litert-community/FunctionGemma_270M_Mobile_Actions

FunctionGemma 270M for Mobile Actions

This model is a fine-tuned version of google/functiongemma-270m-it specialized for mobile assistant actions. It has been trained on the google/mobile-actions dataset to perform structured function calling for common mobile device tasks.

Model Description

Base Model: google/functiongemma-270m-it - A 270M parameter instruction-tuned model from Google's FunctionGemma family, designed for function calling tasks.

Specialization: Mobile assistant actions including:

• Calendar event management
• Email composition and sending
• Contact creation
• Flashlight control
• Wi-Fi settings navigation
• Map location display

Training Objective: The model learns to emit structured function calls in the format call:<function_name>{arg1:value1,arg2:value2,...} instead of natural language responses.

Supported Functions

The model is optimized to call these mobile action functions:

1. turn_on_flashlight() - Turns the device flashlight on
2. turn_off_flashlight() - Turns the device flashlight off
3. create_contact(first_name, last_name, phone_number?, email?) - Creates a new contact
4. send_email(to, subject, body?) - Sends an email to a recipient
5. show_map(query) - Displays a location on the map by name, business, or address
6. open_wifi_settings() - Opens the Wi-Fi settings screen
7. create_calendar_event(title, datetime) - Creates a calendar event (datetime in ISO format: YYYY-MM-DDTHH:MM:SS)

Training Details

Training Data

• Dataset: google/mobile-actions
• Format: JSONL with prompt-completion pairs
• Splits:
  • Training set: examples with "metadata": "train"
  • Evaluation set: examples with "metadata": "eval"
• Preprocessing: Converted to TRL prompt-completion format with completion_only_loss=True

Training Procedure

Fine-tuned using Hugging Face TRL (Transformer Reinforcement Learning) with the SFTTrainer.

Training Configuration:

• Epochs: 4
• Batch size: 8 per device
• Gradient accumulation steps: 4
• Learning rate: 5e-5
• Scheduler: Cosine
• Max sequence length: 997 tokens (based on longest example: 897 tokens)
• Optimizer: AdamW (fused)
• Precision: bfloat16
• Gradient checkpointing: Enabled
• Completion only loss: True (trains only on model outputs, not prompts)

Training Infrastructure:

• Hardware: Google Colab A100 GPU
• Training time: ~20 minutes for 2 epochs
• Library versions: transformers==4.57.1, trl==0.25.1, datasets==4.4.1

Training Results

Final metrics after 2 epochs:

Step	Training Loss	Validation Loss	Mean Token Accuracy
500	0.008800	0.013452	0.996691

The model achieved 99.67% token-level accuracy on the validation set, showing significant improvement over the base model's mobile action capabilities.

Intended Use

This model is designed for:

• Mobile AI assistants that need to execute device actions based on user requests
• Voice-controlled mobile applications
• Conversational agents that interact with mobile device features
• On-device AI applications (can be converted to .litertlm format for deployment)

How to Use

Basic Inference

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import json

# Load model and tokenizer
model_id = "jprtr/google_mobile_actions"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    attn_implementation="eager",
    torch_dtype="auto",
)

# Create pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Define the tools (function schemas)
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"],
            },
        }
    },
    {
        "function": {
            "name": "send_email",
            "description": "Sends an email.",
            "parameters": {
                "type": "OBJECT",
                "properties": {
                    "to": {"type": "STRING", "description": "The recipient email address."},
                    "subject": {"type": "STRING", "description": "The email subject."},
                    "body": {"type": "STRING", "description": "The email body."},
                },
                "required": ["to", "subject"],
            },
        }
    },
    # ... add other function definitions
]

# Create messages
messages = [
    {
        "role": "developer",
        "content": (
            "Current date and time given in YYYY-MM-DDTHH:MM:SS format: 2025-07-10T19:06:29\n"
            "Day of week is Thursday\n"
            "You are a model that can do function calling with the following functions\n"
        ),
    },
    {
        "role": "user",
        "content": 'Schedule a "team meeting" tomorrow at 4pm.',
    },
]

# Apply chat template
prompt = tokenizer.apply_chat_template(
    messages,
    tools=tools,
    tokenize=False,
    add_generation_prompt=True,
)

# Generate
output = pipe(prompt, max_new_tokens=200)[0]["generated_text"][len(prompt):].strip()
print("Model output:", output)
# Example output: call:create_calendar_event{datetime:2025-07-11T16:00:00,title:team meeting}

Parsing Function Calls

The model outputs function calls in a simple format:

call:<function_name>{arg1:value1,arg2:value2,...}

For multiple function calls, they appear sequentially:

call:create_calendar_event{datetime:2025-07-15T10:30:00,title:Dental Checkup}
call:send_email{to:[email protected],subject:Appointment,body:See you there!}

You can parse these by:

1. Splitting on call: to identify individual function calls
2. Extracting the function name (text before {)
3. Parsing the arguments block (content within {})

Evaluation

The model was evaluated on the held-out test set from the mobile-actions dataset. Evaluation metrics compare exact string matching of the model's function call outputs against ground truth labels.

Key Observations:

• The base FunctionGemma 270M model often fails to call appropriate functions for mobile actions
• After fine-tuning, the model reliably generates correct function calls with proper argument formatting
• Token-level accuracy on the validation set: 99.67%

Limitations

• The model is specialized for the 7 mobile action functions listed above and may not generalize well to other function calling tasks
• Date/time parsing relies on context provided in the developer message (current date/time must be specified)
• The model outputs may occasionally include variations in argument formatting that are semantically correct but don't exactly match the expected format
• This is a 270M parameter model, so while efficient for mobile deployment, it may have lower accuracy than larger models

On-Device Deployment

The model can be converted to .litertlm format for on-device deployment using ai-edge-torch. See the training notebook for conversion instructions.

The converted model can be deployed on:

• Android devices via Google AI Edge
• AI Edge Gallery app

Training Notebook

For full training details, hyperparameter tuning, and evaluation, see the original Colab notebook: Finetune FunctionGemma 270M for Mobile Actions

Citation

If you use this model, please cite the original FunctionGemma paper and the Google Mobile Actions dataset:

@misc{functiongemma2024,
  title={FunctionGemma: Function Calling for Gemma Models},
  author={Google},
  year={2024},
  url={https://huggingface.co/google/functiongemma-270m-it}
}

License

This model is released under the Gemma license. See the Gemma Terms of Use for details.