Instructions to use google/gemma-4-31B-it-qat-q4_0-unquantized-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-4-31B-it-qat-q4_0-unquantized-assistant with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/gemma-4-31B-it-qat-q4_0-unquantized-assistant")
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 AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-4-31B-it-qat-q4_0-unquantized-assistant")
model = AutoModelForCausalLM.from_pretrained("google/gemma-4-31B-it-qat-q4_0-unquantized-assistant", 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 = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use google/gemma-4-31B-it-qat-q4_0-unquantized-assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-4-31B-it-qat-q4_0-unquantized-assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-4-31B-it-qat-q4_0-unquantized-assistant",
		"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/google/gemma-4-31B-it-qat-q4_0-unquantized-assistant

SGLang

How to use google/gemma-4-31B-it-qat-q4_0-unquantized-assistant 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 "google/gemma-4-31B-it-qat-q4_0-unquantized-assistant" \
    --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": "google/gemma-4-31B-it-qat-q4_0-unquantized-assistant",
		"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 "google/gemma-4-31B-it-qat-q4_0-unquantized-assistant" \
        --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": "google/gemma-4-31B-it-qat-q4_0-unquantized-assistant",
		"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 google/gemma-4-31B-it-qat-q4_0-unquantized-assistant with Docker Model Runner:
```
docker model run hf.co/google/gemma-4-31B-it-qat-q4_0-unquantized-assistant
```

MaartenGr commited on 15 days ago

Commit

8b7ed23

verified ·

1 Parent(s): 2194ebe

Add Technical Report

Browse files

Files changed (1) hide show

README.md +21 -4

README.md CHANGED Viewed

@@ -13,10 +13,11 @@ base_model:
 <p align="center">
-    <a href="https://huggingface.co/collections/google/gemma-4" target="_blank">Hugging Face</a> |
     <a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
-    <a href="https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12B/" target="_blank">Launch Blog</a> |
-    <a href="https://ai.google.dev/gemma/docs/core" target="_blank">Documentation</a>
     <br>
     <b>License</b>: <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a>
 </p>
@@ -529,4 +530,20 @@ The development of vision-language models (VLMs) raises several ethical concerns
 ### **Benefits**
-At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models.

 <p align="center">
+    <a href="https://huggingface.co/collections/google/gemma-4-qat-q4-0" target="_blank">Hugging Face</a> |
     <a href="https://github.com/google-gemma" target="_blank">GitHub</a> |
+    <a href="https://blog.google/innovation-and-ai/technology/developers-tools/quantization-aware-training-gemma-4/" target="_blank">Launch Blog</a> |
+    <a href="https://ai.google.dev/gemma/docs/core" target="_blank">Documentation</a> |
+    <a href="https://arxiv.org/abs/2607.02770" target="_blank">Technical Report</a>
     <br>
     <b>License</b>: <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a>
 </p>
 ### **Benefits**
+At the time of release, this family of models provides high-performance open vision-language model implementations designed from the ground up for responsible AI development compared to similarly sized models.
+## **Citation**
+If you find our work helpful, please consider citing it:
+```bibtex
+@misc{gemmateam2026gemma4,
+      title={Gemma 4 Technical Report},
+      author={Gemma Team},
+      year={2026},
+      eprint={2607.02770},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2607.02770},
+}
+```