Phi2 finetune own data
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A Mere Mortal's Visual Guide To AI Vector Embeddings
so you can be the life of the party
Build LLM Apps with LangChain.js - DeepLearning.AI
Efficient LLM inference
On quantization, distillation, and efficiency
Function Calling With Anthropic Claude and Amazon Bedrock
Amazon Bedrock is a powerful platform that grants seamless access to some of the most advanced Large Language Models (LLMs) through a…
What is Prompt Engineering? | prmpts.AI
Prompt sandbox
What are Vector Embeddings | Pinecone
Rerankers and Two-Stage Retrieval
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Ultimate ChatGPT Pack (20k Prompts)
👇🏻Access Your 1000+ ChatGPT Marketing Prompts Handbook here 👇🏻
There Will Be No AGI | by FS Ndzomga | in MLearning.ai - Freedium
Anthropomorphic Views of LLMs and AGI Hopes
Building a Multi-User Chatbot with Langchain and Pinecone in Next.JS
In this example, we’ll imagine that our chatbot needs to answer questions about the content of a website. To do that, we’ll need a way to store and access that information when the chatbot generates its response.
Eugene Yan
I design, build, and operate machine learning systems that serve customers at scale. I also write about data/ML systems and career.
What is a large language model (LLM)? | Cloudflare
Large language models (LLMs) are machine learning models that can comprehend and generate human language text. Learn how LLMs work and their security risks.
What is Machine Learning? Definition, Types, Tools & More
Learn what machine learning is, how it differs from AI and deep learning, and why it is one of the most exciting fields in data science.
Machine Learning, often abbreviated as ML, is a subset of artificial intelligence (AI) that focuses on the development of computer algorithms that improve automatically through experience and by the use of data. In simpler terms, machine learning enables computers to learn from data and make decisions or predictions without being explicitly programmed to do so.
Learn Python, Data Viz, Pandas & More | Tutorials | Kaggle
Practical data skills you can apply immediately: that's what you'll learn in these no-cost courses. They're the fastest (and most fun) way to become a data scientist or improve your current skills.
Intro
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RAG But Better: Rerankers with Cohere AI
Rerankers have been a common component of retrieval pipelines for many years. They allow us to add a final "reranking" step to our retrieval pipelines — like...
Building Production-Ready RAG Applications: Jerry Liu
Large Language Models (LLM's) are starting to revolutionize how users can search for, interact with, and generate new content. Some recent stacks and toolkit...
Train your own language model with nanoGPT | Let’s build a songwriter
Real-time coding and exploring nanoGPT with me! See detailed model explanation in Andrej Karpathy's legendary video (best GPT explanation on the internet): h...
Train your own language model with nanoGPT | Let’s build a songwriter
Real-time coding and exploring nanoGPT with me! See detailed model explanation in Andrej Karpathy's legendary video (best GPT explanation on the internet): h...
Dense Vectors: Capturing Meaning with Code
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Introduction to Facebook AI Similarity Search (Faiss)
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Evaluating ragas: A framework for RAG pipelines
TL;DR ragas is a framework designed to assess the performance of Retrieval Augmented Generation (RAG) pipelines, a type of LLM application that uses external data to enhance its context. While there are tools to build RAG pipelines, evaluating their performance can be challenging. ragas offers tools rooted in recent research to evaluate LLM-generated text, providing insights into RAG pipeline performance. It can also be incorporated into CI/CD for ongoing performance checks.
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A Complete Guide for Ragas, the RAG Pipeline for LLM Evaluation – AI StartUps Product Information, Reviews, Latest Updates
Dive into the world of Ragas, the go-to Retrieval-Augmented Generation (RAG) pipeline evaluator. This comprehensive guide will walk you through its features, metrics, and integrations, making you an expert in evaluating Language Models (LLMs).
Practical Deep Learning for Coders - 10: Diving Deeper
Learn Deep Learning with fastai and PyTorch, 2022
Harnessing Retrieval Augmented Generation With Langchain
Implementing RAG using Langchain
A New Era of Text Generation: RAG, LangChain, and Vector Databases
This article unveils the transformative potential of RAG and its integration with LangChain and Vector Databases.
leehanchung/llm-pdf-qa-workshop: Introduction to LLM App Development Workshop: PDF Q&A App using OpenAI, Langchain, and Chainlit
Introduction to LLM App Development Workshop: PDF Q&A App using OpenAI, Langchain, and Chainlit - GitHub - leehanchung/llm-pdf-qa-workshop: Introduction to LLM App Development Workshop: PDF...
facebookresearch/faiss
A library for efficient similarity search and clustering of dense vectors.