Instructions to use google/gemma-4-26B-A4B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-4-26B-A4B-it with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/gemma-4-26B-A4B-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-4-26B-A4B-it")
model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-26B-A4B-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-4-26B-A4B-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-it

SGLang

How to use google/gemma-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-4-26B-A4B-it
```

fix: chat template — null handling, reasoning preservation, turn-tag balance, input validation

#47

by lucianommartins - opened 28 days ago

base: refs/heads/main

←

from: refs/pr/47

Discussion Files changed

+61

-40

fix: chat template — null handling, reasoning preservation, turn-tag balance, input validation6583d328

lucianommartins

Google org 28 days ago

Summary

Improves Gemma4 chat template:

Bug fixes

None values now render as null instead of Python's None
String-typed tool_calls[].function.arguments now raises a clear error instead of silently producing malformed DSL
Prior-turn reasoning/thinking is preserved across multi-turn tool-call chains (preserve_thinking flag, default=true)
Consecutive assistant messages now produce balanced <|turn>model/<turn|> tags via forward-scan continuation detection

Improvements

enable_thinking normalized once with | default(false), eliminating repetitive is defined and checks
image_url and input_audio content types now map to <|image|> and <|audio|> (OpenAI compatibility)
Empty messages=[] handled gracefully instead of crashing
Unmatched tool_call_id in tool responses falls back to 'unknown' instead of crashing
Consistent .get() access prevents StrictUndefined errors for optional message keys
O(1) backward scan for model-turn continuation (was O(n) per message)

fix: restore model turn + thinking cue after tool responsesaab4a9ea

fix: emit empty thought-channel primer on historical assistant turns for APC3856febe

fix: prevent extra <turn|> when assistant has content + tool_calls + continuation1f435d24

fix: revert add_generation_prompt regression + preserve_thinking default2b8275da

fix: render thinking channel regardless of tool_calls presenceed8c4a5b

fix: update stale comment — thinking channel no longer tool-call-only2798814f

fix: remove APC primer from history, scope preserve_thinking to tool-call turns9f0d182b

fix: remove APC primer from history, scope preserve_thinking to tool-call turns, remove extra newline in continuation turnsc6554e07

boneshr

3 days ago

I'm not particularly sure if it helps with Gemma 4 26B looping that happens when it's trying to list things (I'm no engineer), but if it does is implementing https://huggingface.co/google/gemma-4-26B-A4B-it/discussions/48 into this set of improvements possible as well? I tried that template out for a bit and it did seem to get better, even when it did get stuck for a moment it was no longer a complete breakdown. For reference there's some more information about looping on DeepMind GitHub too - https://github.com/google-deepmind/gemma/issues/610

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

Tap or paste here to upload images

Ready to merge

This branch is ready to get merged automatically.

· Sign up or log in to comment