This is a fine-tuned version of google/functiongemma-270m-it specialized for mobile device function calling tasks. The model has been trained on the Google Mobile Actions dataset to reliably execute phone commands through function calling.

Model Details

Base Model: google/functiongemma-270m-it (270M parameters)
Fine-tuning Method: Supervised Fine-Tuning (SFT) with TRL
Task: Function calling for mobile device actions
Language: English
License: Apache 2.0

Supported Functions

The model can call the following 7 mobile device functions:

turn_on_flashlight - Turns the flashlight on
turn_off_flashlight - Turns the flashlight off
open_wifi_settings - Opens the Wi-Fi settings
send_email - Sends an email (parameters: to, subject, body)
create_calendar_event - Creates a new calendar event (parameters: title, datetime)
show_map - Shows a location on the map (parameters: query)
create_contact - Creates a contact in the phone's contact list (parameters: first_name, last_name, email, phone_number)

Training

Dataset

Name: Google Mobile Actions Dataset
Size: ~9,654 examples
Split: ~8,693 training samples, ~961 evaluation samples

Training Hardware

GPU: NVIDIA with ~7.65 GB VRAM
Precision: BF16 mixed precision training

Usage

Installation

pip install transformers torch

Basic Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "abubakar-siddik/functiongemma-270m-it-mobile-actions"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

# Define available tools
tools = [
    {
        "function": {
            "name": "create_calendar_event",
            "description": "Creates a new calendar event.",
            "parameters": {
                "type": "OBJECT",
                "properties": {
                    "title": {"type": "STRING", "description": "The title of the event."},
                    "datetime": {"type": "STRING", "description": "The date and time in YYYY-MM-DDTHH:MM:SS format."}
                },
                "required": ["title", "datetime"]
            }
        }
    }
    # Add other tools as needed...
]

# Create messages
messages = [
    {
        "role": "developer",
        "content": "Current date and time: 2024-11-15T05:59:00. You are a model that can do function calling."
    },
    {
        "role": "user",
        "content": "Schedule a team meeting tomorrow at 4pm"
    }
]

# Generate response
prompt = tokenizer.apply_chat_template(
    messages,
    tools=tools,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)

Example Output

Input: "Schedule a team meeting tomorrow at 4pm"

Output (function call):

<start_function_call>call:create_calendar_event{title:<escape>team meeting<escape>,datetime:<escape>2024-11-16T16:00:00<escape>}<end_function_call>

Model Performance

The fine-tuned model shows significant improvement over the base FunctionGemma 270M model for mobile action tasks:

Base Model: Struggles with correct function calling on mobile-specific prompts
Fine-tuned Model: Reliable function calling with proper parameter extraction

Deployment

On-Device Deployment

This model can be converted to .litertlm format for deployment on Android devices using Google AI Edge tools.

Limitations

Model is fine-tuned specifically for the 7 mobile actions listed above
Performance may vary on unseen function combinations
Temporal reasoning may be limited (e.g., "tomorrow at 4pm" requires date context)
Model is small (270M parameters) and may have limitations on complex queries

Ethical Considerations

This model should be used responsibly:

Only execute function calls after user confirmation
Implement proper authentication and authorization checks
Be transparent about AI-generated actions
Respect user privacy for contact and calendar operations

Acknowledgments

Base model by Google
Training dataset: Google Mobile Actions
Training framework: Hugging Face TRL

Model Card Version: 1.0.0 Last Updated: 2025-12-29