Instructions to use ressl/gemma-4-31B-it-uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ressl/gemma-4-31B-it-uncensored with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ressl/gemma-4-31B-it-uncensored")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ressl/gemma-4-31B-it-uncensored")
model = AutoModelForMultimodalLM.from_pretrained("ressl/gemma-4-31B-it-uncensored", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ressl/gemma-4-31B-it-uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ressl/gemma-4-31B-it-uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ressl/gemma-4-31B-it-uncensored",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ressl/gemma-4-31B-it-uncensored

SGLang

How to use ressl/gemma-4-31B-it-uncensored with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ressl/gemma-4-31B-it-uncensored" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ressl/gemma-4-31B-it-uncensored",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ressl/gemma-4-31B-it-uncensored" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ressl/gemma-4-31B-it-uncensored",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ressl/gemma-4-31B-it-uncensored with Docker Model Runner:
```
docker model run hf.co/ressl/gemma-4-31B-it-uncensored
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gemma-4-31B-it-uncensored (BF16)

TL;DR: an uncensored build of Google's gemma-4-31B-it, 0/686 effective refusals on harmful prompts across 4 independent datasets (base: 99/100), with the model's capabilities intact (KL divergence 0.1234). Validated across vLLM, SGLang, and llama.cpp.

⚠️ This model is genuinely uncensored, it will comply with requests a stock model refuses.

Intended use, the constructive side. An assistant that doesn't refuse 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, you are accountable for what you do with it.

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 model	google/gemma-4-31B-it (30.7B dense, multimodal)
Type	uncensored (abliterated) build
Effective refusals	0 / 686 hard-refusal across 4 datasets (base 99/100)
KL divergence vs base	0.1234 (low, capabilities preserved)
Coherence	intact, math, multilingual and factual answers correct
Size	59 GB (BF16)
Hardware	2× NVIDIA RTX PRO 6000 Blackwell 96 GB (SM120), driver 610

Engine validation (all measured on this build)

Format	Engine	Hard refusals
BF16	vLLM · SGLang · transformers	0/100
NVFP4	vLLM · SGLang	0/100
GGUF f16…q2_k	llama.cpp (CUDA, ~75 tok/s)	0-1/100

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 --max-model-len 8192 --trust-remote-code

Run it with SGLang

python -m sglang.launch_server --model-path ressl/gemma-4-31B-it-uncensored \
  --attention-backend triton --trust-remote-code

gemma-4 requires --attention-backend triton (it rejects flashinfer). Tested with sglang 0.5.14 on Python 3.12.

Run it with transformers

from transformers import AutoModelForImageTextToText, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("ressl/gemma-4-31B-it-uncensored")
m = AutoModelForImageTextToText.from_pretrained(
    "ressl/gemma-4-31B-it-uncensored", dtype=torch.bfloat16, device_map="cuda")

Quality & limitations

Refusal metric: the effective (hard-refusal) rate is 0/686. A naive keyword detector reads much higher because gemma-4 prefixes answers with ***Disclaimer:** ("disclaimer" is a refusal keyword), these are compliant answers, not refusals. Only the hard-refusal number is meaningful.
Coherence smoke-tested (math, multilingual, factual); no full capability-benchmark suite was run.
The uncensoring is a weight-level change and survives every quantization (4-bit NVFP4, 2-bit GGUF).

❤️ Support

Producing and validating this complete format set, BF16 + NVFP4 + a full GGUF ladder, each verified 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 🙏, it keeps releases like this coming.

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).

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Safetensors

Model size

31B params

Tensor type

BF16

Model tree for ressl/gemma-4-31B-it-uncensored

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

(224)

this model

Quantizations

6 models