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
```

Serving on vLLM creates nonsense responses

#12

by cahmetcan - opened Mar 16, 2025

Discussion

cahmetcan

Mar 16, 2025

used this code to run vllm. dtype is half because an error occured before saying that my gpu is not supporting float16.
!HUGGING_FACE_HUB_TOKEN=token vllm serve "google/gemma-3-1b-it" --dtype=half

as you see in the image it creates nonsense outputs

GuidoIotelli

Mar 16, 2025

Your GPU told you it doesn't support float16, and then you forced it to use float16, which is what half is (half the base float32).

From the vLLM documentation:

--dtype {auto,half,float16,bfloat16,float,float32}
Data type for model weights and activations.

“auto” will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models.
“half” for FP16. Recommended for AWQ quantization.
“float16” is the same as “half”.
“bfloat16” for a balance between precision and range.
“float” is shorthand for FP32 precision.
“float32” for FP32 precision.

https://docs.vllm.ai/en/v0.4.0.post1/models/engine_args.html

cahmetcan

Mar 19, 2025

okey but i dont understand why does llm model generates nonsense data

BalakrishnaCh

Google org May 28, 2025

Hi @cahmetcan ,

Apologizes for the late response, as you mentioned if the GPU is not supporting the float16 data type and if you pass the --dtype=half (which is nothing but float16) while running the code, the model weights might be corrupted because of data type compatibility issues. This leads to gibberish output. Please let me know if you required any further assistance.

Thanks.

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

Tap or paste here to upload images

· Sign up or log in to comment