Instructions to use google/gemma-3n-E2B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-3n-E2B-it with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/gemma-3n-E2B-it")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("google/gemma-3n-E2B-it")
model = AutoModelForImageTextToText.from_pretrained("google/gemma-3n-E2B-it")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use google/gemma-3n-E2B-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-3n-E2B-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-3n-E2B-it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/google/gemma-3n-E2B-it

SGLang

How to use google/gemma-3n-E2B-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-3n-E2B-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-3n-E2B-it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-3n-E2B-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-3n-E2B-it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use google/gemma-3n-E2B-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-3n-E2B-it
```

Question about NaN logprob

#23

by hzhiqi - opened Feb 7

Discussion

hzhiqi

Feb 7

•

edited Feb 7

Hi,

Thanks for the model. I have an issue when PEFT fine-tuning it with TRL GRPOTrainer + vLLM.

First, TRL gives a bunch of warning messages like the following:

WARNING vllm_serve.py:413: Generated NaN logprob, token logprob 'Logprob(logprob=nan, rank=0, decoded_token='<pad>')' will be ignored

Then, it fails with the error message:

{'type': 'float_type', 'loc': ('response', 'logprobs', 7, 4092), 'msg': 'Input should be a valid number', 'input': None}

It seems the <pad> token does not have a valid log probability. I tried the following, but the error persists.

set prompt_logprobs = None in generation_kwargs of GRPOConfig
set tokenizer.pad_token = tokenizer.eos_token and model.config.pad_token_id = tokenizer.eos_token_id

Any suggestions? Thank you!

pannaga10

Google org Feb 10

Hi @hzhiqi
From the Gemma model’s perspective, the core issue is that is not a semantically valid token for probability estimation. This issue could be when the interation happens between GRPO (TRL) and vLLM. To understand better

Could you please confirm the exact versions of trl, vllm, and transformers you are using? There have been recent updates in TRL's integration with vLLM regarding how special tokens are handled and also different versions have different bugs and fixes for logprob handling.
Could you share the part of your code where you initialize GRPOConfig and the GRPOTrainer Specifically, I want to see how you're passing the vllm_backend_config.

Thanks

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