# MiniMind Resources Collection

> A curated collection of tutorials, tools, and learning resources for MiniMind - Train a 26M-parameter GPT from scratch in just 2h!

## 📚 Official Documentation

- **MiniMind Docs**: https://minimind.readthedocs.io
- **GitHub Pages**: https://jingyaogong.github.io/minimind
- **Main Repository**: https://github.com/jingyaogong/minimind

## 🎯 Tutorials & Deep Dives

### Core Concepts
- **MiniMind In-Depth**: https://github.com/hanz0809/MiniMind-in-Depth
  - Comprehensive guide covering tokenizer, RoPE, MoE, KV Cache
  - Pretraining, SFT, LoRA, and DPO techniques
  
- **Mini RWKV Implementation**: https://github.com/AliC-Li/Mini_RWKV_7
  - Alternative architecture exploration
  - Self-implemented mini-RWKV-7 model

### Step-by-Step Guides
1. **Getting Started with GPT Training**
   - Understanding the 26M parameter architecture
   - Setting up your training environment
   - Data preparation and tokenization

2. **Attention Mechanisms in MiniMind**
   - Multi-head attention explained
   - KV cache optimization
   - Rotary Position Embeddings (RoPE)

3. **Efficient Training Techniques**
   - Mixed precision training
   - Gradient accumulation strategies
   - Memory-efficient implementations

## 💻 Code Examples

### Training Scenarios
```python
# Quick Start: Train MiniMind on your dataset
python train.py --config configs/minimind_26m.yaml --data_path ./your_data
```

### Fine-tuning Examples
- Custom dataset fine-tuning
- Domain-specific adaptation
- Few-shot learning scenarios

### Evaluation Scripts
```python
# Evaluate model performance
python eval.py --model_path ./checkpoints/best_model.pt --eval_tasks ["perplexity", "accuracy"]
```

## 🛠️ Advanced Techniques

### Mixture of Experts (MoE)
- Understanding sparse models
- Router network design
- Load balancing strategies

### LoRA (Low-Rank Adaptation)
- Parameter-efficient fine-tuning
- Adapter modules implementation
- Multi-task LoRA setups

### DPO (Direct Preference Optimization)
- Human feedback integration
- Preference learning techniques
- Alignment strategies

## 🎓 Community Resources

### Research Papers
- **GPT Architecture**: "Attention Is All You Need"
- **Parameter-Efficient Fine-tuning**: LoRA and related methods
- **Small Model Optimization**: Techniques for efficient training

### Video Tutorials
- YouTube: MiniMind training walkthroughs
- Bilibili: Chinese language tutorials
- Conference talks and presentations

### Discussion Forums
- GitHub Discussions: https://github.com/jingyaogong/minimind/discussions
- Issues tracker for Q&A
- Community Discord/Slack channels

## ❓ Frequently Asked Questions

**Q: Can I run MiniMind on Google Colab?**
A: Yes! MiniMind is optimized to run on free tier GPUs. Check the examples folder for Colab notebooks.

**Q: What's the minimum GPU requirement?**
A: You can train on NVIDIA GPUs with 8GB+ VRAM. For inference, even CPUs work with quantization.

**Q: How do I add my own dataset?**
A: Follow the data preparation guide in docs/data_prep.md. Convert your text to the required format.

**Q: Can I use MiniMind for production?**
A: Yes, under Apache 2.0 license. Consider additional safety checks and alignments for production use.

## 📦 Related Projects

- **nanoGPT**: Minimal GPT implementation by Andrej Karpathy
- **minGPT**: Educational GPT codebase
- **GPT-2 from scratch**: Complete implementation tutorials

## 🔗 Useful Links

- **Hugging Face Models**: Pre-trained checkpoints
- **Datasets**: Common training corpora
- **Benchmarks**: Performance comparisons

## 📝 Contributing

Want to add more resources? Please:
1. Fork the repository
2. Add your resource with description
3. Submit a pull request
4. Follow the contribution guidelines

## 💬 Community

- **GitHub Issues**: Bug reports and feature requests
- **Discussions**: General Q&A and ideas
- **Pull Requests**: Code contributions welcome!

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**Last Updated**: October 2025
**Maintainers**: MiniMind Community
**License**: Apache-2.0

*This resource collection is community-driven. Contributions are welcome!*