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Yagna1/functiongemma-270m-mobile-actions

sourceHugging Faceapache-2.0updated 9mo agoView on Hugging Face

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Model Card

FunctionGemma 270M - Mobile Actions (LiteRT-LM Ready)

A fine-tuned FunctionGemma 270M model optimized for on-device function calling on Android devices using Google AI Edge Gallery and LiteRT-LM runtime.

🎯 Features

—✅ Ready-to-use: Pre-converted .litertlm format for immediate deployment
—✅ On-device function calling: Runs entirely on Android devices without internet
—✅ Optimized: INT8 quantization (~271 MB) for efficient mobile deployment
—✅ Mobile Actions: Supports 6 native Android functions
—✅ Low latency: Optimized with extended KV cache (1024 tokens)

📱 Supported Mobile Actions

The model can execute the following Android functions via natural language:

Function	Example Prompt
Flashlight	"Turn on the flashlight"
Contacts	"Create a contact for John Doe with phone 555-1234"
Email	"Send email to john@example.com"
Maps	"Show Times Square on the map"
WiFi	"Turn off WiFi"
Calendar	"Create a calendar event for Team Meeting tomorrow at 2 PM"

🚀 Quick Start

Download the Model

bash

wget https://huggingface.co/Yagna1/functiongemma-270m-mobile-actions/resolve/main/mobile-actions_q8_ekv1024.litertlm

Or use Python:

python

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="Yagna1/functiongemma-270m-mobile-actions",
    filename="mobile-actions_q8_ekv1024.litertlm"
)
print(f"Downloaded to: {model_path}")

Use in Google AI Edge Gallery App

1.Install the Google AI Edge Gallery Android app
2.Import the mobile-actions_q8_ekv1024.litertlm file into the app
3.Navigate to "Mobile Actions" feature
4.Test with natural language prompts like:
5."Turn on flashlight"
6."Create contact John Smith"
7."Show Central Park on map"

🏗️ Model Architecture

—Base Model: google/functiongemma-270m-it
—Architecture: Gemma 3 (270M parameters)
—Quantization: INT8 (Dynamic)
—KV Cache: Extended to 1024 tokens for longer conversations
—Runtime: LiteRT-LM (Google's on-device inference engine)

📊 Model Details

Property	Value
Parameters	270M
Quantization	INT8
Model Size	271 MB
Format	`.litertlm` (LiteRT-LM)
Context Length	1024 tokens
Target Device	Android (ARM)

🔧 Function Calling Format

The model uses LiteRT-LM's native function calling format:

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

Example outputs:

User: "Turn on the flashlight" Model: <start_function_call>call:enableFlashlight{}<end_function_call>

User: "Create contact John Doe with phone 555-1234" Model: <start_function_call>call:createContact{contactName:John Doe,phoneNumber:555-1234}<end_function_call>

📚 Training Details

This model was fine-tuned on synthetic Mobile Actions data designed to match LiteRT-LM's expected function calling format. The training focused on:

—Natural language → function call mapping
—Parameter extraction from user queries
—Handling edge cases and variations
—Multi-turn conversation support

⚠️ Limitations

—Limited to 6 pre-defined Android functions
—English language only
—Requires Android device with ARMv8-A or newer
—May not handle complex multi-step actions
—Function parameters must match expected schema

🤝 Credits

Original Model: This is a mirror/re-upload of JackJ1/functiongemma-270m-it-mobile-actions-litertlm

Thanks to:

—JackJ1 for the original fine-tuning work
—Google for FunctionGemma base model and LiteRT-LM runtime
—Google AI Edge Team for the Gallery app and tools

📄 License

Apache 2.0 (same as base FunctionGemma model)

🔗 Resources

📞 Contact

For issues or questions about this model mirror, please open an issue on the repository.

Note: This model is specifically formatted for the Google AI Edge Gallery app and requires the LiteRT-LM runtime. For general-purpose inference, use the base model or convert to standard formats.