ZEROGPU_SETUP.md

# 🚀 ZeroGPU Setup Guide: Free H200 Training

## 🎯 What is ZeroGPU?

**ZeroGPU** is Hugging Face's **FREE** compute service that provides:
- **Nvidia H200 GPU** (70GB memory)
- **No time limits** (unlike the 4-minute daily limit)
- **No credit card required**
- **Perfect for training** nanoGPT models

## 📊 ZeroGPU vs Previous Approach

| Feature | Previous (HF Spaces) | ZeroGPU |
|---------|---------------------|---------|
| **GPU** | H200 (4 min/day) | H200 (unlimited) |
| **Memory** | Limited | 70GB |
| **Time** | 4 minutes daily | No limits |
| **Cost** | Free | Free |
| **Use Case** | Demos/Testing | Real Training |

## 🚀 How to Use ZeroGPU

### Option 1: Hugging Face Training Cluster (Recommended)

1. **Create HF Model Repository:**
   ```bash
   huggingface-cli repo create nano-coder-zerogpu --type model
   ```

2. **Upload Training Files:**
   ```bash
   python upload_to_zerogpu.py
   ```

3. **Launch ZeroGPU Training:**
   ```bash
   python launch_zerogpu.py
   ```

### Option 2: Direct ZeroGPU API

1. **Install HF Hub:**
   ```bash
   pip install huggingface_hub
   ```

2. **Set HF Token:**
   ```bash
   export HF_TOKEN="your_token_here"
   ```

3. **Run ZeroGPU Training:**
   ```bash
   python zerogpu_training.py
   ```

## 📁 Files for ZeroGPU

- `zerogpu_training.py` - Main training script
- `upload_to_zerogpu.py` - Upload files to HF
- `launch_zerogpu.py` - Launch training job
- `ZEROGPU_SETUP.md` - This guide

## ⚙️ ZeroGPU Configuration

### Model Settings (Full Power!)
- **Layers**: 12 (full model)
- **Heads**: 12 (full model)
- **Embedding**: 768 (full model)
- **Context**: 1024 tokens
- **Parameters**: ~124M (full GPT-2 size)

### Training Settings
- **Batch Size**: 48 (optimized for H200)
- **Learning Rate**: 6e-4 (standard GPT-2)
- **Iterations**: 10,000 (no time limits!)
- **Checkpoints**: Every 1000 iterations

## 🎯 Expected Results

With ZeroGPU H200 (no time limits):
- **Training Time**: 2-4 hours
- **Final Loss**: ~1.8-2.2
- **Model Quality**: Production-ready
- **Code Generation**: High quality Python code

## 🔧 Setup Steps

### Step 1: Create HF Repository
```bash
huggingface-cli repo create nano-coder-zerogpu --type model
```

### Step 2: Prepare Dataset
```bash
python prepare_code_dataset.py
```

### Step 3: Launch Training
```bash
python zerogpu_training.py
```

## 📊 Monitoring

### Wandb Dashboard
- Real-time training metrics
- Loss curves
- Model performance

### HF Hub
- Automatic checkpoint uploads
- Model versioning
- Training logs

## 💰 Cost: **$0** (Completely Free!)

- **No credit card required**
- **No time limits**
- **H200 GPU access**
- **70GB memory**

## 🎉 Benefits of ZeroGPU

1. **No Time Limits** - Train for hours, not minutes
2. **Full Model** - Use complete GPT-2 architecture
3. **Better Results** - Production-quality models
4. **Real Training** - Not just demos
5. **Automatic Saving** - Models saved to HF Hub

## 🚨 Troubleshooting

### If Training Won't Start
1. Check HF token is set
2. Verify repository exists
3. Check dataset is prepared

### If Out of Memory
1. Reduce batch_size to 32
2. Reduce gradient_accumulation_steps
3. Use smaller model (but why?)

### If Upload Fails
1. Check internet connection
2. Verify HF token permissions
3. Check repository access

## 🎯 Use Cases

### Perfect For:
- ✅ **Production Training** - Real model training
- ✅ **Research** - Experiment with different configs
- ✅ **Learning** - Understand full training process
- ✅ **Model Sharing** - Upload to HF Hub

### Not Suitable For:
- ❌ **Quick Demos** - Use HF Spaces for that
- ❌ **Testing** - Use local GPU for that

## 🔄 Workflow

1. **Setup**: Create HF repo and prepare data
2. **Train**: Launch ZeroGPU training
3. **Monitor**: Watch progress on Wandb
4. **Save**: Models automatically uploaded
5. **Share**: Use trained models

## 📈 Performance

Expected training performance on ZeroGPU H200:
- **Iterations/second**: ~2-3
- **Memory usage**: ~40-50GB
- **Training time**: 2-4 hours for 10k iterations
- **Final model**: Production quality

## 🎉 Success!

ZeroGPU is the **proper way** to use Hugging Face's free compute for real training. No more 4-minute limits - train your nano-coder model properly!

**Next Steps:**
1. Create HF repository
2. Upload files
3. Launch training
4. Monitor progress
5. Use your trained model!

Happy ZeroGPU training! 🚀