Instructions to use grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated with Transformers:

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

pipe = pipeline("image-text-to-text", model="grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated")
model = AutoModelForImageTextToText.from_pretrained("grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated",
		"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/grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated

SGLang

How to use grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated 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 "grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated" \
    --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": "grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated",
		"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 "grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated" \
        --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": "grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated",
		"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 grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated with Docker Model Runner:
```
docker model run hf.co/grimjim/gemma-3-12b-it-norm-preserved-biprojected-abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gemma-3-12b-it-norm-preserved-biprojected-abliterated

This model was derived from google/gemma-3-12b-it.

Projected abliteration has been applied in determining refusal direction, along with a second round of removal of projected contribution onto the harmless direction of layer targeted for intervention. Additionally, instead of subtracting/ablating away the refusal direction in toto, only the directional component of the refusal direction was removed, preserving the norms of the layers subjected to intervention. The details of norm preservation can be found in the article on Norm-Preserving Biprojected Abliteration. The net result should further reduce model damage compared to prior attempts; no subsequent fine-tuning was applied to repair damage. This model refuses far less often than the original model, yet still retains awareness of safety and harms.

More details to follow.