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相關頁面: AI相關工具非常非常的多，RAG開發工具會整理在RAG開發工具，LLM以及相關工具會整理在Large Language Models，Graph RAG開發工具會整理在Graph RAG，其他工具整理在Generative AI開發工具中。目前還有一些資料在Generative AI及RAG中，將逐步整理這些內容。

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介紹

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未來會將LLM相關連結移到此頁

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• LLM十大觀念
  • 【LLM 10大觀念-1】Scaling Law
    • Scaling Law 定義：我們可以用模型大小、Dataset大小、總計算量，來預測模型最終能力。
  • 【LLM 10大觀念-2】Good Data is All You Need
    • Data是幾乎所有公司在開發自己的LLM時都會遇到的問題。Data需要考量的面相又多又複雜，我自己最重視的就是兩點：Data Mixture跟Data Quality。
      • Data Mixture思考的是：我們要在Training data中加入哪些種類的Data？帶給LLM哪些能力？
      • Data Quality則要從微觀跟巨觀考量，圍觀考量每一個Data point是否可學、好學？巨觀則考量整的Dataset是否有某些糟糕的性質沒有被處理好？
  • 【LLM 10大觀念-3】快速建造自己個instruction tuning dataset
    • 如何finetune出一個公司內部解決特定任務的LLM。
• Understanding LLMs from scratch using middle school math
  • What makes large language models work so well?
    • Embeddings
    • Subword Tokenizers
    • Self Attention
    • Softmax
    • Residual connections
    • Layer Normalization
    • Dropout
    • Multi-head Attention
    • Positional encoding and embedding
  • The GPT architecture
  • The transformer architecture
• Decoding an LLM’s Thoughts: Logit Lens in Just 25 Lines of Code
• The Language Model Landscape — Version 7
  • Zone 1 — Language Models Disruption
  • Zone 2 — General Use-Cases
  • Zone 3 — Specific Implementations
  • Zone 4 — Commercial Model Providers
  • Zone 5 — Model Diversification
  • Zone 6 — Foundation Tooling
  • Zone 7— End User UIs
• Language Model Categorisation
  • Understanding Model Size From Small to Extra-Large Models
    • Large Language Models
    • Medium to Small Language Models
    • Expanding Beyond Language with Multi-Modal Models
    • Action Models (also known as Large Action Models)
• LLM Architectures Explained
  • NLP Fundamentals (Part 1) (Friend Link)
  • Word Embeddings (Part 2) (Friend Link)
  • RNNs, LSTMs & GRUs (Part 3) (Friend Link)
  • LLM Architectures Explained: Encoder-Decoder Architecture (Part 4) (Friend Link)
  • LLM Architectures Explained: Attention Mechanism (Part 5) (Friend Link)
  • LLM Architectures Explained: Transformers (Part 6) (Friend Link)
  • LLM Architectures Explained: Coding a Transformer (Part 7) (Friend Link)
• Unmasking the Surprising Diversity of AI Hallucinations
• Your LLM knows when it’s lying to you. (Friend Link)
  • the Statement Accuracy Prediction, based on Language Model Activations, is a method that uses the activations to predict the accuracy of a statement outputted by an LLM.
• Step-by-Step Exploration of Transformer Attention Mechanisms (Friend Link)
• Which AI Model is the Best in 2024?
  • ChatPlayground is a comprehensive AI platform that offers 16 powerful AI tools in one subscription. It provides industry-leading AI models, a prompt library for various use cases, real-time web search capabilities, image generation, history recall, multilingual support, and more. It is designed for developers, data scientists, students, researchers, content creators, writers, and AI enthusiasts.
• Advanced LLM Chain Orchestration: Building Complex AI Systems That Scale (Friend Link)
• The Next Frontier in LLM Accuracy: Exploring the Power of Lamini Memory Tuning

Text Splitting / Chunking

• The Art of Text Splitting: From Basic to Advanced Techniques for Language Models

Embedding

• Vector Embeddings Explained for Developers!
  • Types of vector embeddings
    • Word embeddings
    • Sentence and document embeddings
    • Graph embeddings
    • Image embeddings
• Text Embeddings: Comprehensive Guide
  • Practical applications
    • Clustering
    • Classification
    • Finding anomalies
  • To be able to work with an extensive knowledge base, we can leverage the RAG approach:
    • Compute embeddings for all the documents and store them in vector storage.
    • When we get a user request, we can calculate its embedding and retrieve relevant documents from the storage for this request.
    • Pass only relevant documents to LLM to get a final answer.