awesome-azure-openai-llm
a curated list of 🌌 Azure OpenAI, 🦙Large Language Models, and references with notes.
Stars: 336
This repository is a collection of references to Azure OpenAI, Large Language Models (LLM), and related services and libraries. It provides information on various topics such as RAG, Azure OpenAI, LLM applications, agent design patterns, semantic kernel, prompting, finetuning, challenges & abilities, LLM landscape, surveys & references, AI tools & extensions, datasets, and evaluations. The content covers a wide range of topics related to AI, machine learning, and natural language processing, offering insights into the latest advancements in the field.
README:
This repository contains references to Azure OpenAI, Large Language Models (LLM), and related services and libraries.
🔹Brief each item on a few lines as possible.
🔹The dates are based on the first commit, article publication, or paper version 1 issuance.
🔹Capturing a chronicle and key terms of that rapidly advancing field.
🔹Disclaimer: Please be aware that some content may be outdated.
- Section 1 🎯: RAG
- Section 2 🌌: Azure OpenAI
- Section 3 🌐: LLM Applications
- Section 4 🤖: Agent
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Section 5 🏗️: Semantic Kernel & DSPy
- Semantic Kernel: Micro-orchestration
- DSPy: Optimizer frameworks
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Section 6 🛠️: LangChain | LlamaIndex
- LangChain Features: Macro & Micro-orchestration
- LangChain Agent & Criticism
- LangChain vs Competitors
- LlamaIndex: Micro-orchestration & RAG
-
Section 7 🧠: Prompting | Finetuning
- Prompt Engineering
- Finetuning: PEFT (e.g., LoRA), RLHF, SFT
- Quantization & Optimization
- Other Techniques: e.g., MoE
- Visual Prompting
- Section 8 🏄♂️: Challenges & Abilities
-
Section 9 🌍: LLM Landscape
- LLM Taxonomy
- LLM Collection
- Domain-Specific LLMs: e.g., Software development
- Multimodal LLMs
- Generative AI Landscape
-
Section 10 📚: Surveys & References
- LLM Surveys | Business use cases
- Building LLMs: from scratch
- LLMs for Korean & Japanese
- Section 11 🧰: AI Tools & Extensions
- Section 12 📊: Datasets
- Section 13 📝: Evaluations
-
Legend 🔑:
-
ref: external URL -
doc: archived doc -
cite: the source of comments -
cnt: number of citations -
git: GitHub link -
x-ref: Cross reference - 📺: youtube or video
-
ⓒ https://github.com/kimtth all rights reserved.
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