Instructions to use google/gemma-3-4b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-3-4b-it with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/gemma-3-4b-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-3-4b-it")
model = AutoModelForMultimodalLM.from_pretrained("google/gemma-3-4b-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
Local Apps Settings

vLLM

How to use google/gemma-3-4b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-3-4b-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-3-4b-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-3-4b-it

SGLang

How to use google/gemma-3-4b-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-3-4b-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-3-4b-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-3-4b-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-3-4b-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-3-4b-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-3-4b-it
```

4bit Quantization Failure for gemma 4b

#73

by shabha7092 - opened Oct 6, 2025

Discussion

shabha7092

Oct 6, 2025

•

edited Oct 6, 2025

bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True
)
self.language_model = AutoModelForCausalLM.from_pretrained(
config.text_config.model_path,
trust_remote_code=True,
device_map= device_map,
config = self.text_config,
attn_implementation= "flash_attention_2",
**kwargs ) Fine tuning is failing with assertion failure assert module.weight.shape[1] == 1 in fix_4bit_weight_quant_state_from_module definiton of bitsandbytes . Tried updating transformers/ bitsandbytes and accelerate to latest version but had no luck. Current package versions : transformers==4.54.1 peft==0.17.0 bitsandbytes==0.47.0 accelerate==1.9.0. PS. gemma12b seems to be fine.

shabha7092

Oct 6, 2025

Did anyone face this issue before ?

BalakrishnaCh

Google org Oct 8, 2025

Hi @shabha7092 ,

Welcome to Google's Gemma family of open source models, thanks for reaching out to us. I have done the 4 bit (int4) quantization for the google/gemma-3-4b-it model and also done the quantization of the model by using above mentioned your bnb parameters. I'm not facing any kind of difficultly or issues with these quantizations. Please find the attached gist file for your reference.

Thanks.

shabha7092

Oct 8, 2025

If you havent noticed in the above code snippet i am trying to load only the Language Model component of gemma-3-4b-it using AutoModelForCausalLM. from_pretrained. I am not using Gemma3ForConditionalGeneration to load the whole model. Let me take a step back and explain you what i am doing: step 1) Load the gemma-3-4b-it using the Gemma3ForConditionalGeneration 2) Extract the language model model = Gemma3ForConditionalGeneration.from_pretrained("google/gemma-3-4b-it", device_map=None) language_model = model.language_model language_model.save_pretrained('/language_model') 3) When i load back this saved language model using AutoModelForCausalLM.from_pretrained and bnb config i am facing the above mentioned error. Note that i dint have face anycissues doing the same thing for google/gemma-3-12b-it and i was succesufly able to fine tune. P.S upgrading the packages to latest version also dint help.

shabha7092

Oct 9, 2025

Any update on this ?

Gerald001

Dec 8, 2025

•

edited Dec 8, 2025

hi @shabha7092 @BalakrishnaCh , how to do fp8 quantization? would BitsAndBytesConfig also work for that? where do you use BitsAndBytesConfig?

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