Instructions to use p2kalita/gemma-3n-E4B-it-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use p2kalita/gemma-3n-E4B-it-finetuned with Transformers:

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

pipe = pipeline("image-text-to-text", model="p2kalita/gemma-3n-E4B-it-finetuned")
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("p2kalita/gemma-3n-E4B-it-finetuned")
model = AutoModelForMultimodalLM.from_pretrained("p2kalita/gemma-3n-E4B-it-finetuned", device_map="auto")
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 p2kalita/gemma-3n-E4B-it-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "p2kalita/gemma-3n-E4B-it-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "p2kalita/gemma-3n-E4B-it-finetuned",
		"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/p2kalita/gemma-3n-E4B-it-finetuned

SGLang

How to use p2kalita/gemma-3n-E4B-it-finetuned 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 "p2kalita/gemma-3n-E4B-it-finetuned" \
    --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": "p2kalita/gemma-3n-E4B-it-finetuned",
		"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 "p2kalita/gemma-3n-E4B-it-finetuned" \
        --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": "p2kalita/gemma-3n-E4B-it-finetuned",
		"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 Desktop
Docker Model Runner
How to use p2kalita/gemma-3n-E4B-it-finetuned with Docker Model Runner:
```
docker model run hf.co/p2kalita/gemma-3n-E4B-it-finetuned
```

🚀 Finetuned Gemma 3n Model

🧠 Developed by: p2kalita
📄 License: Apache-2.0
🔧 Finetuned from: unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit

This is a finetuned version of the Gemma 3n model using Unsloth, built for fast, efficient, and instruction-aligned text generation. Training was completed in 7 epochs on Google Colab A100 GPU, utilizing 4-bit quantization for optimal performance.

🏗️ Training Details

Framework: PyTorch + Hugging Face Transformers + TRL
Finetuning Engine: Unsloth
Precision: 4-bit (bnb)
Epochs: 7
GPU: A100 (via Google Colab)
LoRA / PEFT: Enabled
Training Time: ~ 2:52:02 (hh:mm:ss)

✨ Use Cases

Chatbots / Assistants
Instruction following
Educational tools
Question answering
Creative writing

🛠️ Inference Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("p2kalita/gemma-3n-E4B-it-finetuned")
tokenizer = AutoTokenizer.from_pretrained("p2kalita/gemma-3n-E4B-it-finetuned")

prompt = "Explain quantum mechanics simply."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This gemma3n model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 14

Safetensors

Model size

8B params

Tensor type

BF16

p2kalita
/

gemma-3n-E4B-it-finetuned

🚀 Finetuned Gemma 3n Model

🏗️ Training Details

✨ Use Cases

🛠️ Inference Example

Dataset used to train p2kalita/gemma-3n-E4B-it-finetuned

Space using p2kalita/gemma-3n-E4B-it-finetuned 1