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

Libraries

How to use google/shieldgemma-2b with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/google/shieldgemma-2b

SGLang

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

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

Suggestion: NaN logits when padding used

#20

by WillBankes - opened Aug 29, 2024

Discussion

WillBankes

Aug 29, 2024

Hi, this is a really interesting model. Had fun playing around with it.

I've come across the following issue which I thought would be good to raise here. The nan issues described in this thread: https://github.com/huggingface/transformers/issues/32390 is also an issue with this model when the inputs are padded.

I found updating the code example from the model card, changing the torch.bfloat16 to torch.float16 fixed this issue for me.

GopiUppari

Google org Sep 3, 2024

Hi @WillBankes ,

I executed the code that was reported to cause NaN issues when using padding, as described in this GitHub thread https://github.com/huggingface/transformers/issues/32390 . However, I didn't encounter the NaN logits issue with the model google/shieldgemma-2b. You can refer to the detailed execution in this Colab notebook https://colab.research.google.com/gist/Gopi-Uppari/ffe907c215f0ebfdfb16e1f173c54942/nan-logits-when-padding-used.ipynb .

Thank you.

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