---
language:
- en
license: apache-2.0
tags:
- function-calling
- music
- gemma
- peft
- lora
base_model: google/functiongemma-270m-it
---

# FunctionGemma Music Assistant (2-Function)

A fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) for music playback control.
This model supports 2 core music functions with 100% accuracy.

## Model Description

This is a **LoRA-adapted** FunctionGemma model trained specifically for music function calling.
The model generates function calls in the FunctionGemma format for controlling music playback.

**Training Details:**
- Base Model: google/functiongemma-270m-it (270M parameters)
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Parameters Trained: 3.8M (1.40% of total)
- Training Examples: 60 (30 per function)
- Training Time: ~1 minute
- Accuracy: 100% (5/5 test cases)

## Supported Functions

### 1. play_song
Play a specific song by name or artist.

**Parameters:**
- `song_name` (required): Name of the song to play
- `artist` (optional): Artist name
- `album` (optional): Album name

**Examples:**
- "Play Bohemian Rhapsody"
- "Play Imagine by John Lennon"
- "I want to hear Wonderwall"

### 2. playback_control
Control music playback (pause, resume, skip).

**Parameters:**
- `action` (required): One of: play, pause, skip, next, previous, stop, resume

**Examples:**
- "Pause"
- "Resume"
- "Skip to next song"
- "Stop the music"

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained(
    "google/functiongemma-270m-it",
    torch_dtype=torch.float16,
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("google/functiongemma-270m-it")

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "your-username/music-2func")

# Define functions
FUNCTIONS = [
    {
        "type": "function",
        "function": {
            "name": "play_song",
            "description": "Play a specific song by name or artist",
            "parameters": {
                "type": "object",
                "properties": {
                    "song_name": {"type": "string", "description": "Name of the song to play"},
                    "artist": {"type": "string", "description": "Artist name (optional)"},
                    "album": {"type": "string", "description": "Album name (optional)"}
                },
                "required": ["song_name"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "playback_control",
            "description": "Control music playback",
            "parameters": {
                "type": "object",
                "properties": {
                    "action": {
                        "type": "string",
                        "enum": ["play", "pause", "skip", "next", "previous", "stop", "resume"]
                    }
                },
                "required": ["action"]
            }
        }
    }
]

# Generate function call
user_input = "Play Bohemian Rhapsody"

messages = [{"role": "user", "content": user_input}]

prompt = tokenizer.apply_chat_template(
    messages,
    tools=FUNCTIONS,
    add_generation_prompt=True,
    tokenize=False
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=0.1,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=False)
print(response)
```

**Expected Output:**
```
<start_function_call>call:play_song{song_name:<escape>Bohemian Rhapsody<escape>}<end_function_call>
```

## Test Results

| Test Input | Expected Function | Result |
|-----------|------------------|--------|
| "Play Bohemian Rhapsody" | `play_song` | ✅ Pass |
| "Pause the music" | `playback_control` | ✅ Pass |
| "Skip to next song" | `playback_control` | ✅ Pass |
| "Play Wonderwall" | `play_song` | ✅ Pass |
| "Resume" | `playback_control` | ✅ Pass |

**Success Rate: 100% (5/5 tests)**

## Training Methodology

This model was trained using a **gradual scaling approach** to avoid cognitive overload:

1. Started with 2 functions (play_song, playback_control)
2. 30 examples per function covering diverse phrasings
3. Correct format: Pass dict directly to `apply_chat_template` (NOT `json.dumps()`)

### Key Learnings

1. **Critical Bug Fixed**: Must pass arguments as dict, not `json.dumps(arguments)`
2. **Cognitive Overload**: Training with 18 functions failed (0% accuracy), but 2 functions achieved 100%
3. **Gradual Scaling**: Recommended path is 2→4→8→18 functions

## Limitations

- Only supports 2 functions (play_song and playback_control)
- Trained on English language only
- Best performance with clear, direct commands
- **Not compatible with Ollama** (Ollama doesn't support FunctionGemma's dynamic tool schema)

## Future Work

- Scale to 4 functions (add search_music, create_playlist)
- Scale to 8 functions (add volume control, queue management)
- Eventually scale to full 18-function music system

## Citation

```bibtex
@misc{music-2func-2026,
  title={FunctionGemma Music Assistant (2-Function)},
  author={Your Name},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/your-username/music-2func}}
}
```

## License

Apache 2.0 (inherited from base model)

## Base Model

This model is based on [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it).