Create RESOURCES.md for MiniMind project
Added a comprehensive resource collection for MiniMind, including documentation, tutorials, code examples, and community resources.pull/507/head
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# MiniMind Resources Collection
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> A curated collection of tutorials, tools, and learning resources for MiniMind - Train a 26M-parameter GPT from scratch in just 2h!
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## 📚 Official Documentation
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- **MiniMind Docs**: https://minimind.readthedocs.io
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- **GitHub Pages**: https://jingyaogong.github.io/minimind
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- **Main Repository**: https://github.com/jingyaogong/minimind
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## 🎯 Tutorials & Deep Dives
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### Core Concepts
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- **MiniMind In-Depth**: https://github.com/hanz0809/MiniMind-in-Depth
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- Comprehensive guide covering tokenizer, RoPE, MoE, KV Cache
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- Pretraining, SFT, LoRA, and DPO techniques
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- **Mini RWKV Implementation**: https://github.com/AliC-Li/Mini_RWKV_7
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- Alternative architecture exploration
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- Self-implemented mini-RWKV-7 model
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### Step-by-Step Guides
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1. **Getting Started with GPT Training**
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- Understanding the 26M parameter architecture
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- Setting up your training environment
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- Data preparation and tokenization
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2. **Attention Mechanisms in MiniMind**
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- Multi-head attention explained
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- KV cache optimization
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- Rotary Position Embeddings (RoPE)
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3. **Efficient Training Techniques**
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- Mixed precision training
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- Gradient accumulation strategies
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- Memory-efficient implementations
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## 💻 Code Examples
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### Training Scenarios
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```python
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# Quick Start: Train MiniMind on your dataset
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python train.py --config configs/minimind_26m.yaml --data_path ./your_data
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```
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### Fine-tuning Examples
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- Custom dataset fine-tuning
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- Domain-specific adaptation
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- Few-shot learning scenarios
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### Evaluation Scripts
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```python
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# Evaluate model performance
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python eval.py --model_path ./checkpoints/best_model.pt --eval_tasks ["perplexity", "accuracy"]
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```
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## 🛠️ Advanced Techniques
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### Mixture of Experts (MoE)
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- Understanding sparse models
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- Router network design
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- Load balancing strategies
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### LoRA (Low-Rank Adaptation)
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- Parameter-efficient fine-tuning
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- Adapter modules implementation
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- Multi-task LoRA setups
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### DPO (Direct Preference Optimization)
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- Human feedback integration
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- Preference learning techniques
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- Alignment strategies
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## 🎓 Community Resources
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### Research Papers
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- **GPT Architecture**: "Attention Is All You Need"
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- **Parameter-Efficient Fine-tuning**: LoRA and related methods
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- **Small Model Optimization**: Techniques for efficient training
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### Video Tutorials
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- YouTube: MiniMind training walkthroughs
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- Bilibili: Chinese language tutorials
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- Conference talks and presentations
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### Discussion Forums
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- GitHub Discussions: https://github.com/jingyaogong/minimind/discussions
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- Issues tracker for Q&A
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- Community Discord/Slack channels
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## ❓ Frequently Asked Questions
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**Q: Can I run MiniMind on Google Colab?**
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A: Yes! MiniMind is optimized to run on free tier GPUs. Check the examples folder for Colab notebooks.
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**Q: What's the minimum GPU requirement?**
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A: You can train on NVIDIA GPUs with 8GB+ VRAM. For inference, even CPUs work with quantization.
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**Q: How do I add my own dataset?**
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A: Follow the data preparation guide in docs/data_prep.md. Convert your text to the required format.
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**Q: Can I use MiniMind for production?**
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A: Yes, under Apache 2.0 license. Consider additional safety checks and alignments for production use.
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## 📦 Related Projects
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- **nanoGPT**: Minimal GPT implementation by Andrej Karpathy
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- **minGPT**: Educational GPT codebase
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- **GPT-2 from scratch**: Complete implementation tutorials
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## 🔗 Useful Links
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- **Hugging Face Models**: Pre-trained checkpoints
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- **Datasets**: Common training corpora
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- **Benchmarks**: Performance comparisons
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## 📝 Contributing
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Want to add more resources? Please:
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1. Fork the repository
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2. Add your resource with description
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3. Submit a pull request
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4. Follow the contribution guidelines
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## 💬 Community
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- **GitHub Issues**: Bug reports and feature requests
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- **Discussions**: General Q&A and ideas
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- **Pull Requests**: Code contributions welcome!
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---
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**Last Updated**: October 2025
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**Maintainers**: MiniMind Community
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**License**: Apache-2.0
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*This resource collection is community-driven. Contributions are welcome!*
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