Instructions to use essobi/functiongemma-mobile-actions-v6-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use essobi/functiongemma-mobile-actions-v6-16bit with Transformers:

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

pipe = pipeline("text-generation", model="essobi/functiongemma-mobile-actions-v6-16bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("essobi/functiongemma-mobile-actions-v6-16bit")
model = AutoModelForCausalLM.from_pretrained("essobi/functiongemma-mobile-actions-v6-16bit")
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

vLLM

How to use essobi/functiongemma-mobile-actions-v6-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "essobi/functiongemma-mobile-actions-v6-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "essobi/functiongemma-mobile-actions-v6-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/essobi/functiongemma-mobile-actions-v6-16bit

SGLang

How to use essobi/functiongemma-mobile-actions-v6-16bit 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 "essobi/functiongemma-mobile-actions-v6-16bit" \
    --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": "essobi/functiongemma-mobile-actions-v6-16bit",
		"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 "essobi/functiongemma-mobile-actions-v6-16bit" \
        --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": "essobi/functiongemma-mobile-actions-v6-16bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use essobi/functiongemma-mobile-actions-v6-16bit 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 essobi/functiongemma-mobile-actions-v6-16bit 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 essobi/functiongemma-mobile-actions-v6-16bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for essobi/functiongemma-mobile-actions-v6-16bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="essobi/functiongemma-mobile-actions-v6-16bit",
    max_seq_length=2048,
)

Docker Model Runner
How to use essobi/functiongemma-mobile-actions-v6-16bit with Docker Model Runner:
```
docker model run hf.co/essobi/functiongemma-mobile-actions-v6-16bit
```

FunctionGemma Mobile Actions v6

A fine-tuned version of FunctionGemma 270M optimized for mobile device function calling. This model excels at understanding natural language commands and mapping them to structured function calls for common mobile actions.

Model Description

Base Model: google/gemma-3-1b-it (270M parameters)
Fine-tuning Method: LoRA (r=128, alpha=128)
Training Data: google/mobile-actions + synthetic augmentation
Optimized For: Mobile assistant function calling

Supported Functions

Function	Description	Example Input
`set_alarm`	Set alarms	"Wake me up at 7am"
`create_reminder`	Create reminders	"Remind me to buy milk"
`set_timer`	Set countdown timers	"Timer for 10 minutes"
`make_call`	Make phone calls	"Call Mom"
`send_message`	Send text messages	"Text John I'm running late"
`create_calendar_event`	Schedule events	"Schedule meeting at 3pm"
`play_music`	Play music	"Play some jazz"
`get_weather`	Get weather info	"What's the weather like?"
`open_app`	Open applications	"Open the camera"
`navigate`	Get directions	"Navigate to the airport"
`set_volume`	Adjust volume	"Turn the volume up"
`calculator`	Math calculations	"What's 15 times 23?"

Usage

from vllm import LLM, SamplingParams

llm = LLM(model="essobi/functiongemma-mobile-actions-v6-16bit", trust_remote_code=True)

# See full documentation for prompt format and tool definitions

Output Format

<start_function_call>call:function_name{param1:<escape>value1<escape>,param2:<escape>value2<escape>}<end_function_call>

License

This model is released under the Gemma License.

Downloads last month: 2

Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for essobi/functiongemma-mobile-actions-v6-16bit

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Finetuned

(545)

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functiongemma-mobile-actions-v6-16bit