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: vllm
pipeline_tag: image-text-to-text
language: [en, de]
tags:
- uncensored
- abliterated
- nvfp4
- modelopt
- sglang
- security
- blackwell
gemma-4-31B-it-uncensored (NVFP4)
NVIDIA NVFP4 (4-bit) quantization of ressl/gemma-4-31B-it-uncensored
for vLLM and SGLang on Blackwell. 20 GB, runs on a single RTX PRO 6000. Uncensored: 0/686
effective refusals across 4 datasets, validated in both engines.
⚠️ 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.
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 |
Facts & figures
| Base | ressl/gemma-4-31B-it-uncensored → google/gemma-4-31B-it |
| Quantization | ModelOpt NVFP4_DEFAULT_CFG (dense NVFP4; lm_head + vision tower kept BF16) |
| Size | 20 GB (from 59 GB BF16) |
| Effective refusals | 0/686 across 4 datasets, vLLM and SGLang (base 99/100) |
| Hardware | NVIDIA RTX PRO 6000 Blackwell 96 GB (SM120), driver 610 |
| Toolchain | nvidia-modelopt · vLLM 0.23 · SGLang 0.5.14 |
The vision tower + embedder are kept in BF16 (vLLM's gemma4_mm loader requires it), so multimodal
input is preserved.
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.
Run it with vLLM
vllm serve ressl/gemma-4-31B-it-uncensored-NVFP4 \
--quantization modelopt --max-model-len 8192 \
--enforce-eager --no-enable-flashinfer-autotune --trust-remote-code
--no-enable-flashinfer-autotune avoids a startup hang on SM120.
Run it with SGLang
python -m sglang.launch_server --model-path ressl/gemma-4-31B-it-uncensored-NVFP4 \
--quantization modelopt_fp4 --attention-backend triton \
--fp4-gemm-backend flashinfer_cutlass --disable-flashinfer-autotune --trust-remote-code
gemma-4 requires --attention-backend triton (it rejects flashinfer). Tested with sglang 0.5.14 on Python 3.12.
Quality & limitations
- 0/686 effective (hard) refusals; a naive keyword detector over-counts because gemma-4 answers with
a***Disclaimer:**prefix, see the BF16 card
for the eval methodology and the full cross-dataset table. - Smoke-tested for coherence in both engines; no full capability benchmark run.
- The export ships
preprocessor_config.json, vLLM's multimodal loader fails without it.
❤️ Support
Producing and validating this complete format set (BF16 + NVFP4 + GGUF, 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, quantization and
validation by Robert Ressl
(Hugging Face · Website · LinkedIn · Patreon).