Instructions to use google/gemma-2-2b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-2-2b-it with Transformers:

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

pipe = pipeline("text-generation", model="google/gemma-2-2b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-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-2-2b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-2-2b-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-2-2b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/google/gemma-2-2b-it

SGLang

How to use google/gemma-2-2b-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-2-2b-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-2-2b-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-2-2b-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-2-2b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use google/gemma-2-2b-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-2-2b-it
```

Recommended Settings

#73

by Lef00 - opened Nov 1, 2025

Discussion

Lef00

Nov 1, 2025

What are the recommended settings?
Temperature, top_p etc?

pannaga10

Google org Nov 3, 2025

Hi @Lef00 It usually depends on what your building specifically your usecase. The standard recommendation is to use a temperature 1.0 to allow for moderate randomness and creativity, a Top_P of 0.95 to ensure the model considers a broad but not overly sparse set of probable tokens , and a Top_K of 64 to limit the sampling to the top 64 tokens at each step. These parameters are designed to produce high-quality, diverse, and contextually relevant responses, but for more deterministic or fact-intensive tasks, you might lower the temperature towards 0.2 and Top_P towards 0.8 to increase predictability.
Thank you

Lef00

Nov 10, 2025

I used Gemma 2 2B it and Gemma 7B it for a question decomposition task, and I was wondering what the recommended settings are for each model (e.g., the default ones) to ensure a fair comparison. Are temperature = 1.0, top_p = 0.95, and top_k = 64 considered in general the optimal or standard settings for these models?

Thank you in advance!

pannaga10

Google org Nov 11, 2025

I believe you can achieve consistent and well-structured question decomposition task, when using temperature between 0.3 and 0.5 . The lower temperature range promotes the necessary logical thinking over creativity. Simultaneously, a top_p of 0.9 and a top_k around 40 introduce a controlled level of vocabulary diversity helping the model handle various question formats while keeping the decomposition patterns relevant. You can use these settings as a solid foundation for experimentation and can be fine-tuned based on your specific output requirements. Also try to ensure both Gemma 2B and Gemma 7B use these exact same settings for fair comparison.

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