Instructions to use alpha-ai/gemma-3N-E2B-Just-Chatty with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alpha-ai/gemma-3N-E2B-Just-Chatty with Transformers:

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

pipe = pipeline("image-text-to-text", model="alpha-ai/gemma-3N-E2B-Just-Chatty")
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("alpha-ai/gemma-3N-E2B-Just-Chatty")
model = AutoModelForMultimodalLM.from_pretrained("alpha-ai/gemma-3N-E2B-Just-Chatty")
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 alpha-ai/gemma-3N-E2B-Just-Chatty with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alpha-ai/gemma-3N-E2B-Just-Chatty"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpha-ai/gemma-3N-E2B-Just-Chatty",
		"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/alpha-ai/gemma-3N-E2B-Just-Chatty

SGLang

How to use alpha-ai/gemma-3N-E2B-Just-Chatty 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 "alpha-ai/gemma-3N-E2B-Just-Chatty" \
    --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": "alpha-ai/gemma-3N-E2B-Just-Chatty",
		"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 "alpha-ai/gemma-3N-E2B-Just-Chatty" \
        --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": "alpha-ai/gemma-3N-E2B-Just-Chatty",
		"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 alpha-ai/gemma-3N-E2B-Just-Chatty 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 alpha-ai/gemma-3N-E2B-Just-Chatty 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 alpha-ai/gemma-3N-E2B-Just-Chatty to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alpha-ai/gemma-3N-E2B-Just-Chatty to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="alpha-ai/gemma-3N-E2B-Just-Chatty",
    max_seq_length=2048,
)

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

Uploaded model: gemma-3N-Just-Chatty

Developed by: Alpha AI

License: apache-2.0

Finetuned from model: unsloth/gemma-3n-E2B-it

Format: Float16 (safetensors) + Convert to GGUF for local deployments

Overview

gemma-3N-Just-Chatty is a fine-tuned Gemma-3N conversational model, optimized for human-level fluency, higher engagement, and emotional intelligence.

Training Dataset:
- Public split: Human-Like-DPO-Dataset
- Proprietary split: Alpha AI “Friendly-Empathy” corpus (private)
Samples: 10 884 (public) + 6 100 (private)
Topics: 256 + everyday social chats
Alignment: Conversational SFT + DPO
Framework: Unsloth + Hugging Face TRL

This model excels at delivering chatty, context-aware, and personable dialogue for real-world applications, with GGUF-f16 for efficient local/edge inference. We do not provide the GGUF version just yet as there seems to some compatibility issues with Unsloth and HuggingFace. You can copy this repo and create your own version of GGUF using "https://huggingface.co/spaces/ggml-org/gguf-my-repo"

What's New in Just-Chatty?

Blends open-source dialogues with proprietary, friendliness-focused data to encourage warmth and empathy.
Dual-response structure: Every prompt has both a casual “human-like” answer and a more formal “AI” answer for preference optimization.
Strict adherence to Gemma-3N chat templates ensures robust multi-turn conversations.

Model Details

Attribute	Value
Base Model	unsloth/gemma-3n-E2B-it
Type	Causal Language Model (text generation)
Fine-tuned By	Alpha AI
Training Framework	Unsloth + HF TRL
Precision	16-bit float (f16)
Context Length	32 000 tokens
Languages	English (en)

Suggested generation parameters: temperature=1.0, top_k=64, top_p=0.95

Example Dataset Structure

Each training sample includes:

Conversational Prompt: Natural, engaging question.
Human-Like Response: Relatable, chatty, emotionally nuanced answer.
Formal Response: Professional, structured answer for DPO contrast.

Use Cases

Conversational AI: Chatbots, customer support, digital companions.
Local/Edge Deployments: Runs efficiently on laptops, servers, and edge devices via GGUF-f16.
Research: Ideal for DPO and SFT studies in alignment, empathy modeling, or instruction following.

Performance Highlights

Empathy & Engagement: Responds with warmth and situational awareness, avoiding a “robotic” tone.
Efficiency: Maintains extended, coherent conversations with low latency.
Balanced Realism: Produces lifelike small talk while respecting safety filters.

Limitations and Responsible Use

Despite careful alignment, the model may reproduce social biases or inaccuracies present in training data. Always audit output before deploying in sensitive settings, and supervise use in production or critical-decision contexts.

License

Released under the Apache-2.0 license.

Acknowledgments

Thanks to the Unsloth team, Hugging Face TRL contributors, and the creators of the Human-Like-DPO-Dataset for advancing open, natural conversational AI.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("alpha-ai/gemma-3N-Just-Chatty", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("alpha-ai/gemma-3N-Just-Chatty")

prompt = "user\nWhat are some fun DIY projects for a rainy day?\nmodel\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))

Website: https://www.alphaai.biz