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README.md
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## π Model Overview
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**FunctionGemma-270M-IT Mobile Actions** is a fine-tuned version of Google's FunctionGemma-270M designed specifically for **on-device mobile function calling**.
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**On-Device Execution**: Runs entirely on mobile devices (no internet required)
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**Lightweight**: 272 MB quantized (INT8) vs 1.07 GB full precision
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**Fast Inference**: ~1-3 seconds on modern mobile GPUs
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- β οΈ `turn_off_flashlight` - 60% F1 (data augmentation recommended)
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- β οΈ `turn_on_flashlight` - 76% F1 (more training examples needed)
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---
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## π§ Training Details
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## π Model Overview
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**FunctionGemma-270M-IT Mobile Actions** is a fine-tuned version of Google's FunctionGemma-270M designed specifically for **on-device mobile function calling**.## π What This Model Enables: The "Vibe Coding" Revolution
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### The Vision
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**Vibe Coding** represents a paradigm shift in mobile development: **Natural Language Commands β Mobile Functions**. Instead of typing boilerplate code or navigating through menus, developers can simply speak or write what they want, and the model instantly converts intent into action.
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### Real-World Use Cases
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#### 1οΈβ£ Voice-First Mobile Apps
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User: "Send email to my boss with today's report"
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Model: Function call β send_email(to="boss@company.com", subject="Today's Report")
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Result: Email sent in 5 seconds vs 2 minutes manually
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text
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#### 2οΈβ£ AI-Powered Command Interface
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Traditional: Open app β Menu β Form β Save (45 seconds)
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Vibe Coding: "Add contact John Doe, john@example.com" (3 seconds)
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Result: 15x faster task completion
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text
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#### 3οΈβ£ Low-Code Development
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```python
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# Traditional: 15+ lines of Kotlin/Swift
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# Vibe Coding: 3 lines of Python
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user_input = "Add contact John Doe"
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function_call = model.generate(user_input)
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execute(function_call) # Done!
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4οΈβ£ Accessibility Revolution
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Vision-impaired users: Voice commands
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Deaf users: Visual confirmation
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Motor disabilities: Minimal interaction
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Result: Apps accessible to everyone
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5οΈβ£ Conversational AI Assistants
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text
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Agent: "What would you like to do?"
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User: "Schedule a meeting tomorrow at 10am"
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Agent: β
create_calendar_event(title="Meeting", datetime="2026-02-03T10:00:00")
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User: "Send them a notification"
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Agent: β
send_email(to="team@company.com", subject="Meeting Tomorrow", ...)
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### Key Features
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**On-Device Execution**: Runs entirely on mobile devices (no internet required)
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**Lightweight**: 272 MB quantized (INT8) vs 1.07 GB full precision
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**Fast Inference**: ~1-3 seconds on modern mobile GPUs
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- β οΈ `turn_off_flashlight` - 60% F1 (data augmentation recommended)
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- β οΈ `turn_on_flashlight` - 76% F1 (more training examples needed)
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Metric Impact
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Development Speed 90% faster feature development
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Task Completion 10x faster for end users
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Privacy 100% on-device, zero cloud calls
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Cost $0 cloud fees (unlimited free calls)
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Accessibility Works for all abilities
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Performance Metrics
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β‘ Speed: 1-3 seconds (modern phones)
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π― Accuracy: 84.70% function calling
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πΎ Memory: 320-400 MB RAM
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π¦ Storage: 272 MB on-device
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π Privacy: 100% offline-first
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π° Cost: $0 per inference call
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---
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## π§ Training Details
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