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")
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
```

lucianommartins commited on Jun 15

Commit

ee2de7d

verified ·

1 Parent(s): 27596f5

fix: revert add_generation_prompt regression + preserve_thinking default

Browse files

Two fixes based on reviewer feedback from vllm-project/vllm#45553:

1. Restore original add_generation_prompt guard: suppress <|turn>model
when prev_message_type is 'tool_response' or 'tool_call'. The model
continues the same turn after tool responses — a new <|turn>model
breaks multi-step tool chains (assistant->tool->assistant->tool).
Keep the <|channel>thought\n cue for thinking-enabled after tool
responses.

2. Change preserve_thinking default from true to false. Per Gemma4 docs
(https://ai.google.dev/gemma/docs/core/prompt-formatting-gemma4
#managing-thought-context): 'You must remove (strip) the model's
generated thoughts from the previous turn.' Thoughts within a
single tool call chain are still preserved (they're after
last_user_idx).

Ref: https://github.com/vllm-project/vllm/pull/45553
Ref: https://github.com/vllm-project/vllm/pull/42776

Files changed (1) hide show

chat_template.jinja +5 -10

chat_template.jinja CHANGED Viewed

@@ -178,7 +178,7 @@
 {%- set ns = namespace(prev_message_type=None, prev_non_tool_role=None) -%}
 {%- set loop_messages = messages -%}
 {%- set enable_thinking = enable_thinking | default(false) -%}
-{%- set preserve_thinking = preserve_thinking | default(true) -%}
 {{- bos_token -}}
 {#- Handle System/Tool Definitions Block -#}
 {%- if enable_thinking or tools or (messages and messages[0]['role'] in ['system', 'developer']) -%}
@@ -376,17 +376,12 @@
 {%- endfor -%}
 {%- if add_generation_prompt -%}
-    {%- if ns.prev_message_type != 'tool_call' -%}
         {{- '<|turn>model\n' -}}
-        {%- if enable_thinking and ns.prev_message_type == 'tool_response' -%}
-            {{- '<|channel>thought\n' -}}
-        {%- endif -%}
-    {%- endif -%}
-    {%- if not enable_thinking -%}
-        {#- Suppress thinking — but not when awaiting tool responses -#}
-        {%- if ns.prev_message_type != 'tool_call' -%}
             {{- '<|channel>thought\n<channel|>' -}}
         {%- endif -%}
     {%- endif -%}
 {%- endif -%}

 {%- set ns = namespace(prev_message_type=None, prev_non_tool_role=None) -%}
 {%- set loop_messages = messages -%}
 {%- set enable_thinking = enable_thinking | default(false) -%}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
 {{- bos_token -}}
 {#- Handle System/Tool Definitions Block -#}
 {%- if enable_thinking or tools or (messages and messages[0]['role'] in ['system', 'developer']) -%}
 {%- endfor -%}
 {%- if add_generation_prompt -%}
+    {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}
         {{- '<|turn>model\n' -}}
+        {%- if not enable_thinking -%}
             {{- '<|channel>thought\n<channel|>' -}}
         {%- endif -%}
+    {%- elif ns.prev_message_type == 'tool_response' and enable_thinking -%}
+        {{- '<|channel>thought\n' -}}
     {%- endif -%}
 {%- endif -%}