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
```

functiongemma-smarthome / README.md

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	---
	base_model: google/functiongemma-270m-it
	library_name: transformers
	model_name: final_model_stable
	tags:
	- generated_from_trainer
	- sft
	- trl
	licence: license
	---

	# Model Card for functiongemma-smarthome

	[Dataset](https://huggingface.co/datasets/altaidevorg/smarthome-tool-calling-tiny) \| [Notebook](https://github.com/altaidevorg/functiongemma-afterimage-demo/blob/main/fgemma-training.ipynb) \| [Demo Video](https://www.youtube.com/watch?v=TJxtyrWSgo0)

	This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) on a [custom tool-calling dataset](https://huggingface.co/datasets/altaidevorg/smarthome-tool-calling-tiny) synthetically generated with Afterimage, our purpose-built synthetic dataset generation engine.

	See the [demo video](https://www.youtube.com/watch?v=TJxtyrWSgo0).

	## 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.