README.md

metadata

language:
  - en
license: apache-2.0
base_model: google/functiongemma-270m-it
tags:
  - function-calling
  - mobile-actions
  - on-device
  - litertlm
  - edge-ai
  - android
  - gemma3
library_name: transformers
pipeline_tag: text-generation

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

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

Or use 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:
   • "Turn on flashlight"
   • "Create contact John Smith"
   • "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

• Google AI Edge Gallery GitHub
• FunctionGemma Documentation
• LiteRT-LM Runtime
• Original Base Model

📞 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.