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

Libraries

How to use unsloth/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="unsloth/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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("unsloth/gemma-3n-E2B-it")
model = AutoModelForMultimodalLM.from_pretrained("unsloth/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 Settings

vLLM

How to use unsloth/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 "unsloth/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": "unsloth/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/unsloth/gemma-3n-E2B-it

SGLang

How to use unsloth/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 "unsloth/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": "unsloth/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 "unsloth/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": "unsloth/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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use unsloth/gemma-3n-E2B-it with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/gemma-3n-E2B-it to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/gemma-3n-E2B-it to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/gemma-3n-E2B-it to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="unsloth/gemma-3n-E2B-it",
    max_seq_length=2048,
)

Docker Model Runner
How to use unsloth/gemma-3n-E2B-it with Docker Model Runner:
```
docker model run hf.co/unsloth/gemma-3n-E2B-it
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Learn how to run & fine-tune Gemma 3n correctly - Read our Guide.

See our collection for all versions of Gemma 3n including GGUF, 4-bit & 16-bit formats.

Unsloth Dynamic 2.0 achieves SOTA accuracy & performance versus other quants.

✨ Gemma 3n Usage Guidelines

Currently only text is supported.
Ollama: ollama run hf.co/unsloth/gemma-3n-E4B-it:Q4_K_XL - auto-sets correct chat template and settings
Set temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0
Gemma 3n max tokens (context length): 32K. Gemma 3n chat template:

<bos><start_of_turn>user\nHello!<end_of_turn>\n<start_of_turn>model\nHey there!<end_of_turn>\n<start_of_turn>user\nWhat is 1+1?<end_of_turn>\n<start_of_turn>model\n

For complete detailed instructions, see our step-by-step guide.

🦥 Fine-tune Gemma 3n with Unsloth

Fine-tune Gemma 3n (4B) for free using our Google Colab notebook here!
Read our Blog about Gemma 3n support: unsloth.ai/blog/gemma-3n
View the rest of our notebooks in our docs here.

Unsloth supports	Free Notebooks	Performance	Memory use
Gemma-3n-E4B	▶️ Start on Colab	2x faster	80% less
GRPO with Gemma 3 (1B)	▶️ Start on Colab	2x faster	80% less
Gemma 3 (4B)	▶️ Start on Colab	2x faster	60% less
Qwen3 (14B)	▶️ Start on Colab	2x faster	60% less
DeepSeek-R1-0528-Qwen3-8B (14B)	▶️ Start on Colab	2x faster	80% less
Llama-3.2 (3B)	▶️ Start on Colab	2.4x faster	58% less