Instructions to use kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4")
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("kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4")
model = AutoModelForImageTextToText.from_pretrained("kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4")
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 kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4",
		"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/kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4

SGLang

How to use kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4 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 "kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4" \
    --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": "kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4",
		"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 "kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4" \
        --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": "kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4",
		"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 kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/kabachuha/Gemma-4-The-Deckards-Brain-31B-NVFP4
```

The DECKARD's Brain - Gemma 4, 31b - NVFP4

NVFP4-quantized version of kabachuha/Gemma-4-The-Deckards-Brain-31B, a merge of two heretic Gemma 4 31B dense models with thinking capabilities.

Calibration was done on r/writingprompts story continuations using the modelopt library.

For 5090: Assuming you don't run heavy processes, you can easily fully fit up to ~49000 tokens context into a single GPU. This gives around 60+ tokengen/s.

Convertation to GGUF is made easily with llama.cpp's convert_hf_to_gguf.py.

The prompt format is fully inherited from DavidAU's The Deckard Thinking, meaning the chat template will have thinking by default. To disable, override the chat template with chat_template-instruct.jinja.

Thinking is highly recommended to not be ever turned off! If you are role-playing in non-English, this is crucial to bring your language output distribution closer to the English/Japanese training set, otherwise it will be much more bland and more censored!

For the rest of the information, see the original model card.

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Safetensors

Model size

18B params

Tensor type

F16

F8_E4M3

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kabachuha/Gemma-4-The-Deckards-Brain-31B

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