🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐇𝐚𝐝𝐨𝐨𝐩 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/big-dat....a-hadoop-training-ce
This Edureka video on "Hadoop Pig Tutorial" will provide you with detailed knowledge about Hadoop Pig and the functionalities it can perform.

00:00:00 Introduction
00:00:13 Agenda for today's Session
00:00:51 MapReduce Way
00:02:33 Why go for PIG when MR is there?
00:03:13 Apache Pig vs MapReduce
00:04:20 Why Apache Pig?
00:06:00 Twitter Case Study
00:07:03 High Level Implementation
00:07:47 Detailed Implementation Flow
00:11:32 Apache Pig Architecture
00:12:33 Apache Pig Components
00:13:36 Pig Running Modes
00:16:25 Pig Data Model - Tuple and Bag
00:17:58 Pig Data Model - Map and Atom
00:19:26 Pig Operators
00:32:39 Analysing Logs Using Apache Pig

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

🔵 DevOps Online Training: http://bit.ly/3VkBRUT
🌕 AWS Online Training: http://bit.ly/3ADYwDY
🔵 React Online Training: http://bit.ly/3Vc4yDw
🌕 Tableau Online Training: http://bit.ly/3guTe6J
🔵 Power BI Online Training: http://bit.ly/3VntjMY
🌕 Selenium Online Training: http://bit.ly/3EVDtis
🔵 PMP Online Training: http://bit.ly/3XugO44
🌕 Salesforce Online Training: http://bit.ly/3OsAXDH
🔵 Cybersecurity Online Training: http://bit.ly/3tXgw8t
🌕 Java Online Training: http://bit.ly/3tRxghg
🔵 Big Data Online Training: http://bit.ly/3EvUqP5
🌕 RPA Online Training: http://bit.ly/3GFHKYB
🔵 Python Online Training: http://bit.ly/3Oubt8M
🌕 Azure Online Training: http://bit.ly/3i4P85F
🔵 GCP Online Training: http://bit.ly/3VkCzS3
🌕 Microservices Online Training: http://bit.ly/3gxYqqv
🔵 Data Science Online Training: http://bit.ly/3V3nLrc
🌕 CEHv12 Online Training: http://bit.ly/3Vhq8Hj
🔵 Angular Online Training: http://bit.ly/3EYcCTe

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬

🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy
🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT
🌕 Big Data Architect Masters Program: http://bit.ly/3tTWT0V
🔵 Machine Learning Engineer Masters Program: http://bit.ly/3AEq4c4
🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz
🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv
🌕 RPA Developer Masters Program: http://bit.ly/3OteYfP
🔵 Web Development Masters Program: http://bit.ly/3U9R5va
🌕 Computer Science Bootcamp Program : http://bit.ly/3UZxPBy
🔵 Cyber Security Masters Program: http://bit.ly/3U25rNR
🌕 Full Stack Developer Masters Program : http://bit.ly/3tWCE2S
🔵 Automation Testing Engineer Masters Program : http://bit.ly/3AGXg2J
🌕 Python Developer Masters Program : https://bit.ly/3EV6kDv
🔵 Azure Cloud Engineer Masters Program: http://bit.ly/3AEBHzH

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh


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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐞𝐚𝐜𝐭 𝐉𝐒 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 : https://www.edureka.co/reactjs....-redux-certification (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
This Edureka video on "𝐖𝐡𝐚𝐭 𝐢𝐬 𝐑𝐞𝐚𝐜𝐭𝐉𝐒 ?" will help you understand the fundamentals of ReactJS and help you in building a strong foundation in React by understanding the advantages of ReactJS along with its features and major aspects. Below are the topics covered in this What is ReactJS video
00:00:00 Introduction
00:00:45 Agenda
00:01:05 What is ReactJS?
00:02:07 Aspects of ReactJS
00:03:52 Installation of ReactJS
00:09:56 Programming in ReactJS

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

🔵 DevOps Online Training: http://bit.ly/3VkBRUT
🌕 AWS Online Training: http://bit.ly/3ADYwDY
🔵 React Online Training: http://bit.ly/3Vc4yDw
🌕 Tableau Online Training: http://bit.ly/3guTe6J
🔵 Power BI Online Training: http://bit.ly/3VntjMY
🌕 Selenium Online Training: http://bit.ly/3EVDtis
🔵 PMP Online Training: http://bit.ly/3XugO44
🌕 Salesforce Online Training: http://bit.ly/3OsAXDH
🔵 Cybersecurity Online Training: http://bit.ly/3tXgw8t
🌕 Java Online Training: http://bit.ly/3tRxghg
🔵 Big Data Online Training: http://bit.ly/3EvUqP5
🌕 RPA Online Training: http://bit.ly/3GFHKYB
🔵 Python Online Training: http://bit.ly/3Oubt8M
🌕 Azure Online Training: http://bit.ly/3i4P85F
🔵 GCP Online Training: http://bit.ly/3VkCzS3
🌕 Microservices Online Training: http://bit.ly/3gxYqqv
🔵 Data Science Online Training: http://bit.ly/3V3nLrc
🌕 CEHv12 Online Training: http://bit.ly/3Vhq8Hj
🔵 Angular Online Training: http://bit.ly/3EYcCTe

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬

🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy
🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT
🌕 Big Data Architect Masters Program: http://bit.ly/3tTWT0V
🔵 Machine Learning Engineer Masters Program: http://bit.ly/3AEq4c4
🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz
🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv
🌕 RPA Developer Masters Program: http://bit.ly/3OteYfP
🔵 Web Development Masters Program: http://bit.ly/3U9R5va
🌕 Computer Science Bootcamp Program : http://bit.ly/3UZxPBy
🔵 Cyber Security Masters Program: http://bit.ly/3U25rNR
🌕 Full Stack Developer Masters Program : http://bit.ly/3tWCE2S
🔵 Automation Testing Engineer Masters Program : http://bit.ly/3AGXg2J
🌕 Python Developer Masters Program : https://bit.ly/3EV6kDv
🔵 Azure Cloud Engineer Masters Program: http://bit.ly/3AEBHzH

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh


📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/
📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐞𝐚𝐜𝐭 𝐉𝐒 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞
: https://www.edureka.co/reactjs....-redux-certification code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎")
In this Edureka video on "𝐑𝐞𝐚𝐜𝐭 𝐑𝐨𝐮𝐭𝐞𝐫 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥",you will learn about React, Routing, React Router, the need of React Router and React Router DOM. Moving on, it will also explain the components and the various types of React Router. This video also contains the installation & demo with detailed explanation.
00:00:00 Introduction
00:00:39 Agenda
00:01:13 What is React
00:02:01 What is React Router
00:04:06 Why React Router
00:04:35 Installation
00:06:01 Components of React Router
00:07:47 Types of React Router

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🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

🔵 DevOps Online Training: http://bit.ly/3VkBRUT
🌕 AWS Online Training: http://bit.ly/3ADYwDY
🔵 React Online Training: http://bit.ly/3Vc4yDw
🌕 Tableau Online Training: http://bit.ly/3guTe6J
🔵 Power BI Online Training: http://bit.ly/3VntjMY
🌕 Selenium Online Training: http://bit.ly/3EVDtis
🔵 PMP Online Training: http://bit.ly/3XugO44
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🔵 Cybersecurity Online Training: http://bit.ly/3tXgw8t
🌕 Java Online Training: http://bit.ly/3tRxghg
🔵 Big Data Online Training: http://bit.ly/3EvUqP5
🌕 RPA Online Training: http://bit.ly/3GFHKYB
🔵 Python Online Training: http://bit.ly/3Oubt8M
🌕 Azure Online Training: http://bit.ly/3i4P85F
🔵 GCP Online Training: http://bit.ly/3VkCzS3
🌕 Microservices Online Training: http://bit.ly/3gxYqqv
🔵 Data Science Online Training: http://bit.ly/3V3nLrc
🌕 CEHv12 Online Training: http://bit.ly/3Vhq8Hj
🔵 Angular Online Training: http://bit.ly/3EYcCTe

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬

🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy
🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT
🌕 Big Data Architect Masters Program: http://bit.ly/3tTWT0V
🔵 Machine Learning Engineer Masters Program: http://bit.ly/3AEq4c4
🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz
🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv
🌕 RPA Developer Masters Program: http://bit.ly/3OteYfP
🔵 Web Development Masters Program: http://bit.ly/3U9R5va
🌕 Computer Science Bootcamp Program : http://bit.ly/3UZxPBy
🔵 Cyber Security Masters Program: http://bit.ly/3U25rNR
🌕 Full Stack Developer Masters Program : http://bit.ly/3tWCE2S
🔵 Automation Testing Engineer Masters Program : http://bit.ly/3AGXg2J
🌕 Python Developer Masters Program : https://bit.ly/3EV6kDv
🔵 Azure Cloud Engineer Masters Program: http://bit.ly/3AEBHzH

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh


📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

#graphgpt #gpt3 #machinelearning
GraphGPT converts unstructured natural language into a knowledge graph. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or a transcript from a video to generate a graph visualization of entities and their relationships. A classic use case of prompt engineering.

Github: https://github.com/varunshenoy/GraphGPT
Demo: https://graphgpt.vercel.app/

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⏩ IMPORTANT LINKS
Research Paper Summaries: https://www.youtube.com/watch?v=ykClwtoLER8&list=PLsAqq9lZFOtWUz1WEoJ3GXw197LD7BxMc

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#techviz #datascienceguy #deeplearning #ai #transformers #knowledgegraph #promptengineering

In this video, I go over how to download and run the open-source implementation of GPT3, called GPT Neo. This model is 2.7 billion parameters, which is the same size as GPT3 Ada. The results are very good and are a large improvement over GPT-2. I am excited to play around with this model more and for the future of even larger NLP models.

Notebook https://github.com/mallorbc/GPTNeo_notebook
GPT Neo github https://github.com/EleutherAI/gpt-neo (use the first release tag)
GPT Neo HuggingFace docs https://huggingface.co/transfo....rmers/model_doc/gpt_
A useful article about transformer parameters https://huggingface.co/blog/how-to-generate

00:00 - GPT3 Background
01:07 - GPT3 Interview
02:06 - GPT Neo Github
03:14 - GPT Neo HuggingFace
03:52 - Setting up Anaconda and Jupyter
05:05 - Starting the Jupyter notebook
06:14 - Installing dependencies in the notebook
06:58 - Importing needed dependencies
07:33 - Selecting what GPT model to use
08:45 - Checking our computer hardware
09:36 - Loading the tokenizer
09:55 - Giving our inputs
10:39 - Generating the tokens with the model
11:42 - Decoding and reading the result
13:22 - Reflections on Transformers
14:26 - Outro questions future work

#gpt3 #openai #gpt-3

How far can you go with ONLY language modeling? Can a large enough language model perform NLP task out of the box? OpenAI take on these and other questions by training a transformer that is an order of magnitude larger than anything that has ever been built before and the results are astounding.

OUTLINE:
0:00 - Intro & Overview
1:20 - Language Models
2:45 - Language Modeling Datasets
3:20 - Model Size
5:35 - Transformer Models
7:25 - Fine Tuning
10:15 - In-Context Learning
17:15 - Start of Experimental Results
19:10 - Question Answering
23:10 - What I think is happening
28:50 - Translation
31:30 - Winograd Schemes
33:00 - Commonsense Reasoning
37:00 - Reading Comprehension
37:30 - SuperGLUE
40:40 - NLI
41:40 - Arithmetic Expressions
48:30 - Word Unscrambling
50:30 - SAT Analogies
52:10 - News Article Generation
58:10 - Made-up Words
1:01:10 - Training Set Contamination
1:03:10 - Task Examples

https://arxiv.org/abs/2005.14165
https://github.com/openai/gpt-3

Abstract:
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.

Authors: Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei

Links:
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BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher

In this short clip, Dr. Craig Booth explains what a Large Language Model (LLM) is and how they work. LLMs are the foundational technology category behind models like GPT3, BERT, and other generative AI models.

To watch the complete webinar, go to: https://www.youtube.com/watch?v=LgtDe7ufSRA

In this video I show you how to setup and install GPT4All and create local chatbots with GPT4All and LangChain! Privacy concerns around sending customer and organizational data to OpenAI APIs is a huge issue in AI right now but using local models like GPT4All, LLaMa, Alpaca etc can be a viable alternative.

I did some research and figured out how to make your own version of ChatGPT trained on your own data (PDFs, docs etc) using only open source models like GPT4All which is based on GPT-J or LLaMa depending on the version you use. LangChain has great support for models like these so in this video we use LangChain to integrate LLaMa embeddings with GPT4All and a FAISS local vector database to store out documents.

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Mentioned in the video:
Code: https://github.com/wombyz/gpt4....all_langchain_chatbo
GPT4ALL (Pre-Converted):
https://huggingface.co/mrgaang..../aira/blob/main/gpt4
Embedding Model:
https://huggingface.co/Pi3141/....alpaca-native-7B-ggm
Mac User Troubleshooting: https://docs.google.com/docume....nt/d/1JDMBTOjbRtJo49

Timestamps:
0:00 - What is GPT4All?
2:28 - Setup & Install
7:13 - LangChain + GPT4All
11:42 - Custom Knowledge Query
19:42 - Chatbot App
24:19 - My Thoughts on GPT4All

About Me 👋🏼
Hi! My name is Liam Ottley and I’m an AI developer and entrepreneur, founder of my own AI development company: Morningside AI (https://morningside.ai). You’ve found my YouTube channel where I help other entrepreneur’s wrap their heads around AI with tutorials on how to get started with building AI apps!

With developments in AI moving so fast, I’m constantly working on my own projects and those of clients and sharing my learnings with my viewers 2-3x per week.

I don’t have anything to sell you, my only hope is that one day some of you will learn enough from my videos to see an exciting opportunity in the AI space and want to work with my development company to make it a reality.

In this talk, we will cover the basics of Reinforcement Learning from Human Feedback (RLHF) and how this technology is being used to enable state-of-the-art ML tools like ChatGPT. Most of the talk will be an overview of the interconnected ML models and cover the basics of Natural Language Processing and RL that one needs to understand how RLHF is used on large language models. It will conclude with open question in RLHF.

RLHF Blogpost: https://huggingface.co/blog/rlhf
The Deep RL Course: https://hf.co/deep-rl-course
Slides from this talk: https://docs.google.com/presen....tation/d/1eI9PqRJTCF
Nathan Twitter: https://twitter.com/natolambert
Thomas Twitter: https://twitter.com/thomassimonini

Nathan Lambert is a Research Scientist at HuggingFace. He received his PhD from the University of California, Berkeley working at the intersection of machine learning and robotics. He was advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab and Roberto Calandra at Meta AI Research. He was lucky to intern at Facebook AI and DeepMind during his Ph.D. Nathan was was awarded the UC Berkeley EECS Demetri Angelakos Memorial Achievement Award for Altruism for his efforts to better community norms.

Did you ever wonder how to create a BERT or GPT2 tokenizer in your own language or on your own corpus? This video will teach you how to do this with any tokenizer of the 🤗 Transformers library.

This video is part of the Hugging Face course: http://huggingface.co/course
Open in colab to run the code samples:
https://colab.research.google.....com/github/huggingfa

Related videos:
- Building a new tokenizer: https://youtu.be/MR8tZm5ViWU

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This demo shows how to run large AI models from #huggingface on a Single GPU without Out of Memory error. Take a OPT-175B or BLOOM-176B parameter model .These are large language models and often require very high processing machine or multi-GPU, but thanks to bitsandbytes, in just a few tweaks to your code, you can run these large models on single node.

In this tutorial, we'll see 3 Billion parameter BLOOM AI model (loaded from Hugging Face) and #LLM inference on Google Colab (Tesla T4) without OOM.

This is brilliant! Kudos to the team.

bitsandbytes - https://github.com/TimDettmers/bitsandbytes
Google Colab Notebook - https://colab.research.google.....com/drive/1qOjXfQIAU

In this video, I will show you how to install PrivateGPT on your local computer. PrivateGPT uses LangChain to combine GPT4ALL and LlamaCppEmbeddeing for information retrieval from documents in different formats including PDF, TXT and CVS. The list of file types can be easily extended with PrivateGPT.

LINKS:
PrivateGPT GitHub: https://github.com/imartinez/privateGPT
Open Embeddings Video: https://youtu.be/ogEalPMUCSY
LLM Playlist: https://www.youtube.com/playli....st?list=PLVEEucA9MYh

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All Interesting Videos:
Everything LangChain: https://www.youtube.com/playli....st?list=PLVEEucA9MYh

Everything LLM: https://youtube.com/playlist?l....ist=PLVEEucA9MYhNF5-

Everything Midjourney: https://youtube.com/playlist?l....ist=PLVEEucA9MYhMdrd

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Introduction to the world of LLM (Large Language Models) in April 2023. With detailed explanation of GPT-3.5, GPT-4, T5, Flan-T5 to LLama, Alpaca and KOALA LLM, plus dataset sources and configurations.
Including ICL (in-context learning), adapter fine-tuning, PEFT LoRA and classical fine-tuning of LLM explained. When to choose what type of data set for what LLM job?

Addendum: Beautiful, new open-source "DOLLY 2.0" LLM was not published at time of recording, therefore a special link to my video explaining DOLLY 2:
https://youtu.be/kZazs6V3314

A comprehensive LLM /AI ecosystem is essential for the creation and implementation of sophisticated AI applications. It facilitates the efficient processing of large-scale data, the development of complex machine learning models, and the deployment of intelligent systems capable of performing complex tasks.

As the field of AI continues to evolve and expand, the importance of a well-integrated and cohesive AI ecosystem cannot be overstated.

A complete overview of today's LLM and how you can train them for your needs.

#naturallanguageprocessing
#LargeLanguageModels
#chatgpttutorial
#finetuning
#finetune
#ai
#introduction
#overview
#chatgpt

Dead simple way to run LLaMA on your computer. - https://cocktailpeanut.github.io/dalai/

LLaMa Model Card - https://github.com/facebookres....earch/llama/blob/mai

LLaMa Announcement - https://ai.facebook.com/blog/l....arge-language-model-

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