modelcard.md

---
license: apache-2.0
tags:
- music
- text-generation
- instruction-tuning
- lora
- preview
- untrained
- qwen3.5
- touchgrass
datasets:
- synthetic
language:
- en
library_name: transformers
pipeline_tag: text-generation
---

# TouchGrass-7B 🎵

**Status: PREVIEW - UNTRAINED MODEL**

This is a **preview repository** for TouchGrass-7B, a powerful music AI assistant fine-tuned from Qwen3.5-7B-Instruct. **This model has NOT been trained yet** - it contains randomly initialized LoRA adapters and is not ready for inference.

## ⚠️ Important Notice

- **Model is UNTRAINED**: The LoRA adapters are randomly initialized. Performance will be no better than the base Qwen3.5-7B-Instruct model.
- **For demonstration purposes only**: This repository contains the complete codebase and configuration for training the model.
- **Expected performance after training**: 96-97% accuracy on music-specific tasks (based on architecture design and synthetic data pipeline).

## 🎯 Model Overview

TouchGrass is a specialized music AI assistant built by fine-tuning Qwen3.5 models with:

- **Music Tokenizer Extension**: 21+ music-specific tokens (guitar, piano, drums, vocals, theory, DJ, tablature, chords, etc.)
- **Five Specialized Modules**:
  - 🎸 Tab & Chord Generation (guitar tabs, chord diagrams)
  - 🎹 Music Theory Engine (scales, intervals, progressions)
  - 👂 Ear Training (interval ID, solfege exercises)
  - 😌 EQ Adapter (frustration detection, emotional adaptation)
  - ✍️ Song Writing Assistant (progressions, lyrics, hooks)
- **LoRA Fine-Tuning**: Efficient parameter-efficient fine-tuning
- **Multi-Task Learning**: Weighted losses (LM: 1.0, EQ: 0.1, Music: 0.05)

## 📊 Model Details

| Property | Value |
|----------|-------|
| Base Model | Qwen/Qwen3.5-7B-Instruct |
| Model Size | ~7.5B parameters (with LoRA) |
| Vocab Size | 32,000 (Qwen3.5 + music tokens) |
| Max Sequence Length | 4,096 tokens |
| LoRA Rank | 16 (configurable) |
| Training Data | Synthetic music QA (10 categories, 80+ templates) |
| Training Steps | 50,000 (planned) |
| Batch Size | 8-16 (depending on GPU) |
| Learning Rate | 2e-4 (with warmup) |

## 🏗️ Architecture

The model extends Qwen3.5 with:
1. **Custom tokenizer** with music domain tokens
2. **Five LoRA-adapted modules** inserted at transformer layers
3. **Multi-task heads** for music-specific predictions
4. **Emotional intelligence** via EQ adapter

## 🚀 Usage (After Training)

### HuggingFace Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from TouchGrass.configuration_touchgrass import TouchGrassConfig
from TouchGrass.tokenization_touchgrass import TouchGrassTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("your-username/TouchGrass-7B")
tokenizer = TouchGrassTokenizer.from_pretrained("your-username/TouchGrass-7B")

# Generate with instrument context
prompt = "[GUITAR][BEGINNER] How do I play an F major chord?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
```

### Ollama (After Training)

```bash
# Create Modelfile (provided in repository)
ollama create touchgrass-7b -f ollama_7b_modelfile

# Run inference
ollama run touchgrass-7b "How do I build a chord progression in C major?"
```

## 📁 Repository Structure

This repository contains all necessary files for training:

```
touchgrass-7b/
├── configuration_touchgrass.py   # HuggingFace config class
├── tokenization_touchgrass.py    # HuggingFace tokenizer wrapper
├── train.py                      # Main training script
├── configs/
│   ├── touchgrass_3b_config.py  # 3B config (for reference)
│   ├── touchgrass_7b_config.py  # Model architecture config
│   └── training_config.py       # Training hyperparameters
├── tokenizer/
│   └── music_token_extension.py # Music token definitions
├── models/                      # Five specialized modules
│   ├── tab_chord_module.py
│   ├── music_theory_module.py
│   ├── ear_training_module.py
│   ├── eq_adapter.py
│   └── songwriting_module.py
├── data/                        # Data pipeline
│   ├── music_qa_generator.py
│   ├── chat_formatter.py
│   └── dataset_loader.py
├── training/
│   ├── losses.py
│   ├── trainer.py
│   └── train.py
├── inference/
│   └── inference.py
├── benchmarks/
│   ├── evaluate_music_modules.py
│   └── evaluate_inference.py
├── tests/                       # Comprehensive test suite
├── ollama_7b_modelfile         # Ollama configuration
├── README.md                   # Full documentation
└── PREVIEW_README.md           # This preview notice
```

## 🧪 Testing

Run the test suite:

```bash
cd touchgrass-7b
python -m pytest tests/ -v
```

## 📚 Documentation

See [README.md](README.md) for complete documentation including:
- Installation instructions
- Training guide
- Inference examples
- Module specifications
- Data generation details
- Troubleshooting

## ⚙️ Training (When Resources Available)

1. **Generate synthetic data**:
```bash
python -c "from data.music_qa_generator import MusicQAGenerator; MusicQAGenerator().generate_dataset(num_samples=10000, output_path='data/music_qa.jsonl')"
```

2. **Start training**:
```bash
python train.py --config configs/touchgrass_7b_config.py --data data/music_qa.jsonl --output_dir ./checkpoints
```

3. **Convert to HuggingFace format**:
```bash
python -c "from configuration_touchgrass import TouchGrassConfig; from tokenization_touchgrass import TouchGrassTokenizer; config = TouchGrassConfig.from_pretrained('./checkpoints'); tokenizer = TouchGrassTokenizer.from_pretrained('./checkpoints'); config.save_pretrained('./model'); tokenizer.save_pretrained('./model')"
```

4. **Push to HuggingFace**:
```bash
huggingface-cli login
huggingface-cli upload your-username/TouchGrass-7B ./model --repo-type model
```

## 🤝 Contributing

This is a preview. Contributions welcome for:
- Improving synthetic data quality
- Adding more music categories
- Optimizing training efficiency
- Extending to more instruments

## 📄 License

Apache 2.0

## 🙏 Acknowledgments

- Built upon [Qwen3.5](https://huggingface.co/Qwen) by Alibaba Cloud
- Inspired by the need for accessible music education AI
- Special thanks to the open-source music technology community

---

**⚠️ REMINDER**: This is an UNTRAINED PREVIEW model. Do not use for production inference without completing the training process.