AI/ML

‘Champagne for my real friends, real pain for my sham friends’ is clever. Let’s find more phrases like that with NLP.

Colab : https://drp.li/W5n1F Model Page: https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca My Links: Twitter - https://twitter.com/Sam_Witteveen Linkedin - https://www.linkedin.com/in/samwitteveen/ Github: https://github.com/samwit/langchain-tutorials https://github.com/samwit/llm-tutorials 00:00 Intro 00:17 MistralOrca 7B on Hugging Face 01:48 Code Time 02:04 ChatML 03:02 Code Generation 03:20 Instruction Answering 04:30 The Strength of the System Prompt 05:50 Succint Output 06:43 Story Telling 07:20 GSM8k

To be clear right from the outset: if you have to use generative models to keep your job or otherwise have no choice in the matter, then obviously you aren’t being a dick.

Colab: https://drp.li/gyYpV My Links: Twitter - https://twitter.com/Sam_Witteveen Linkedin - https://www.linkedin.com/in/samwitteveen/ Github: https://github.com/samwit/langchain-tutorials https://github.com/samwit/llm-tutorials 00:00 Intro 00:52 Parent Document Retriever Diagram 04:56 Code time #langchain #openai #llm

A modern representation of the commonplace book. Contribute to bramses/commonplace-bot development by creating an account on GitHub.

Zapier today announced the launch of Canvas, a new tool that aims to help its users plan and diagram their business-critical processes -- with a fair bit

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

Web scraping using ChatGPT: extract data from websites using code. ChatGPT is a powerful tool for web scraping. Techniques include using Selenium and Beautiful Soup. Get started now!

How does LoRA work? Low-Rank Adaptation for Parameter-Efficient LLM Finetuning explained. Works for any other neural network as well, not just for LLMs. 📜 „Lora: Low-rank adaptation of large language models“ Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L. and Chen, W., 2021. https://arxiv.org/abs/2106.09685 📚 https://sebastianraschka.com/blog/2023/llm-finetuning-lora.html 📽️ LoRA implementation: https://youtu.be/iYr1xZn26R8 Thanks to our Patrons who support us in Tier 2, 3, 4: 🙏 Dres. Trost GbR, Siltax, Vignesh Valliappan, Mutual Information, Kshitij Outline: 00:00 LoRA explained 00:59 Why finetuning LLMs is costly 01:44 How LoRA works 03:45 Low-rank adaptation 06:14 LoRA vs other approaches ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🔥 Optionally, pay us a coffee to help with our Coffee Bean production! ☕ Patreon: https://www.patreon.com/AICoffeeBreak Ko-fi: https://ko-fi.com/aicoffeebreak ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ 🔗 Links: AICoffeeBreakQuiz: https://www.youtube.com/c/AICoffeeBreak/community Twitter: https://twitter.com/AICoffeeBreak Reddit: https://www.reddit.com/r/AICoffeeBreak/ YouTube: https://www.youtube.com/AICoffeeBreak #AICoffeeBreak #MsCoffeeBean #MachineLearning #AI #research​ Music 🎵 : Meadows - Ramzoid Video editing: Nils Trost

An online research report on causality for machine learning by Cloudera Fast Forward.

Seamlessly integrate LLMs as Python functions. Contribute to jackmpcollins/magentic development by creating an account on GitHub.

Brian Merchant’s new book, “Blood in the Machine,” argues that Luddism stood not against technology per se but for the rights of workers in the face of automation.

Dive into this series of 4 student use cases for AI to discover how generative AI tools like ChatGPT can be used as a feedback generator, tutor, team coach, and learner. Get sample prompts and shareable guidelines to help students use AI tools effectively.

https://github.com/bramses/commonplace-bot/tree/main----------WEEKLY NEWSLETTER: https://www.bramadams.dev/STENOGRAPHY: https://stenography.dev/GITHUB: https...

For the notebook used in this talk, see https://github.com/fastai/lm-hackers . This is an extended version of the keynote given at posit::conf(2023).

To better understand how transformers work, I hand-assigned all the weights to predict a simple sequence.

Before the widespread corporate adoption of the computer, especially those with a graphic user interface, design was full of sharp knives, smelly solvents, and tacky glues. Sound familiar?

In this video, we work through building a chatbot using Retrieval Augmented Generation (RAG) from start to finish. We use OpenAI's gpt-3.5-turbo Large Language Model (LLM) as the "engine", we implement it with LangChain's ChatOpenAI class, use OpenAI's text-embedding-ada-002 for embedding, and the Pinecone vector database as our knowledge base. 📌 Code: https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/rag-chatbot.ipynb 🌲 Subscribe for Latest Articles and Videos: https://www.pinecone.io/newsletter-signup/ 👋🏼 AI Consulting: https://aurelio.ai 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ 00:00 Chatbots with RAG 00:59 RAG Pipeline 02:35 Hallucinations in LLMs 04:08 LangChain ChatOpenAI Chatbot 09:11 Reducing LLM Hallucinations 13:37 Adding Context to Prompts 17:47 Building the Vector Database 25:14 Adding RAG to Chatbot 28:52 Testing the RAG Chatbot 32:56 Important Notes when using RAG #artificialintelligence #nlp #ai #langchain #openai #vectordb