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
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.
构建自己的个人人工智能助手：构建文本和语音本地LLM的分步指南
In this tutorial we will create a personal local LLM assistant, that you can talk to. You will be able to record your voice using your microphone and send to the LLM. The LLM will return the answer with text AND speech.
高级RAG 02：揭开PDF解析的面纱
20240215 Additional content: Unveiling PDF Parsing: How to extract formulas from scientific pdf papers
如何在没有矢量数据库的情况下进行RAG
Introduction
When it comes to bestowing Large Language Models (LLMs) with long-term memory, the prevalent approach often involves a Retrieval Augmented Generation (RAG) solution, with vector databases acting as the storage mechanism for the long-term memory. This begs the question: Can we achieve the same results without vector databases?
2024年十大数据和人工智能趋势
从LLM将现代数据堆栈转换为矢量数据库的数据可观察性，以下是我对2024年顶级数据工程趋势的预测。
