Drupal多站点-何时、如何以及为什么。管理多个Drupal网站的终极指南
Drupal可以配置为从一个代码库为多个网站提供服务。这样的Drupal安装被称为多站点。使用一个Drupal来运行多个网站的能力是很好的，但另一方面，需要一些考虑。在这篇文章中，我将详细讨论Drupal多站点，让您深入、完整地了解多站点是如何工作的，为什么使用它，何时有意义以及何时避免它。
什么是Drupal多站点及其工作方式
Drupal多站点安装是指在单个代码库上支持多个Drupal网站的安装。
多站点是通过在Drupal系统的/sites/文件夹中为每个网站创建一个单独的文件夹来实现的。例如
GPT-5的完整训练已经上线
We can expect it to be released in November, maybe on the 2nd anniversary of the legendary ChatGPT launch
In similar timeframes, we will also be getting Gemini 2 Ultra, LLaMA-3, Claude-3, Mistral-2 and many other groundbreaking models
(Google’s Gemini already seems to be giving tough competition to GPT-4 turbo)
T-RAG=RAG+微调+实体检测
Introduction
Large Language Models (LLMs) are increasingly utilised across various domains, including question answering over private enterprise documents, where data security and robustness are paramount.
忘记RAG，未来是RAG融合
理解Mixtral中的稀疏专家混合（SMoE）层
检索增强一代的终结？新兴的体系结构标志着一种转变
Retrieval Augmented Generation (RAG) has been a cornerstone in enhancing large language models (LLMs) for complex, knowledge-driven tasks. By pulling in relevant data from a vector database, RAG has empowered LLMs with factual grounding, significantly reducing instances of fabricated information. But is this the end of the road for RAG?
Devin，新的人工智能，能取代人类软件工程师吗？
A new AI named Devin claiming the title of the world’s first AI software engineer. From coding entire projects to fixing GitHub issues, Devin seems to be the new topic. And with such sensational capabilities, the rumor mill is working overtime, sparking fears that the era of human software engineers might be coming to an end. But before you join the panic parade, let’s take a look and see why, despite these advancements, we’re not heading for the job market exit anytime soon.
使用QLoRA在Google Colab中微调Mistral 7b（完整指南）
In this article, we are going to fine-tune Mistral 7b on the entire code base of a game called Enlighten, all for free in Google Colab(or Kaggle) with synthetic data. The resulting model will outperform Openai’s GPT-4 on our benchmark.
These are the steps:
RAG与微调——哪种工具是提升LLM应用程序的最佳工具？
高级RAG 05：探索语义块
After parsing the document, we can obtain structured or semi-structured data. The main task now is to break them down into smaller chunks to extract detailed features, and then embed these features to represent their semantics. Its position in RAG is shown in Figure 1.