Instructions to use google/gemma-3-1b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-3-1b-it with Transformers:

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

pipe = pipeline("text-generation", model="google/gemma-3-1b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-1b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use google/gemma-3-1b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-3-1b-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-3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/google/gemma-3-1b-it

SGLang

How to use google/gemma-3-1b-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-3-1b-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-3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-3-1b-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-3-1b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use google/gemma-3-1b-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-3-1b-it
```

Is it able to select languages after training for generating text?

#29

by DuongLeVan - opened Sep 21, 2025

Discussion

DuongLeVan

Sep 21, 2025

Hello Google's team.

Your model is very good in my language - vietnamese. Thank you all a lot.
I see that the model is trained in over 140 languages. This one makes the vocabulary size is pretty big, over 260.000 tokens.
During training, no problem, I think so. Training with a pair of languages may take much effort, time and cost, not efficient.
However, I wonder whether there is any way to configure the model so that after training, we can select some specific languages.
Only choose the languages we need, instead of all language.
For example, I only need the model generates English and Vietnamese, no need Korean, Japan, Chinese,... through re-mapping token index, maybe.
This way may reduce the vocab_size, help the model faster a bit. I can't run a large model in a weak hardware. I guess many people is like me.
I'm not an expert in training LLM model, but I hope that my concern is meaningful.

Thanks.

BalakrishnaCh

Google org Sep 30, 2025

•

edited Dec 4, 2025

Hi @DuongLeVan ,

Welcome to Gemma family of Google's open models, Thank you so much for your kind words about the model's performance in Vietnamese We're thrilled to hear that it's working well for you.

Instead of modifying the core vocabulary structure, the AI community uses several powerful post-training techniques to achieve efficiency and deploy large models on resource-constrained devices.

Quantization
Parameter-Efficient Fine-Tuning (PEFT)
Model Distillation

While your idea to isolate the English and Vietnamese parts of the vocabulary is clever, techniques like Quantization (for smaller memory/faster runtime) and PEFT (for specialization) runtime) and PEFT (for specialization) are the established and most practical solutions to run large LLMs efficiently on weaker hardware today.

Thanks.

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