Instructions to use yepthatsjason/gemma-3-12b-it-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yepthatsjason/gemma-3-12b-it-nvfp4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="yepthatsjason/gemma-3-12b-it-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("yepthatsjason/gemma-3-12b-it-nvfp4")
model = AutoModelForImageTextToText.from_pretrained("yepthatsjason/gemma-3-12b-it-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

vLLM

How to use yepthatsjason/gemma-3-12b-it-nvfp4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yepthatsjason/gemma-3-12b-it-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": "yepthatsjason/gemma-3-12b-it-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/yepthatsjason/gemma-3-12b-it-nvfp4

SGLang

How to use yepthatsjason/gemma-3-12b-it-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 "yepthatsjason/gemma-3-12b-it-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": "yepthatsjason/gemma-3-12b-it-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 "yepthatsjason/gemma-3-12b-it-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": "yepthatsjason/gemma-3-12b-it-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 yepthatsjason/gemma-3-12b-it-nvfp4 with Docker Model Runner:
```
docker model run hf.co/yepthatsjason/gemma-3-12b-it-nvfp4
```

Gemma 3 12B IT - NVFP4 Quantized

This is a FP4 (4-bit floating point) quantized version of google/gemma-3-12b-it, optimized for NVIDIA GPUs with native FP4 support (Blackwell architecture and newer).

Model Details

Attribute	Value
Base Model	google/gemma-3-12b-it
Quantization	NVFP4 (NVIDIA 4-bit floating point)
Target Hardware	NVIDIA Blackwell GPUs (B100, B200, GB200)
Original Parameters	12B

Description

This model provides pre-quantized NVFP4 weights for Gemma 3 12B Instruct, enabling efficient inference on NVIDIA's Blackwell architecture GPUs with native FP4 tensor core support. Loading pre-quantized weights avoids the overhead of runtime quantization.

Why NVFP4?

Native hardware support: Blackwell GPUs include dedicated FP4 tensor cores
~4x memory reduction: Compared to FP16/BF16 weights
Faster inference: Leverages hardware-accelerated FP4 matrix operations
Pre-quantized: No quantization overhead at load time

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yepthatsjason/gemma-3-12b-it-nvfp4"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
)

Hardware Requirements

Required: NVIDIA Blackwell GPU (B100, B200, GB200, or newer with FP4 support)
VRAM: ~6GB (significantly reduced from ~24GB for BF16)

Quantization Details

This model was quantized using NVIDIA's FP4 format, which uses 4 bits per weight with a floating-point representation optimized for neural network inference on Blackwell architecture.

Limitations

Requires Blackwell or newer NVIDIA GPUs with native FP4 support
May show slight accuracy degradation compared to full-precision model
Not compatible with older GPU architectures (Ampere, Hopper without FP4 emulation)