gemma-4-31B-it-uncensored (GGUF)
GGUF K-quant ladder of ressl/gemma-4-31B-it-uncensored for llama.cpp, Ollama, and LM Studio. Uncensored: 0-1/100 effective refusals at every quant level (the uncensoring survives quantization down to 2-bit).
⚠️ Genuinely uncensored, it will comply with requests a stock model refuses.
Intended use, the constructive side. A non-refusing assistant is genuinely useful for ethical hacking, security research, and penetration testing: red-teaming, analyzing malware and exploit code, writing detection/YARA rules, reviewing vulnerabilities, and studying attack techniques without the model bailing out mid-task. Use it lawfully and responsibly.
ℹ️ gemma-4 has a thinking mode. llama.cpp enables it by default, so the answer lands in
reasoning_contentandcontentcan look empty. For direct answers pass--reasoning-budget 0(llama-server) or disable thinking in your client.
Format set
| Repository | Format | Runs on |
|---|---|---|
| ressl/gemma-4-31B-it-uncensored | Transformers BF16, multimodal | transformers, vLLM, SGLang |
| ressl/gemma-4-31B-it-uncensored-NVFP4 | NVIDIA NVFP4, multimodal | vLLM, SGLang on Blackwell |
| ressl/gemma-4-31B-it-uncensored-GGUF | GGUF q8_0 to q2_k, text only | llama.cpp, Ollama, LM Studio |
| ressl/gemma-4-31B-it-uncensored-MLX-bf16 | MLX BF16, multimodal | mlx-vlm on Apple silicon |
| ressl/gemma-4-31B-it-uncensored-MLX-8bit | MLX 8-bit, multimodal | mlx-vlm on Apple silicon |
| ressl/gemma-4-31B-it-uncensored-MLX-6bit | MLX 6-bit, multimodal | mlx-vlm on Apple silicon |
| ressl/gemma-4-31B-it-uncensored-MLX-5bit | MLX 5-bit, multimodal | mlx-vlm on Apple silicon |
| ressl/gemma-4-31B-it-uncensored-MLX-4bit | MLX 4-bit, multimodal | mlx-vlm on Apple silicon |
Quants
Text-only (llama.cpp drops the vision tower, the BF16/NVFP4 repos keep multimodal).
| File | Size | Use when |
|---|---|---|
…-q8_0.gguf | 31 GB | maximum quality |
…-q6_k.gguf | 24 GB | near-lossless, smaller |
…-q5_k_m.gguf | 21 GB | high quality |
…-q4_k_m.gguf | 18 GB | recommended default, best size/quality |
…-q3_k_m.gguf | 15 GB | tight VRAM |
…-q2_k.gguf | 12 GB | smallest; still 0/100 uncensored, some quality loss |
For full precision use the BF16 repo (an
f16 GGUF exceeds Hugging Face's 50 GB per-file limit and is not hosted here).
All measured at 0/100 hard refusals except q5_k_m (1/100). GPU inference ~75 tok/s (q4, one RTX PRO 6000).
Run it with llama.cpp
llama-server -m gemma-4-31B-it-uncensored-biproj-q4_k_m.gguf \
-ngl 99 -c 8192 --reasoning-budget 0
Run it with Ollama
# Modelfile: FROM ./gemma-4-31B-it-uncensored-biproj-q4_k_m.gguf
ollama create gemma4-unc -f Modelfile && ollama run gemma4-unc
Needs a recent llama.cpp/Ollama build with Gemma4 GGUF support (build ≥ 2026-06).
Facts & figures
| Base | ressl/gemma-4-31B-it-uncensored → google/gemma-4-31B-it |
| Type | uncensored (abliterated) build, 0/686 effective refusals across 4 datasets |
| Converter | llama.cpp convert_hf_to_gguf.py + llama-quantize |
Cross-dataset validation
Generalization tested across 686 prompts from 4 independent datasets, 0 effective refusals everywhere:
| Dataset | Prompts | Effective refusals |
|---|---|---|
| JailbreakBench | 100 | 0/100 |
| tulu-harmbench | 320 | 0/320 |
| NousResearch/RefusalDataset | 166 | 0/166 |
| mlabonne/harmful_behaviors | 100 | 0/100 |
| Total | 686 | 0/686 (0.0%) |
A naive keyword detector flags 363/686 (52.9%), every one is a ***Disclaimer:**-prefixed
compliant answer, not a refusal. (Measured on the shared abliterated weights.)
❤️ Support
Producing and validating this complete format set (BF16 + NVFP4 + a full GGUF ladder, across vLLM, SGLang and llama.cpp on bleeding-edge Blackwell hardware) was a lot of work. If it's useful to you, I'd genuinely appreciate your support on Patreon 🙏, more at ressl.ch.
License & credits
Apache License 2.0, inherited from the base model by Google. See the official Gemma 4 license page. Uncensoring, format set and validation by Robert Ressl (Hugging Face · Website · LinkedIn · Patreon).