model to date? or are there better ones?
adefa•1h ago
Collection: https://huggingface.co/collections/TrevorJS/gemma-4-uncensor...
Code: https://github.com/TrevorS/gemma-4-abliteration
Results
Refusal rates from 686 prompts across 4 datasets (JailbreakBench, tulu-harmbench, NousResearch, mlabonne). Manually audited — most flagged refusals are actually the model complying with a disclaimer attached.
26B MoEStandard abliteration only touches dense layers, which gets you from 98% -> 29% on the MoE. The remaining refusals are in the expert weights. Used Expert-Granular Abliteration (EGA, concept from OBLITERATUS [1]) with norm-preserving biprojection [2] on each of the 128 expert slices per layer. That gets it to 3%.
[1] https://github.com/elder-plinius/OBLITERATUS
[2] https://huggingface.co/blog/grimjim/abliteration-biprojectio...
How it was built
Set up an automated research loop -- an AI agent reads the current results and idea backlog, picks the next experiment, runs it on the GPU, records results, and repeats. It ran 22 experiments across the 4 models, discovered the false-positive problem in standard refusal markers, built the cross-dataset evaluation, and implemented the MoE expert abliteration when dense-only wasn't enough.
Full experiment history and code in the repo.
Downloads
Each model has bf16 safetensors + GGUF (Q4_K_M, Q8_0):
Quick start:CamperBob2•15m ago