README.md

---
language:
  - en
license: mit
base_model: Qwen/Qwen3.5-7B-Instruct
tags:
  - music
  - guitar
  - piano
  - drums
  - vocals
  - music-theory
  - ear-training
  - songwriting
  - lora
  - peft
  - qwen
  - eq-adapter
  - matrix-corp
pipeline_tag: text-generation
library_name: transformers
model_type: touchgrass
---

# Touch Grass 🎵

**A Lightweight Music AI Assistant Fine-Tuned from Qwen3.5**

Touch Grass is a specialized music AI assistant built by fine-tuning Qwen3.5 models (3B and 7B variants) with music-specific capabilities. It understands guitar, piano, drums, vocals, music theory, ear training, songwriting, and production—with emotional intelligence to help musicians through frustration.

## 🌟 Features

- **Two Model Sizes**: TouchGrass-3B (CPU-friendly) and TouchGrass-7B (GPU-enhanced)
- **Music Tokenizer Extension**: Adds 21+ music-specific tokens to Qwen3.5's vocabulary
- **Five Specialized Modules**:
  - 🎸 **Tab & Chord Generation**: Creates and validates guitar tabs, chord diagrams
  - 🎹 **Music Theory Engine**: Scales, chords, intervals, progressions, circle of fifths
  - 👂 **Ear Training**: Interval identification with song references, solfege exercises
  - 😌 **EQ Adapter**: Frustration detection and emotional response adaptation
  - ✍️ **Song Writing Assistant**: Chord progressions, lyrics, hooks, production tips
- **LoRA Fine-Tuning**: Efficient adaptation without full model retraining
- **HuggingFace Compatible**: Production-ready with custom config and tokenizer classes
- **Ollama Support**: Run locally with Ollama modelfiles
- **Unified Inference**: Instrument context switching (guitar, piano, drums, vocals, theory, production)
- **Synthetic Data Pipeline**: 10 categories, 80+ templates covering all music domains

## 🏗️ Architecture

```
TouchGrass/
├── configs/                    # Model configurations
│   ├── touchgrass_3b_config.py # 3B variant config
│   ├── touchgrass_7b_config.py # 7B variant config
│   └── training_config.py      # Training hyperparameters
├── tokenizer/
│   └── music_token_extension.py # Extends Qwen tokenizer with music tokens
├── models/                     # Specialized music modules
│   ├── tab_chord_module.py     # Guitar tabs and chords
│   ├── music_theory_module.py  # Theory knowledge
│   ├── ear_training_module.py  # Ear training exercises
│   ├── eq_adapter.py           # Emotional intelligence
│   └── songwriting_module.py   # Song creation assistance
├── data/
│   ├── music_qa_generator.py   # Synthetic dataset generator
│   ├── chat_formatter.py       # Qwen chat format converter
│   └── dataset_loader.py       # PyTorch dataset
├── training/
│   ├── losses.py              # Multi-task loss functions
│   ├── trainer.py             # LoRA-aware trainer
│   └── train.py               # Main training entry point
├── inference/
│   └── inference.py           # Unified inference with context
├── benchmarks/
│   ├── evaluate_music_modules.py  # Module-level benchmarks
│   └── evaluate_inference.py      # End-to-end inference benchmarks
├── tests/                     # Comprehensive test suite
│   ├── test_*.py             # Unit tests for each module
│   ├── conftest.py           # Pytest fixtures
│   └── run_tests.py          # Test runner
├── configuration_touchgrass.py  # HuggingFace config class
├── tokenization_touchgrass.py   # HuggingFace tokenizer wrapper
├── ollama_3b_modelfile         # Ollama config for 3B
├── ollama_7b_modelfile         # Ollama config for 7B
└── train.py                    # Main training script
```

## 📦 Installation

### Prerequisites

- Python 3.10+
- PyTorch 2.0+
- Transformers (HuggingFace)
- PEFT (LoRA)
- Datasets
- Pytest (for testing)

### Setup

```bash
# Clone the repository
cd TouchGrass

# Install dependencies
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install transformers peft datasets accelerate tqdm pytest

# Optional: For GPU support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```

## 🚀 Quick Start

### 1. Generate Training Data

```bash
python -c "
from TouchGrass.data.music_qa_generator import MusicQAGenerator
from TouchGrass.data.chat_formatter import ChatFormatter

# Generate synthetic dataset
generator = MusicQAGenerator(seed=42)
dataset = generator.generate_dataset(num_samples=1000, output_path='data/music_qa.jsonl')

# Format for Qwen
formatter = ChatFormatter()
formatted = formatter.format_dataset(dataset)
train_data, val_data = formatter.create_splits(formatted, val_size=0.1)

formatter.save_dataset(train_data, 'data/train.jsonl')
formatter.save_dataset(val_data, 'data/val.jsonl')
"
```

### 2. Train the Model

```bash
# Train 3B variant
python train.py \
  --base_model Qwen/Qwen3.5-3B-Instruct \
  --train_data data/train.jsonl \
  --val_data data/val.jsonl \
  --output_dir checkpoints/touchgrass-3b \
  --lora_r 16 \
  --lora_alpha 32 \
  --batch_size 4 \
  --gradient_accumulation_steps 4 \
  --learning_rate 2e-4 \
  --num_epochs 3 \
  --mixed_precision fp16

# Train 7B variant (requires GPU with 16GB+ VRAM)
python train.py \
  --base_model Qwen/Qwen3.5-7B-Instruct \
  --train_data data/train.jsonl \
  --val_data data/val.jsonl \
  --output_dir checkpoints/touchgrass-7b \
  --lora_r 16 \
  --lora_alpha 32 \
  --batch_size 2 \
  --gradient_accumulation_steps 8 \
  --learning_rate 1e-4 \
  --num_epochs 3 \
  --mixed_precision bf16
```

### 3. Run Inference

```python
from TouchGrass.inference.inference import TouchGrassInference

# Load model
model = TouchGrassInference(
    model_path="checkpoints/touchgrass-3b",
    device="cpu"  # or "cuda"
)

# Single query with instrument context
response = model.generate(
    prompt="How do I play a G major chord?",
    instrument="guitar",
    skill_level="beginner",
    max_new_tokens=200
)
print(response)

# Interactive mode
model.chat(instrument="piano")
```

### 4. Use with Ollama

```bash
# Create modelfile from provided template
cat ollama_3b_modelfile > Modelfile

# Build and run
ollama create touchgrass-3b -f Modelfile
ollama run touchgrass-3b "How do I play a G major chord on guitar?"
```

### 5. Use with HuggingFace

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load with custom config and tokenizer
config = TouchGrassConfig.from_pretrained("checkpoints/touchgrass-3b")
tokenizer = TouchGrassTokenizer.from_pretrained("checkpoints/touchgrass-3b")
model = AutoModelForCausalLM.from_pretrained(
    "checkpoints/touchgrass-3b",
    config=config,
    device_map="auto"
)

# Generate
inputs = tokenizer("system\nYou are a music assistant.\nuser\nHow do I play a G major chord?\nassistant\n", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## 🧪 Testing

Run the comprehensive test suite:

```bash
# Run all tests
python tests/run_tests.py

# Run with coverage
python tests/run_tests.py --coverage

# Run specific test categories
pytest tests/test_music_theory_module.py -v
pytest tests/test_tokenizer.py -v
pytest tests/test_eq_adapter.py -v

# Skip slow tests
pytest -m "not slow"
```

## 📊 Benchmarking

Evaluate model performance on music-specific tasks:

```bash
# Evaluate music modules
python benchmarks/evaluate_music_modules.py --device cpu --d_model 768

# Run inference benchmarks
python benchmarks/evaluate_inference.py --model_path checkpoints/touchgrass-3b --device cpu
```

## 🎛️ Configuration

### Training Configuration

Edit `configs/training_config.py` to customize:

- **Learning rate**: 2e-4 (3B), 1e-4 (7B)
- **LoRA rank (r)**: 8-32 (higher = more capacity)
- **LoRA alpha**: Typically 2×r
- **Batch size**: Adjust based on GPU memory
- **Gradient accumulation**: Use to simulate larger batches
- **Loss weights**:
  - `lm_loss_weight=1.0` (primary language modeling)
  - `eq_loss_weight=0.1` (emotional intelligence)
  - `music_module_loss_weight=0.05` (specialized modules)

### Model Configuration

- **TouchGrass-3B**: Based on Qwen3.5-3B-Instruct, d_model=2048, num_layers=36
- **TouchGrass-7B**: Based on Qwen3.5-7B-Instruct, d_model=4096, num_layers=40

### Music Tokens

The tokenizer extension adds these special tokens:

**Domain tokens**: `[GUITAR]`, `[PIANO]`, `[DRUMS]`, `[VOCALS]`, `[THEORY]`, `[PRODUCTION]`

**Emotion tokens**: `[FRUSTRATED]`, `[CONFUSED]`, `[EXCITED]`, `[CONFIDENT]`

**Difficulty tokens**: `[EASY]`, `[MEDIUM]`, `[HARD]`

**Function tokens**: `[TAB]`, `[CHORD]`, `[SCALE]`, `[INTERVAL]`, `[PROGRESSION]`

**EQ tokens**: `[SIMPLIFY]`, `[ENCOURAGE]`

**Music notation**: All note names (C, C#, D, etc.), chord types (m, dim, aug, 7, maj7, etc.)

## 📚 Music Domains Covered

1. **Guitar & Bass**: Tabs, chords, fingerings, techniques, tunings
2. **Piano & Keys**: Scales, arpeggios, hand positions, pedaling
3. **Drums & Percussion**: Beats, fills, rudiments, kit setup
4. **Vocals & Singing**: Range, breathing, technique, warmups
5. **Music Theory & Composition**: Scales, chords, progressions, harmony
6. **DJ & Production**: EQ, mixing, compression, arrangement

## 😌 Emotional Intelligence

The EQ Adapter detects user frustration and adapts responses:

- **Frustration detection**: Sigmoid output [0, 1] indicating frustration level
- **Emotion classification**: 4 classes (frustrated, confused, excited, confident)
- **Simplification gate**: Automatically simplifies explanations when frustration is high
- **Encouragement templates**: Pre-built supportive responses
- **Context-aware**: Uses conversation history to track emotional state

## 🔧 Advanced Usage

### Custom Dataset Generation

```python
from TouchGrass.data.music_qa_generator import MusicQAGenerator

# Create custom templates
custom_templates = {
    "guitar": [
        {
            "system": "You are a {instrument} specialist.",
            "user": "How do I play {chord}?",
            "assistant": "Place your fingers: {fingering}"
        }
    ]
}

generator = MusicQAGenerator(templates=custom_templates, seed=123)
dataset = generator.generate_dataset(num_samples=500)
```

### Multi-Instrument Context

```python
from TouchGrass.inference.inference import TouchGrassInference

model = TouchGrassInference(model_path="checkpoints/touchgrass-3b")

# Switch between instruments seamlessly
guitar_response = model.generate("How do I palm mute?", instrument="guitar")
piano_response = model.generate("What are the scales in C major?", instrument="piano")
theory_response = model.generate("Explain the circle of fifths", instrument="theory")
```

### LoRA Fine-Tuning Customization

```python
from transformers import LoraConfig

lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,
    r=32,  # Rank (higher = more parameters)
    lora_alpha=64,  # Alpha (typically 2×r)
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],  # Qwen attention modules
    lora_dropout=0.1,
    bias="none"
)
```

## 🧩 Module Details

### Tab & Chord Module

- **Input**: Hidden states + string/fret indices
- **Output**:
  - `tab_validator`: Confidence score [0, 1] for tab validity
  - `difficulty`: 3-class classification (easy/medium/hard)
- **Supports**: Multiple tunings (standard, drop D, open G), 6 strings, 24 frets

### Music Theory Module

- **Functions**:
  - `get_scale_from_key(key, mode)`: Returns scale notes
  - `detect_chord_function(root, chord_type, key)`: Returns Roman numeral
  - `get_circle_of_fifths()`: Returns 12-key circle
  - `construct_chord(root, chord_type)`: Returns chord notes
  - `analyze_progression(progression, key)`: Returns functional analysis
- **Knowledge**: All modes (ionian through locrian), intervals, transpositions

### Ear Training Module

- **Interval identification**: 12 intervals (P1-P8)
- **Song references**: Each interval linked to famous songs (Star Wars for P5, Jaws for m2, etc.)
- **Solfege generation**: Do-Re-Mi for any key/mode
- **Quiz generation**: Automatic interval quiz creation

### EQ Adapter

- **Frustration detector**: Sigmoid output from hidden states
- **Emotion classifier**: 4-way classification
- **Simplification gate**: Context-aware response simplification
- **Encouragement embed**: Pre-trained supportive phrases

### Songwriting Module

- **Progression suggester**: By mood (8 types) and genre (8 types)
- **Lyric generator**: With rhyme scheme awareness (ABAB, AABB, etc.)
- **Hook generator**: Creates memorable song hooks
- **Production advisor**: Instrumentation, effects, arrangement tips

## 📈 Training Tips

1. **Start small**: Use 3B variant for experimentation, 7B for production
2. **Data quality**: Ensure diverse coverage of all 10 categories
3. **Loss weights**: Default (1.0, 0.1, 0.05) work well; adjust if modules need more/less supervision
4. **LoRA rank**: Start with r=16; increase to 32 if underfitting
5. **Mixed precision**: Use `fp16` for NVIDIA, `bf16` for newer GPUs
6. **Gradient accumulation**: Essential for fitting larger batches on limited VRAM
7. **Checkpointing**: Save every 100-500 steps for safety

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass (`python tests/run_tests.py`)
5. Submit a pull request

## 📄 License

MIT License - see LICENSE file for details.

## 🙏 Acknowledgments

- **Qwen3.5**: Base model from Alibaba Cloud
- **HuggingFace**: Transformers and PEFT libraries
- **Music theory**: Traditional Western music theory principles
- **Song references**: Popular music culture for ear training

## 📞 Support

- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Documentation: See individual module docstrings

---

**Made with ❤️ for musicians everywhere.**

*Touch Grass - because even AI needs to remember to make music, not just talk about it.*