# Learn-LLM-Easily

**Repository Path**: marten98/Learn-LLM-Easily

## Basic Information

- **Project Name**: Learn-LLM-Easily
- **Description**: 轻松学系列之： 轻松学习LLM大语言模型
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 6
- **Created**: 2024-12-04
- **Last Updated**: 2025-09-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 轻松学习大模型

<div align=center>
<img src="assets/C0-0-logo.png" width = "1000">
</div>



## 项目概览

**本项目是一个轻松学习大模型的教程，主要内容包括：**

1. **Transformer算法解析**，这一部分将带你深入了解Transformer架构的核心理念和技术细节，包括自注意力机制、前馈神经网络、多头注意力等关键组件，以及它们如何协同工作以处理序列数据。

2. **开源大模型的本地化部署**，这里会教授如何选择合适的开源大模型，并详细介绍在本地环境中部署这些模型的方法，包括环境配置、模型下载、运行测试等步骤，确保你能顺利将模型应用于实际场景中。

3. **如何优化大模型**，这部分内容聚焦于提升大模型性能的各种策略，涵盖模型提示词工程、微调等技术，以及如何根据具体应用场景调整模型参数，以达到最佳效果。

4. **开发大模型的应用**，最后，我们将探索如何利用大模型解决实际问题，包括但不限于ChatBot、Copilot、Agent等任务，使大模型能够更好地服务于业务需求。

   

## 必读论文清单

已将 **"Scaling Laws for Neural Language Models"** 加入到之前的论文清单中。更新后的清单如下：

---

### **基础理论与模型架构**
1. **Attention Is All You Need**  
   - [arXiv link](https://arxiv.org/abs/1706.03762)

2. **BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding**  
   - [arXiv link](https://arxiv.org/abs/1810.04805)

3. **GPT: Improving Language Understanding by Generative Pre-training**  
   - [arXiv link](https://arxiv.org/abs/1801.06146)

4. **T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**  
   - [arXiv link](https://arxiv.org/abs/1910.10683)

5. **XLNet: Generalized Autoregressive Pretraining for Language Understanding**  
   - [arXiv link](https://arxiv.org/abs/1906.08237)

---

### **规模化与性能优化**
6. **Language Models Are Few-Shot Learners**  
   - [arXiv link](https://arxiv.org/abs/2005.14165)

7. **Scaling Laws for Neural Language Models**  
   - [arXiv link](https://arxiv.org/abs/2001.08361)

8. **Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity**  
   - [arXiv link](https://arxiv.org/abs/2101.03961)

9. **LoRA: Low-Rank Adaptation of Large Language Models**  
   - [arXiv link](https://arxiv.org/abs/2106.09685)

---

### **强化学习与对齐**
10. **Learning to Summarize with Human Feedback**  
    - [arXiv link](https://arxiv.org/abs/2009.01325)

11. **Training Language Models to Follow Instructions with Human Feedback**  
    - [arXiv link](https://arxiv.org/abs/2203.02155)

---

### **应用与安全**
12. **PaLM-E: An Embodied Multimodal Language Model**  
    - [arXiv link](https://arxiv.org/abs/2305.00735)

13. **Flamingo: A Visual Language Model for Few-Shot Learning**  
    - [arXiv link](https://arxiv.org/abs/2204.14198)

14. **Towards a Human-like Open-Domain Chatbot**  
    - [arXiv link](https://arxiv.org/abs/2001.09977)

15. **Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned**  
    - [arXiv link](https://arxiv.org/abs/2207.03508)

---

### **未来发展与理论探讨**
16. **Emergent Abilities of Large Language Models**  
    - [arXiv link](https://arxiv.org/abs/2205.11916)

17. **Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks**  
    - [arXiv link](https://arxiv.org/abs/2005.11401)

---