Instructions to use ewald1976/gemma-4-12b-auto-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ewald1976/gemma-4-12b-auto-v4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ewald1976/gemma-4-12b-auto-v4")
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("ewald1976/gemma-4-12b-auto-v4")
model = AutoModelForMultimodalLM.from_pretrained("ewald1976/gemma-4-12b-auto-v4", 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 ewald1976/gemma-4-12b-auto-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ewald1976/gemma-4-12b-auto-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ewald1976/gemma-4-12b-auto-v4",
		"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/ewald1976/gemma-4-12b-auto-v4

SGLang

How to use ewald1976/gemma-4-12b-auto-v4 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 "ewald1976/gemma-4-12b-auto-v4" \
    --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": "ewald1976/gemma-4-12b-auto-v4",
		"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 "ewald1976/gemma-4-12b-auto-v4" \
        --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": "ewald1976/gemma-4-12b-auto-v4",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use ewald1976/gemma-4-12b-auto-v4 with Docker Model Runner:
```
docker model run hf.co/ewald1976/gemma-4-12b-auto-v4
```

Gemma 12B Auto v4

This is the first proper release in an experimental fine-tuning series based on unsloth/gemma-4-12b-it.

Training started from the General Inquiry Thinking / Chain-of-Thought dataset and was supplemented with custom repair and broad-coverage examples created for this project. The goal is to help the model learn when reasoning is useful and when a short, direct answer is the better choice.

Small behavioral evaluation

We compared the model with the original Gemma 4 12B Instruct base on an internal suite of 40 fixed prompt/seed combinations. Both models were evaluated as Q6_K GGUFs with temperature 0.7, a maximum of 3,000 new tokens, and identical prompts and seeds.

Model / mode	Separate reasoning used	Mean final answer	Median final answer	Empty finals	Token-limit stops
Base, Thinking enabled	40/40	396.4 words	375.5 words	1	1
Auto v4, Thinking enabled	5/40	126.4 words	43.0 words	1	1
Base, Instruct mode	0/40	404.7 words	381.5 words	0	0
Auto v4, Instruct mode	0/40	294.4 words	311.0 words	0	0

Compared with the base model, Auto v4 produced final answers that were 68.1% shorter on average with Thinking enabled and 27.3% shorter in Instruct mode. With Thinking enabled, it answered directly in 35 of 40 cases while keeping a separate reasoning path available for selected tasks.

This is a small internal behavioral test focused on reasoning routing and verbosity, not a standardized benchmark or a comprehensive accuracy evaluation. The single empty, length-limited Thinking case came from a known ambiguous scheduling prompt shared by both runs.

The model was trained with Unsloth and Hugging Face TRL.