Instructions to use grimjim/gemma-3-12b-it-MPOAdd-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/gemma-3-12b-it-MPOAdd-v1 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-MPOAdd-v1")
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-MPOAdd-v1")
model = AutoModelForImageTextToText.from_pretrained("grimjim/gemma-3-12b-it-MPOAdd-v1")
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

vLLM

How to use grimjim/gemma-3-12b-it-MPOAdd-v1 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-MPOAdd-v1"
# 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-MPOAdd-v1",
		"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-MPOAdd-v1

SGLang

How to use grimjim/gemma-3-12b-it-MPOAdd-v1 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-MPOAdd-v1" \
    --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-MPOAdd-v1",
		"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-MPOAdd-v1" \
        --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-MPOAdd-v1",
		"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-MPOAdd-v1 with Docker Model Runner:
```
docker model run hf.co/grimjim/gemma-3-12b-it-MPOAdd-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gemma-3-12b-it-MPOAdd-v1

This model was derived from google/gemma-3-12b-it, and represents Magnitude-Preserving Orthogonal Addition (MPOAdd).

Projected abliteration has been applied to neasuring 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, the directional component of the refusal direction was added, or enhanced, while preserving the norms of the layers subjected to intervention. The model will now push back against perceived harms in an exaggerated manner, as refusal over safety concerns is more strongly enforced. More details of norm preservation can be found in the article on Norm-Preserving Biprojected Abliteration.

These geometric tweaks defy the conventional wisdom that (blunt) ablation or addition damages the reasoning of models. Perplexity loss has been minimal when measured on Q8_0 GGUFs compared to baseline.

Downloads last month: 8

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for grimjim/gemma-3-12b-it-MPOAdd-v1

Base model

google/gemma-3-12b-pt

Finetuned

google/gemma-3-12b-it

Finetuned

(342)

this model

Quantizations

2 models