---

license: apache-2.0

license_link: https://ai.google.dev/gemma/docs/gemma_4_license

base_model: ressl/gemma-4-31B-it-uncensored

base_model_relation: quantized

library_name: gguf

pipeline_tag: text-generation

language: [en, de]

tags:

- uncensored

- abliterated

- gguf

- llama.cpp

- security

- gemma4

---

!gemma-4-31B-it-uncensored

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_content and content can 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).