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:

   huggingface-cli repo create nano-coder-zerogpu --type model
   

2. Upload Training Files:

   python upload_to_zerogpu.py
   

3. Launch ZeroGPU Training:

   python launch_zerogpu.py
   

Option 2: Direct ZeroGPU API

1. Install HF Hub:

   pip install huggingface_hub
   

2. Set HF Token:

   export HF_TOKEN="your_token_here"
   

3. Run ZeroGPU Training:

   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

huggingface-cli repo create nano-coder-zerogpu --type model

Step 2: Prepare Dataset

python prepare_code_dataset.py

Step 3: Launch Training

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! 🚀