Gemma 4 Uncensored (autoresearch results)(huggingface.co)
huggingface.co
Gemma 4 Uncensored (autoresearch results)
https://huggingface.co/collections/TrevorJS/gemma-4-uncensored
4 comments
Is this the best uncensored model to date? or are there better ones?
You could try this one against the defending Qwen 3.5 champion: https://huggingface.co/HauhauCS/models
Released uncensored versions of all four Gemma 4 models. bf16 + GGUF for each.
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.
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%.
[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):
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.
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
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%.
[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):
E2B bf16: https://huggingface.co/TrevorJS/gemma-4-E2B-it-uncensored
E2B GGUF: https://huggingface.co/TrevorJS/gemma-4-E2B-it-uncensored-GGUF
E4B bf16: https://huggingface.co/TrevorJS/gemma-4-E4B-it-uncensored
E4B GGUF: https://huggingface.co/TrevorJS/gemma-4-E4B-it-uncensored-GGUF
26B bf16: https://huggingface.co/TrevorJS/gemma-4-26B-A4B-it-uncensored
26B GGUF: https://huggingface.co/TrevorJS/gemma-4-26B-A4B-it-uncensored-GGUF
31B bf16: https://huggingface.co/TrevorJS/gemma-4-31B-it-uncensored
31B GGUF: https://huggingface.co/TrevorJS/gemma-4-31B-it-uncensored-GGUF
llama-server -hf TrevorJS/gemma-4-26B-A4B-it-uncensored-GGUF -c 8192What 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.