Instructions to use google/datagemma-rag-27b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/datagemma-rag-27b-it with Transformers:

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

pipe = pipeline("text-generation", model="google/datagemma-rag-27b-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/datagemma-rag-27b-it")
model = AutoModelForCausalLM.from_pretrained("google/datagemma-rag-27b-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use google/datagemma-rag-27b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/datagemma-rag-27b-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/datagemma-rag-27b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/google/datagemma-rag-27b-it

SGLang

How to use google/datagemma-rag-27b-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/datagemma-rag-27b-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/datagemma-rag-27b-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/datagemma-rag-27b-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/datagemma-rag-27b-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use google/datagemma-rag-27b-it with Docker Model Runner:
```
docker model run hf.co/google/datagemma-rag-27b-it
```

Data Gemma Access

#10

by sam2212 - opened Dec 31, 2024

Discussion

sam2212

Dec 31, 2024

Hello, I would like access to the datagemma model. May my request be approved. Thank you.

lkv

Google org Jan 2, 2025

Hi @sam2212 , Could you please confirm if access request is approved. feel free to we can close this issue.

Thank you.

Shub500

Jan 2, 2025

Hello, the access request is still pending.

lkv

Google org Jan 2, 2025

Follow the link provided below and complete the steps to gain access to the “Google Gemma models on HuggingFace”.
https://www.kaggle.com/models/google/gemma-2/license/consent

Click on “Verify via Hugging Face” and also click “continue with Hugging Face”.

By following above step then will get as

Click on "Authorize," which will redirect you to the Kaggle Gemma Consent Form page. Fill out the required fields, accept the terms, and you will receive access to the Google Gemma models on HuggingFace in less than a minute.

Kindly try and let me know if you are facing issues. Thank you.

Shub500

Jan 2, 2025

Yes it worked thank you very much.

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