# 第四部分：前沿与实践篇

- [第十二章：编码器系列模型](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/12_encoder_models.md)
- [12.1 BERT：双向理解的突破](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/12_encoder_models/12.1_bert.md)
- [12.2 RoBERTa、ALBERT 与 ELECTRA：BERT 的改进之路](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/12_encoder_models/12.2_roberta_albert.md)
- [12.3 长文本编码器：Longformer 与 BigBird](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/12_encoder_models/12.3_longformer_bigbird.md)
- [本章小结](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/12_encoder_models/summary.md)
- [第十三章：解码器系列与主流 LLM](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/13_decoder_models.md)
- [13.1 GPT 系列：从语言模型到通用智能的扩展之路](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/13_decoder_models/13.1_gpt_series.md)
- [13.2 Llama 家族：开源如何改变 LLM 格局](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/13_decoder_models/13.2_llama.md)
- [13.3 DeepSeek、Gemini 与其他前沿模型](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/13_decoder_models/13.3_deepseek_gemini.md)
- [13.4 编码器-解码器模型：T5 与 BART 的设计选择](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/13_decoder_models/13.4_t5_bart.md)
- [本章小结](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/13_decoder_models/summary.md)
- [第十四章：架构创新与未来趋势](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends.md)
- [14.1 高效注意力：突破平方复杂度的瓶颈](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/14.1_efficient_attention.md)
- [14.2 混合专家模型：为什么不必激活所有参数](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/14.2_moe.md)
- [14.3 状态空间模型与混合架构：注意力的挑战者](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/14.3_ssm_hybrid.md)
- [14.4 多模态 Transformer：统一不同模态的表示](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/14.4_multimodal.md)
- [14.5 AI Agent 与工具调用：让模型从“说”到“做”](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/14.5_agent_tool_use.md)
- [14.6 推理时计算扩展：让模型学会深度思考](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/14.6_test_time_scaling.md)
- [14.7 长上下文技术：从理论到工程实践](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/14.7_long_context.md)
- [14.8 未来展望](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/14.8_outlook.md)
- [本章小结](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/14_future_trends/summary.md)
- [附录](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/appendix.md)
- [A.1 数学基础速查](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/appendix/a1_math_basics.md)
- [A.2 PyTorch 实现示例](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/appendix/a2_pytorch_examples.md)
- [A.3 主流模型参数速查表](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/appendix/a3_model_reference.md)
- [A.4 推荐阅读与参考文献](https://yeasy.gitbook.io/llm_internals/di-si-bu-fen-qian-yan-yu-shi-jian-pian/appendix/a4_references.md)


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