README_free_H200.md

🆓 Free H200 Training: Nano-Coder on Hugging Face

This guide shows you how to train a nano-coder model using Hugging Face's free H200 GPU access (4 minutes daily).

🎯 What You Get

• Free H200 GPU: 4 minutes per day
• No Credit Card Required: Completely free
• Easy Setup: Just a few clicks
• Model Sharing: Automatic upload to HF Hub

🚀 Quick Start

Option 1: Hugging Face Space (Recommended)

1. Create HF Space:

   huggingface-cli repo create nano-coder-free --type space
   

2. Upload Files:

   • Upload all the Python files to your space
   • Make sure app.py is in the root directory

3. Configure Space:

   • Set Hardware: H200 (free tier)
   • Set Python Version: 3.9+
   • Set Requirements: requirements.txt

4. Launch Training:

   • Go to your space URL
   • Click "🚀 Start Free H200 Training"
   • Wait for training to complete (3.5 minutes)

Option 2: Local Setup with HF Free Tier

1. Install Dependencies:

   pip install -r requirements.txt
   

2. Set HF Token:

   export HF_TOKEN="your_token_here"
   

3. Run Free Training:

   python hf_free_training.py
   

📊 Model Configuration (Free Tier)

Parameter	Free Tier	Full Model
Layers	6	12
Heads	6	12
Embedding	384	768
Context	512	1024
Parameters	~15M	~124M
Training Time	3.5 min	2-4 hours

⏰ Time Management

• Daily Limit: 4 minutes of H200 time
• Training Time: 3.5 minutes (safe buffer)
• Automatic Stop: Script stops before time limit
• Daily Reset: New 4 minutes every day at midnight UTC

🎨 Features

Training Features

• ✅ Automatic Time Tracking: Stops before limit
• ✅ Frequent Checkpoints: Every 200 iterations
• ✅ HF Hub Upload: Models saved automatically
• ✅ Wandb Logging: Real-time metrics
• ✅ Progress Monitoring: Time remaining display

Generation Features

• ✅ Interactive UI: Gradio interface
• ✅ Custom Prompts: Any Python code start
• ✅ Adjustable Parameters: Temperature, tokens
• ✅ Real-time Generation: Instant results

📁 File Structure

nano-coder-free/
├── app.py                    # HF Space app
├── hf_free_training.py       # Free H200 training script
├── prepare_code_dataset.py   # Dataset preparation
├── sample_nano_coder.py      # Code generation
├── requirements.txt          # Dependencies
├── model.py                  # nanoGPT model
├── configurator.py           # Configuration
└── README_free_H200.md       # This file

🔧 Customization

Adjust Training Parameters

Edit hf_free_training.py:

# Model size (smaller = faster training)
n_layer = 4      # Even smaller
n_head = 4       # Even smaller
n_embd = 256     # Even smaller

# Training time (be conservative)
MAX_TRAINING_TIME = 3.0 * 60  # 3 minutes

# Batch size (larger = faster)
batch_size = 128  # If you have memory

Change Dataset

# In prepare_code_dataset.py
dataset = load_dataset("your-dataset")  # Your own dataset

📈 Expected Results

After 3.5 minutes of training on H200:

• Training Loss: ~2.5-3.0
• Validation Loss: ~2.8-3.3
• Model Size: ~15MB
• Code Quality: Basic Python functions
• Iterations: ~500-1000

🎯 Use Cases

Perfect For:

• ✅ Learning: Understand nanoGPT training
• ✅ Prototyping: Test ideas quickly
• ✅ Experiments: Try different configurations
• ✅ Small Models: Code generation demos

Not Suitable For:

• ❌ Production: Too small for real use
• ❌ Large Models: Limited by time/parameters
• ❌ Long Training: 4-minute daily limit

🔄 Daily Workflow

1. Morning: Check if you can train today
2. Prepare: Have your dataset ready
3. Train: Run 3.5-minute training session
4. Test: Generate some code samples
5. Share: Upload to HF Hub if good
6. Wait: Come back tomorrow for more training

🚨 Troubleshooting

Common Issues

1. "Daily limit reached"

   • Wait until tomorrow
   • Check your timezone

2. "No GPU available"

   • H200 might be busy
   • Try again in a few minutes

3. "Training too slow"

   • Reduce model size
   • Increase batch size
   • Use smaller context

4. "Out of memory"

   • Reduce batch_size
   • Reduce block_size
   • Reduce model size

Performance Tips

• Batch Size: Use largest that fits in memory
• Context Length: 512 is good for free tier
• Model Size: 6 layers is optimal
• Learning Rate: 1e-3 for fast convergence

📊 Monitoring

Wandb Dashboard

• Real-time loss curves
• Training metrics
• Model performance

HF Hub

• Model checkpoints
• Training logs
• Generated samples

Local Files

• out-nano-coder-free/ckpt.pt - Latest model
• daily_limit_YYYY-MM-DD.txt - Usage tracking

🎉 Success Stories

Users have achieved:

• ✅ Basic Python function generation
• ✅ Simple class definitions
• ✅ List comprehensions
• ✅ Error handling patterns
• ✅ Docstring generation

🔗 Resources

• Hugging Face Spaces
• Free GPU Access
• NanoGPT Original
• Python Code Dataset

🤝 Contributing

Want to improve the free H200 setup?

1. Optimize Model: Make it train faster
2. Better UI: Improve the Gradio interface
3. More Datasets: Support other code datasets
4. Documentation: Help others get started

📝 License

This project follows the same license as the original nanoGPT repository.

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Happy Free H200 Training! 🚀

Remember: 4 minutes a day keeps the AI doctor away! 😄