Samples to demonstrate pathways for Retrieval Augmented Generation (RAG) for Azure Data - microsoft/AzureDataRetrievalAugmentedGenerationSamples

REFERENCES: 1. Build an LLM from Scratch book: https://mng.bz/M96o 2. Build an LLM from Scratch repo: https://github.com/rasbt/LLMs-from-scratch 3. Slides: https://sebastianraschka.com/pdf/slides/2024-build-llms.pdf 4. LitGPT: https://github.com/Lightning-AI/litgpt 5. TinyLlama pretraining: https://lightning.ai/lightning-ai/studios/pretrain-llms-tinyllama-1-1b DESCRIPTION: This video provides an overview of the three stages of developing an LLM: Building, Training, and Finetuning. The focus is on explaining how LLMs work by describing how each step works. OUTLINE: 00:00 – Using LLMs 02:50 – The stages of developing an LLM 05:26 – The dataset 10:15 – Generating multi-word outputs 12:30 – Tokenization 15:35 – Pretraining datasets 21:53 – LLM architecture 27:20 – Pretraining 35:21 – Classification finetuning 39:48 – Instruction finetuning 43:06 – Preference finetuning 46:04 – Evaluating LLMs 53:59 – Pretraining & finetuning rules of thumb

Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.

Pliny the Prompter has been finding ways to jailbreak, or remove the prohibitions and restrictions on leading LLMs, since last year.

Explore the new LangChain RAG Template with Redis integration. Streamline AI development with efficient, adaptive APIs.

Build your own AI copilot with vCore-based Azure Cosmos DB for MongoDB and Azure OpenAI.

Creating your own AI agent equipped with a sophisticated memory system. This guide provides a detailed walkthrough on leveraging the capabilities of Fireworks AI, MongoDB, and LangChain to construct an AI agent that not only responds intelligently but also remembers past interactions.

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

I'm sure you agree that it has become impossible to ignore Generative AI (GenAI), as we are constantly bombarded with mainstream news about Large Language Models (LLMs). Very likely you have tried…

Scaling up generative AI operations can be costly. At OpenSauced, we faced this challenge while building StarSearch, until we found a low cost solution to deploy an OpenAI-compatible API using open source technology.

Step by step guide to learn Prompt Engineering. We also have resources and short descriptions attached to the roadmap items so you can get everything you want to learn in one place.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Unpacking how large language models work under the hoodEarly view of the next chapter for patrons: https://3b1b.co/early-attentionSpecial thanks to these sup...

Reporting on the productivity impact of SQL generation with generative AI.

Adam Obeng | Data Scientist, Data Platform Science; J.C. Zhong | Tech Lead, Analytics Platform; Charlie Gu | Sr. Manager, Engineering

Learn how to implement a knowledge graph-based RAG application with LangChain to support your DevOps team.

Large language models (LLMs) have taken the world by storm, demonstrating unprecedented capabilities in natural language tasks. In this step-by-step tutorial, you'll leverage LLMs to build your own retrieval-augmented generation (RAG) chatbot using synthetic data with LangChain and Neo4j.

This post will teach you the fundamental intuition behind RAG while providing a simple tutorial to help you get started.

Here’s everything you need to know to build your first LLM app and problem spaces you can start exploring today.