Instructions to use altaidevorg/functiongemma-smarthome with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use altaidevorg/functiongemma-smarthome with Transformers:

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

pipe = pipeline("text-generation", model="altaidevorg/functiongemma-smarthome")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("altaidevorg/functiongemma-smarthome")
model = AutoModelForCausalLM.from_pretrained("altaidevorg/functiongemma-smarthome", device_map="auto")
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 Settings

vLLM

How to use altaidevorg/functiongemma-smarthome with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "altaidevorg/functiongemma-smarthome"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "altaidevorg/functiongemma-smarthome",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/altaidevorg/functiongemma-smarthome

SGLang

How to use altaidevorg/functiongemma-smarthome 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 "altaidevorg/functiongemma-smarthome" \
    --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": "altaidevorg/functiongemma-smarthome",
		"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 "altaidevorg/functiongemma-smarthome" \
        --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": "altaidevorg/functiongemma-smarthome",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use altaidevorg/functiongemma-smarthome with Docker Model Runner:
```
docker model run hf.co/altaidevorg/functiongemma-smarthome
```

Model Card for functiongemma-smarthome

Dataset | Notebook | Demo Video

This model is a fine-tuned version of google/functiongemma-270m-it on a custom tool-calling dataset synthetically generated with Afterimage, our purpose-built synthetic dataset generation engine.

See the demo video.

What is Afterimage?

Building custom Small Language Models (SLMs) starts with great data. Afterimage eliminates the tedious data preparation bottleneck by transforming your organization's unstructured documents into high-quality, LLM-ready Q&A sets, tool-calling datasets and/or other types of structured datasets automatically. It is highly customizable and and aimed at transforming enterprises' way of customizing LLMs.

About ALTAI

ALTAI is a secure, no-code platform that enables organizations to create, train, and deploy customized SLMs using their own internal documents. From "Letsearch" (RAG Engine) to on-premise deployment, we make LLM customization uncool again—simply effective. It can work 100% on-premise and requires 0 technical experience.

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Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for altaidevorg/functiongemma-smarthome

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A set of artifacts related to Afterimage • 2 items • Updated Jan 22