minimind/RESOURCES.md

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

# 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

# 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!