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

Guitar & Bass: Tabs, chords, fingerings, techniques, tunings
Piano & Keys: Scales, arpeggios, hand positions, pedaling
Drums & Percussion: Beats, fills, rudiments, kit setup
Vocals & Singing: Range, breathing, technique, warmups
Music Theory & Composition: Scales, chords, progressions, harmony
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

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

🤝 Contributing

Fork the repository
Create a feature branch
Add tests for new functionality
Ensure all tests pass (python tests/run_tests.py)
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.

Matrix-Corp/TouchGrass-7b

Touch Grass 🎵

🌟 Features

🏗️ Architecture

📦 Installation

Prerequisites

Setup

🚀 Quick Start

1. Generate Training Data

2. Train the Model

3. Run Inference

4. Use with Ollama

5. Use with HuggingFace

🧪 Testing

📊 Benchmarking

🎛️ Configuration

Training Configuration

Model Configuration

Music Tokens

📚 Music Domains Covered

😌 Emotional Intelligence

🔧 Advanced Usage

Custom Dataset Generation

Multi-Instrument Context

LoRA Fine-Tuning Customization

🧩 Module Details

Tab & Chord Module

Music Theory Module

Ear Training Module

EQ Adapter

Songwriting Module

📈 Training Tips

🤝 Contributing

📄 License

🙏 Acknowledgments

📞 Support

Matrix-Corp/
TouchGrass-7b