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

Libraries

How to use google/gemma-4-31B-it 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")
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("google/gemma-4-31B-it")
model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-31B-it", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
AMD Developer Cloud
Local Apps Settings

vLLM

How to use google/gemma-4-31B-it 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"
# 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",
		"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

SGLang

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

Support OpenAI-compatible multimodal content part aliases in chat template

#109

by yasu-oh - opened May 19

base: refs/heads/main

←

from: refs/pr/109

Discussion Files changed

-7

Files changed (1) hide show

chat_template.jinja +9 -7

chat_template.jinja CHANGED Viewed

@@ -296,11 +296,12 @@
                             {%- endfor -%}
                             {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
                             {%- for part in tool_body -%}
-                                {%- if part.get('type') == 'image' -%}
                                     {{- '<|image|>' -}}
-                                {%- elif part.get('type') == 'audio' -%}
                                     {{- '<|audio|>' -}}
-                                {%- elif part.get('type') == 'video' -%}
                                     {{- '<|video|>' -}}
                                 {%- endif -%}
                             {%- endfor -%}
@@ -322,19 +323,20 @@
                 {%- endif -%}
             {%- elif message['content'] is sequence -%}
                 {%- for item in message['content'] -%}
-                    {%- if item['type'] == 'text' -%}
                         {%- if role == 'model' -%}
                             {{- strip_thinking(item['text']) -}}
                         {%- else -%}
                             {{- item['text'] | trim -}}
                         {%- endif -%}
-                    {%- elif item['type'] == 'image' -%}
                         {{- '<|image|>' -}}
                         {%- set ns.prev_message_type = 'image' -%}
-                    {%- elif item['type'] == 'audio' -%}
                         {{- '<|audio|>' -}}
                         {%- set ns.prev_message_type = 'audio' -%}
-                    {%- elif item['type'] == 'video' -%}
                         {{- '<|video|>' -}}
                         {%- set ns.prev_message_type = 'video' -%}
                     {%- endif -%}

                             {%- endfor -%}
                             {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
                             {%- for part in tool_body -%}
+                                {%- set part_type = part.get('type') -%}
+                                {%- if part_type in ['image', 'image_url'] -%}
                                     {{- '<|image|>' -}}
+                                {%- elif part_type in ['audio', 'audio_url', 'input_audio'] -%}
                                     {{- '<|audio|>' -}}
+                                {%- elif part_type in ['video', 'video_url'] -%}
                                     {{- '<|video|>' -}}
                                 {%- endif -%}
                             {%- endfor -%}
                 {%- endif -%}
             {%- elif message['content'] is sequence -%}
                 {%- for item in message['content'] -%}
+                    {%- set item_type = item.get('type') -%}
+                    {%- if item_type == 'text' -%}
                         {%- if role == 'model' -%}
                             {{- strip_thinking(item['text']) -}}
                         {%- else -%}
                             {{- item['text'] | trim -}}
                         {%- endif -%}
+                    {%- elif item_type in ['image', 'image_url'] -%}
                         {{- '<|image|>' -}}
                         {%- set ns.prev_message_type = 'image' -%}
+                    {%- elif item_type in ['audio', 'audio_url', 'input_audio'] -%}
                         {{- '<|audio|>' -}}
                         {%- set ns.prev_message_type = 'audio' -%}
+                    {%- elif item_type in ['video', 'video_url'] -%}
                         {{- '<|video|>' -}}
                         {%- set ns.prev_message_type = 'video' -%}
                     {%- endif -%}