README.md

---
title: Architech - AI Model Architect
emoji: 🏗️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.4.0
app_file: app.py
pinned: false
license: mit
---

# 🏗️ Architech - Your Personal AI Model Architect

**Create custom AI models without the headache!** Just describe what you want, and Architech handles the rest.

## ✨ Features

### 📊 Synthetic Data Generation
- Generate high-quality training data from simple descriptions
- Support for multiple domains: Technology, Healthcare, Finance, Education
- Multiple format types: Conversational, Instruction-following
- 50-500 examples per dataset

### 🚀 Model Training
- Fine-tune state-of-the-art models (GPT-2, DialoGPT)
- Automatic optimization and parameter tuning
- Direct deployment to HuggingFace Hub
- GPU-accelerated training with efficient memory usage

### 🧪 Model Testing
- Load and test your trained models instantly
- Interactive inference with adjustable parameters
- Real-time generation with temperature and length controls

### 🔒 Security & Limits
- **Rate Limiting**: Fair usage for all users
  - Dataset Generation: 10/hour
  - Model Training: 3/hour
  - Model Inference: 50/hour
- **Token Authentication**: Secure HuggingFace integration
- **Error Handling**: Comprehensive error messages and recovery

## 🚀 Quick Start

### 1. Generate Training Data
1. Go to the **"Generate Dataset"** tab
2. Describe your task (e.g., "Customer support chatbot for tech products")
3. Select domain and size
4. Click **"Generate Dataset"**

### 2. Train Your Model
1. Go to the **"Train Model"** tab
2. Enter your model name and HuggingFace token
3. Choose to use synthetic data or provide your own
4. Click **"Train Model"**
5. Wait for training to complete (5-15 minutes)

### 3. Test Your Model
1. Go to the **"Test Model"** tab
2. Enter your model name and token
3. Click **"Load Model"**
4. Enter a test prompt and generate!

## 📋 Requirements

- HuggingFace account with **write** token
- For training: GPU recommended (CPU works but slower)
- Patience during training (coffee break recommended ☕)

## 🎯 Use Cases

- **Customer Support Bots**: Train chatbots for specific products/services
- **Content Generation**: Create domain-specific text generators
- **Educational Tools**: Build tutoring and explanation systems
- **Creative Writing**: Fine-tune for specific writing styles
- **Technical Documentation**: Generate code explanations and docs

## ⚙️ Technical Details

### Supported Base Models
- `distilgpt2` (fastest, smallest)
- `gpt2` (balanced)
- `microsoft/DialoGPT-small` (conversational)

### Training Features
- Gradient accumulation for memory efficiency
- Mixed precision training (FP16)
- Automatic learning rate optimization
- Smart tokenization and padding

### Synthetic Data Quality
- Domain-specific vocabulary
- Natural language variations
- Contextually relevant examples
- Edge case handling

## 🛠️ Troubleshooting

### "GPU Memory Overflow"
- Reduce batch size to 1
- Use smaller base model (distilgpt2)
- Reduce dataset size

### "Permission Denied"
- Check your HuggingFace token has **WRITE** access
- Generate new token at: https://huggingface.co/settings/tokens

### "Rate Limit Exceeded"
- Wait for the cooldown period
- Check remaining requests in error message

## 📚 Best Practices

1. **Start Small**: Begin with 100 examples and 3 epochs
2. **Be Specific**: Detailed task descriptions yield better results
3. **Test First**: Use the Test tab before deploying
4. **Iterate**: Train multiple versions with different parameters
5. **Monitor**: Watch training logs for issues

## 🤝 Contributing

Found a bug? Have a feature request? Open an issue!

## 📜 License

MIT License - feel free to use and modify!

## 🙏 Acknowledgments

Built with:
- [Gradio](https://gradio.app/) - Interface
- [Transformers](https://huggingface.co/transformers/) - Models
- [HuggingFace](https://huggingface.co/) - Infrastructure

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*No PhD required. Just ideas.* ✨