[feat] update docs

Author: jingyaogong

docs/images/train_grpo_512.png

@@ -1,4 +1,4 @@
-# <strong>Welcome to MiniMind!</strong>
+# Welcome to MiniMind!
 
 <figure markdown>
   ![logo](images/logo.png)
@@ -7,47 +7,118 @@
 
 ## 📌 Introduction
 
-MiniMind is a super-small language model project trained completely from scratch, requiring **only $0.5 + 2 hours** to train a **26M** language model!
+**MiniMind** is a complete, open-source project for training ultra-small language models from scratch with minimal cost. Train a **26M** ChatBot in just **2 hours** with only **$3** on a single 3090 GPU!
 
 - **MiniMind** series is extremely lightweight, the smallest version is **1/7000** the size of GPT-3
-- The project open-sources the minimalist structure of large models, including:
-  - Mixture of Experts (MoE)
-  - Dataset cleaning
-  - Pretraining
-  - Supervised Fine-Tuning (SFT)
-  - LoRA fine-tuning
-  - Direct Preference Optimization (DPO)
-  - Model distillation
-- All core algorithm code is reconstructed from scratch using native PyTorch, without relying on third-party abstract interfaces
-- This is not only a full-stage open-source reproduction of large language models, but also a tutorial for getting started with LLMs
-
-!!! note "Training Cost"
-    "2 hours" is based on NVIDIA 3090 hardware (single card) testing, "$0.5" refers to GPU server rental cost
-
-## ✨ Key Features
-
-- **Ultra-low cost**: Single 3090, 2 hours, $0.5 to train a ChatBot from scratch
-- **Complete pipeline**: Covers Tokenizer, pretraining, SFT, LoRA, DPO, distillation full process
-- **Education-friendly**: Clean code, suitable for learning LLM principles
-- **Ecosystem compatible**: Supports `transformers`, `llama.cpp`, `vllm`, `ollama` and other mainstream frameworks
-
-## 📊 Model List
-
-| Model (Size) | Inference Memory (Approx.) | Release |
-|------------|----------|---------|
-| MiniMind2-small (26M) | 0.5 GB | 2025.04.26 |
-| MiniMind2-MoE (145M) | 1.0 GB | 2025.04.26 |
-| MiniMind2 (104M) | 1.0 GB | 2025.04.26 |
+- Complete implementation covering:
+  - **Tokenizer training** with custom vocabulary
+  - **Pretraining** (knowledge learning)
+  - **Supervised Fine-Tuning (SFT)** (conversation patterns)
+  - **LoRA fine-tuning** (parameter-efficient adaptation)
+  - **Direct Preference Optimization (DPO)** (human preference alignment)
+  - **RLAIF algorithms** (PPO/GRPO/SPO - reinforcement learning)
+  - **Knowledge distillation** (compress large model knowledge)
+  - **Model reasoning distillation** (DeepSeek-R1 style)
+  - **YaRN algorithm** (context length extrapolation)
+- **Pure PyTorch implementation**: All core algorithms are implemented from scratch using native PyTorch, without relying on third-party abstract interfaces
+- **Educational value**: This is not only a full-stage open-source reproduction of large language models, but also a comprehensive tutorial for getting started with LLMs
+- **Extended capabilities**: MiniMind now supports [MiniMind-V](https://github.com/jingyaogong/minimind-v) for vision multimodal tasks
+
+!!! note "Training Cost & Time"
+    "2 hours" is based on **NVIDIA 3090** hardware (single card) testing
+    
+    "$3" refers to GPU server rental cost
+    
+    With 8× RTX 4090 GPUs, training time can be compressed to **under 10 minutes**
+
+## ✨ Key Highlights
+
+- **Ultra-low cost**: Single 3090, 2 hours, $3 to train a fully functional ChatBot from scratch
+- **Complete pipeline**: Tokenizer → Pretraining → SFT → LoRA → DPO/RLAIF → Distillation → Reasoning
+- **Latest algorithms**: Implements cutting-edge techniques including GRPO, SPO, and YaRN
+- **Education-friendly**: Clean, well-documented code suitable for learning LLM principles
+- **Ecosystem compatible**: Seamless support for `transformers`, `trl`, `peft`, `llama.cpp`, `vllm`, `ollama`, and `Llama-Factory`
+- **Full capabilities**: Supports multi-GPU training (DDP/DeepSpeed), model visualization (Wandb/SwanLab), and dynamic checkpoint management
+- **Production-ready**: OpenAI API protocol support for easy integration with third-party UIs (FastGPT, Open-WebUI, etc.)
+- **Multimodal extension**: Extended to vision with [MiniMind-V](https://github.com/jingyaogong/minimind-v)
+
+## 📊 Model Series
+
+### MiniMind2 Series (Latest - 2025.04.26)
+
+| Model | Parameters | Vocabulary | Layers | Hidden Dim | Context | Inference Memory |
+|-------|-----------|------------|--------|-----------|---------|-----------------|
+| MiniMind2-small | 26M | 6,400 | 8 | 512 | 2K | ~0.5 GB |
+| MiniMind2-MoE | 145M | 6,400 | 8 | 640 | 2K | ~1.0 GB |
+| MiniMind2 | 104M | 6,400 | 16 | 768 | 2K | ~1.0 GB |
+
+### MiniMind-V1 Series (Legacy - 2024.09.01)
+
+| Model | Parameters | Vocabulary | Layers | Hidden Dim | Context |
+|-------|-----------|------------|--------|-----------|---------|
+| minimind-v1-small | 26M | 6,400 | 8 | 512 | 2K |
+| minimind-v1-moe | 104M | 6,400 | 8 | 512 | 2K |
+| minimind-v1 | 108M | 6,400 | 16 | 768 | 2K |
+
+## 📅 Latest Updates (2025-10-24)
+
+🔥 **RLAIF Training Algorithms**: Native implementation of PPO, GRPO, and SPO
+
+- **YaRN Algorithm**: RoPE length extrapolation for improved long-sequence handling
+- **Adaptive Thinking**: Reasoning models support optional thinking chains
+- **Full template support**: Tool calling and reasoning tags (`<tool_call>`, `<think>`, etc.)
+- **Visualization**: Switched from WandB to [SwanLab](https://swanlab.cn/) (China-friendly)
+- **Reasoning models**: Complete MiniMind-Reason series based on DeepSeek-R1 distillation
+
+## 🎯 Project Contents
+
+- Complete MiniMind-LLM architecture code (Dense + MoE models)
+- Detailed Tokenizer training code
+- Full training pipeline: Pretrain → SFT → LoRA → RLHF/RLAIF → Distillation
+- High-quality, curated and deduplicated datasets at all stages
+- Native PyTorch implementation of key algorithms, minimal third-party dependencies
+- Multi-GPU training support (single-machine multi-card DDP, DeepSpeed, distributed clusters)
+- Visualization with Wandb/SwanLab
+- Model evaluation on third-party benchmarks (C-Eval, C-MMLU, OpenBookQA)
+- YaRN algorithm for RoPE context length extrapolation
+- OpenAI API protocol server for easy integration
+- Streamlit web UI for chat
+- Full compatibility with community tools: llama.cpp, vllm, ollama, Llama-Factory
+- MiniMind-Reason models: Complete open-source data + weights for reasoning distillation
 
 ## 🚀 Quick Navigation
 
-- [Quick Start](quickstart.md) - Environment setup, model download, quick testing
-- [Model Training](training.md) - Pretraining, SFT, LoRA, DPO training process
+- **[Quick Start](quickstart.md)** - Environment setup, model download, quick testing
+- **[Model Training](training.md)** - Pretraining, SFT, LoRA, RLHF, RLAIF, and reasoning training
 
-## 🔗 Related Links
+## 🔗 Links & Resources
 
+**Project Repositories**:
 - **GitHub**: [https://github.com/jingyaogong/minimind](https://github.com/jingyaogong/minimind)
 - **HuggingFace**: [MiniMind Collection](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5)
-- **ModelScope**: [MiniMind Models](https://www.modelscope.cn/profile/gongjy)
-- **Online Demo**: [ModelScope Studio](https://www.modelscope.cn/studios/gongjy/MiniMind)
+- **ModelScope**: [MiniMind Profile](https://www.modelscope.cn/profile/gongjy)
+
+**Online Demos**:
+- [ModelScope Studio - Standard Chat](https://www.modelscope.cn/studios/gongjy/MiniMind)
+- [ModelScope Studio - Reasoning Model](https://www.modelscope.cn/studios/gongjy/MiniMind-Reasoning)
+- [Bilibili Video Introduction](https://www.bilibili.com/video/BV12dHPeqE72/)
+
+**Vision Extension**:
+- [MiniMind-V](https://github.com/jingyaogong/minimind-v) - Multimodal vision language models
+
+## 💡 Why MiniMind?
+
+The AI community is flooded with high-cost, complex frameworks that abstract away the fundamentals. MiniMind aims to democratize LLM learning by:
+
+1. **Lowering the barrier**: No need for expensive GPUs or cloud services
+2. **Understanding, not just using**: Learn every detail from tokenization to inference
+3. **End-to-end learning**: Train from scratch, not just fine-tune existing models
+4. **Code clarity**: Pure PyTorch implementations you can read and understand
+5. **Practical results**: Get a working ChatBot with minimal resources
+
+As we say: **"Building a Lego airplane is far more exciting than flying first class!"**
+
+---
+
+Next: [Get Started →](quickstart.md)