之前我们谈到如何 从私有代码库自动部署Hugo站到GitHub Pages 。以为将之前的workflow yaml修改为Hexo的版本非常容易，亲自试了下发现打脸了。原因在于Hexo的依赖很多，因此环境配置比Hugo就复杂很多，同时还兼有各种包和库的兼容性问题。相比之下，Hugo就显得非常干净，使用GitHub Action容易不少。
花了好多时间并且尝试不了下20次，才将Hexo的action workflow最终调通 :-) ，记录下踩过的坑和解决文案。
与Hugo的workflow相比，需要解决如下几个问题：
开始吧！
Last time we talked how to deploy Hugo from private repository to GitHub Pages . I thought it is trivial to modify the workflow yaml to make it works for Hexo. However, it is much more complicated than I ever thought. The reason is that Hexo has to setup more dependencies with compatibility issues while Hugo is relatively self-contained and clean.
Actually, I spent several hours and attempted more than 20 times to make the workflow yaml works. :-)
Compared to Hugo workflow, there are several issues to be resolved:
Let's start!
NLP adapters主要想解决不同任务需要finetune整个模型的痛点，与Prompting一样，是一种轻量级的训练方法，也是Transfer Learning的应用。按出现时间来看，finetune早于adapters，adapters早于prompting。今天来重看这篇Adapters的文章，可以更好地理解lightweight finetune的发展过程。
Adapters在NLP中的应用源于这篇 ICML2019 文章：Parameter-Efficient Transfer Learning for NLP adapters 。
Adapter-based tuning requires training two orders of magnitude fewer parameters to fine-tuning, while attaining similar performance.
Hugo是个极好的静态网站生成器。一个常见的情况是原始的网站源码放在私有代码库中，但希望自动化构建和部署的功能。假设私有代码库托管在github上，希望能自动化部署到GitHub Pages，这个功能可以通过github actions轻松搞定。
开始之前，假设我们已有了两个repo(可以隶属于不同的github账户):
Hugo is a nice static site generator. A common scenario is to store your website source code in a private repository and serve it on GitHub Pages. Can we leverage the github actions to automatically build and deploy the site from the private repository to GitHub Pages? The answer is absolutely yes!
Before we start, you need to have two repos (can belong to different github accounts):
My first understanding of a language model is originated from n-gram. When I know RNNLM, I have a question: why a neural network can represent a language model?
After some research, I found the answer. Essentially, language model is a probability distribution:
A statistical language model is a probability distribution over sequences of words.
# Scaling Laws for Neural Language Models
一篇实验Paper，调研了神经网络语言模型交叉熵损失变化满足power-law定律，挺有意思的文章。Transformer之后有许多探索不同模型结构的文章，并在一些任务上取得了新的SOTA，却鲜有人考虑影响模型性能的主要因素是什么。
Throughout we will observe precise power-law scalings for performance as a function of training time, context length, dataset size, model size, and compute budget.
LeetCode Top K Frequent Words need to use STL priority_queue to solve the problem.
template<
class T,
class Container = std::vector<T>,
class Compare = std::less<typename Container::value_type>
> class priority_queue;There are two ways to implement the priority_queue compare function of customized type.
LeetCode Top K Frequent Words 中会用到priority_queue，同时需要定义priority_queue的排序算法。
template<
class T,
class Container = std::vector<T>,
class Compare = std::less<typename Container::value_type>
> class priority_queue;有两种方式实现自定义类型priority_queue的比较函数。