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
Local Apps Settings

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
```

Conversion to onnx

#29

by Parma7876 - opened Aug 13, 2024

Discussion

Parma7876

Aug 13, 2024

Conversion to onnx is not supported by ORTModelxxx .
Can support of architecture can be provided for gemma2 models.
Plz tell me how i can convert alternately to onnx to run on device model

lkv

Google org Nov 6, 2024

Hi @Parma7876 , Sorry for late response, If you want converted ONNX model to be compatible with a certain ONNX version, please specify the target_opset parameter upon invoking the convert function and Gemma2 model can be converted to TensorFlow's SavedModel format, you can then use tf2onnx to convert it to ONNX. Kindly refer this link for more information. Thank you.

Parma7876

Nov 6, 2024

@lkv Thanku for the response ,i was trying to export gemma2 2b to onnxx using Transformer library ORTModelxxx but it did not convert then i tried using torch.onnx.export and it also failed to export to onnx.
Your suggesting is to convert huggingface gemma2modelforcasuallm which is a pytorch model to convert it to tensorflow saved model and then convert it to onnx.i will definitely try but could u plz tell me how i can convert gemma2modelforcasuallm to tensorflow model first as i can see tensorflow model is not supported according to huggingface documentation.
Thanku in advance.

lkv

Google org Nov 19, 2024

Could you please refer this link for more information. Thank you.

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