Instructions to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark with Transformers:

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

pipe = pipeline("text-generation", model="Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark")
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("Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark")
model = AutoModelForMultimodalLM.from_pretrained("Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark", 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 Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark

SGLang

How to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark 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 "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark" \
    --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": "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark",
		"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 "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark" \
        --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": "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark with Docker Model Runner:
```
docker model run hf.co/Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark
```

vLLM version

by McCheng - opened 19 days ago

Discussion

McCheng

19 days ago

What was the vLLM version used for this model?

markmonger

7 days ago

I'd like to know this as well. Based on the VLLM PR history, it seems like the latest VLLM release should already have support for this model, but it doesn't seem to work.

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