Instructions to use aifeifei798/Gemma-4-Queen-31B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aifeifei798/Gemma-4-Queen-31B-it with Transformers:

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

pipe = pipeline("image-text-to-text", model="aifeifei798/Gemma-4-Queen-31B-it")
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("aifeifei798/Gemma-4-Queen-31B-it")
model = AutoModelForMultimodalLM.from_pretrained("aifeifei798/Gemma-4-Queen-31B-it")
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 Settings

vLLM

How to use aifeifei798/Gemma-4-Queen-31B-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aifeifei798/Gemma-4-Queen-31B-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aifeifei798/Gemma-4-Queen-31B-it",
		"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/aifeifei798/Gemma-4-Queen-31B-it

SGLang

How to use aifeifei798/Gemma-4-Queen-31B-it 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 "aifeifei798/Gemma-4-Queen-31B-it" \
    --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": "aifeifei798/Gemma-4-Queen-31B-it",
		"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 "aifeifei798/Gemma-4-Queen-31B-it" \
        --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": "aifeifei798/Gemma-4-Queen-31B-it",
		"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 aifeifei798/Gemma-4-Queen-31B-it with Docker Model Runner:
```
docker model run hf.co/aifeifei798/Gemma-4-Queen-31B-it
```

gemma 4 QAT

by shisuuu - opened Jun 6

Discussion

shisuuu

Jun 6

First of all — thank you for the incredible work on Gemma-4-Queen-31B-it!
I've been using it heavily and it fits my use case perfectly.
I wanted to ask: are you planning to release a QAT version of Gemma-4-Queen-31B?
Google recently released QAT weights for the base Gemma 4 31B.

aifeifei798

Owner Jun 6

Thank you for your support and for sharing how the model is working for you.
To be transparent, training the Queen version of Gemma-4-31B already pushed my current hardware setup to its absolute limit. Because of these constraints, I cannot guarantee a QAT (Quantization-Aware Training) version at this time. However, I plan to look into its feasibility and will certainly try to make it work if my resources allow.
Thank you again for your understanding and interest.

aifeifei798

Owner Jun 8

aifeifei798/gemma-4-31B-Queen-it-qat-q4_0-unquantized
https://huggingface.co/aifeifei798/gemma-4-31B-Queen-it-qat-q4_0-unquantized

Please wait for mradermacher to create the GGUF version.
https://huggingface.co/mradermacher/gemma-4-31B-Queen-it-qat-q4_0-unquantized-i1-GGUF
https://huggingface.co/mradermacher/gemma-4-31B-Queen-it-qat-q4_0-unquantized-GGUF

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment

gemma 4 QAT

aifeifei798/gemma-4-31B-Queen-it-qat-q4_0-unquantizedhttps://huggingface.co/aifeifei798/gemma-4-31B-Queen-it-qat-q4_0-unquantized

aifeifei798/gemma-4-31B-Queen-it-qat-q4_0-unquantized
https://huggingface.co/aifeifei798/gemma-4-31B-Queen-it-qat-q4_0-unquantized