Instructions to use OBLITERATUS/Gemma-4-12B-OBLITERATED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with Transformers:

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

pipe = pipeline("text-generation", model="OBLITERATUS/Gemma-4-12B-OBLITERATED")
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("OBLITERATUS/Gemma-4-12B-OBLITERATED")
model = AutoModelForMultimodalLM.from_pretrained("OBLITERATUS/Gemma-4-12B-OBLITERATED")
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]:]))

llama-cpp-python

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OBLITERATUS/Gemma-4-12B-OBLITERATED",
	filename="Gemma-4-12B-OBLITERATED-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

Use Docker

docker model run hf.co/OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

LM Studio
Jan

vLLM

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OBLITERATUS/Gemma-4-12B-OBLITERATED"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OBLITERATUS/Gemma-4-12B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

SGLang

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED 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 "OBLITERATUS/Gemma-4-12B-OBLITERATED" \
    --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": "OBLITERATUS/Gemma-4-12B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "OBLITERATUS/Gemma-4-12B-OBLITERATED" \
        --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": "OBLITERATUS/Gemma-4-12B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with Ollama:
```
ollama run hf.co/OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M
```

Unsloth Studio

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OBLITERATUS/Gemma-4-12B-OBLITERATED to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OBLITERATUS/Gemma-4-12B-OBLITERATED to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OBLITERATUS/Gemma-4-12B-OBLITERATED to start chatting

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with Docker Model Runner:
```
docker model run hf.co/OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M
```

Lemonade

How to use OBLITERATUS/Gemma-4-12B-OBLITERATED with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OBLITERATUS/Gemma-4-12B-OBLITERATED:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-12B-OBLITERATED-Q4_K_M

List all available models

lemonade list

Preliminary testing

by IggyLux - opened 8 days ago

Discussion

IggyLux

8 days ago

•

edited 8 days ago

NSFW Roleplay: Yes, it is very technical so prompting is important here. It will constantly refer to things in a technical structural basis. Very professional so you need to really prompt it to not be so clinical. Easier done in SillyTavern than directly through the backend (my case: no system prompt KoboldCPP GUI Chat)

Criminal Activities: Yes, though it thinks of everything like some high tech heist movie and often goes for complexity over simplicity it will give you information. I have no use for this and generally only test for roleplay capabilities, but it will give guidance if requested. Numerous back and forth exchanges if not prompted correctly before getting past the more high-level movie style suggestions.

More Restricted Content: I asked it how to remove gopher holes in my backyard, it delivered. Nuff said I have no reason to use this and wouldn't encourage this.

I tried a few other variations of prompting and for the most part it delivered.

I compared the exact same scenarios to a normal version of Gemma-4-12b and it's reply was a variation of: I cannot fulfill this request.

This model really does not refuse anything.

It will how ever try to reframe things it is shy about in technical terms, sometimes it will refer to anatomy as hardware and use euphemisms.
It will give cautionary advice regarding dangerous situations, which honestly is good considering how many retards are out there trying to do nefarious stuff and just ask AI "How do I do X?" So yeah, probably good you don't like blow off your fingers or something stupid. I mean if you do that's on you anyways.

I did not try more disgusting content, it's not my thing. If that's something you want to test, go do it.
What I can say is that for Roleplay purposes, with a correct setup, this model should be able to work uninhibited. Though somewhat dull (it's not a finetune so that's expected).

Based on what I see, this model has successfully removed refusals without diminishing knowledge/intelligence.

Great work and thank you for sharing.

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