Fine-tuning GPT-3.
In this video we demonstrate how to use APIs and audio to create prompts and completions that can be used to fine-tune Transformers such as GPT-3. We show how to use a News API with Python to extract news articles and create a dataset for training and fine-tuning models.

We explain what prompt and completion datasets are, and the role they play in natural language processing in fine-tuning transformers. We then discuss using video and audio streams to build data pipelines for fine-tuning and transcribing speech to text using OpenAI's Whisper.

With over 500 hours of YouTube videos being uploaded every minute video is a rich source of detailed information with which to train AI.

The video first includes step-by-step demonstrations of using the News API to create a prompt and completion dataset and then shows how to build pipelines transcribing speech to text using Whisper.

Foundation models like GPT-3 can be 'Fine-Tuned'. This Fine_tuning allows them to learn the vocabulary and semantics of particular disciplines. If you want your AI solution to be familiar with options pricing algorithms, then you can show it lots of options pricing papers. If you'd like your AI to be able to converse and write about monetary policy then you can expose it to policy papers, books on economic theory, central bank meeting minutes and news conferences.

You train transformer AI models in a particular way. You don't simply feed in a file containing the text of the book or article that you are using to train the AI. You break the text up into a series of "Prompts" and "Completions". Prompts are the beginning of a passage of text and completions are what follow a prompt.

In this post we leverage the breadth and depth of content on YouTube. We propose a strategy to use selected videos to train our AI language models. These videos are chosen because they are rich in expertise in an area we want to train our language model in. The example used in this video is to train our AI in monetary policy by studying content generated by the Federal Reserve. We devise a strategy to use speech to text transcription of YouTube videos. We use OpenAI's 'Whisper' to perform this transcription. We then break this text up into 'prompts' and completions to train our model.

Following this approach we can easily train our NLP models to have expertise in any field we choose.



#NLP #Python #FineTuning #AI #MachineLearning #NewsAPI #Whisper #Lucidate #chatgpt #gpt3 #gpt-3 #openai #whisper #openai-whisper

Links:
Attention Mechanism in Transformers: https://youtu.be/sznZ78HquPc Transformer playlist: https://youtube.com/playlist?l....ist=PLaJCKi8Nk1hwaMU
Federal reserve Description: https://youtu.be/lJTmCv2kKKs
FOMC Feb 2023 Press Conference: https://www.youtube.com/watch?v=3Iv4aCN0OOo&t=2s
Python code for Transcriber class: https://github.com/mrspiggot/p....rompts_and_completio

=========================================================================
Link to introductory series on Neural networks:
Lucidate website: https://www.lucidate.co.uk/blog/categ...

YouTube: https://www.youtube.com/playlist?list...

Link to intro video on 'Backpropagation':
Lucidate website: https://www.lucidate.co.uk/post/intro...

YouTube: https://youtu.be/8UZgTNxuKzY

'Attention is all you need' paper - https://arxiv.org/pdf/1706.03762.pdf

=========================================================================
Transformers are a type of artificial intelligence (AI) used for natural language processing (NLP) tasks, such as translation and summarisation. They were introduced in 2017 by Google researchers, who sought to address the limitations of recurrent neural networks (RNNs), which had traditionally been used for NLP tasks. RNNs had difficulty parallelizing, and tended to suffer from the vanishing/exploding gradient problem, making it difficult to train them with long input sequences.

Transformers address these limitations by using self-attention, a mechanism which allows the model to selectively choose which parts of the input to pay attention to. This makes the model much easier to parallelize and eliminates the vanishing/exploding gradient problem.

Self-attention works by weighting the importance of different parts of the input, allowing the AI to focus on the most relevant information and better handle input sequences of varying lengths. This is accomplished through three matrices: Query (Q), Key (K) and Value (V). The Query matrix can be interpreted as the word for which attention is being calculated, while the Key matrix can be interpreted as the word to which attention is paid. The eigenvalues and eigenvectors of these matrices tend to be similar, and the product of these two matrices gives the attention score.

=========================================================================

#ai #deeplearning #chatgpt #gpt3 #neuralnetworks #attention #attentionisallyouneed

This video shows a user how to train a GPT-2 model in Python. It requires a library called gpt-2-simple. This video trains the model on the Tiny Shakespeare set.


https://colab.research.google.....com/drive/1TCkUMfehk

Become a GPT text generation expert with our comprehensive 3-part video series. Learn how to build, train, and test your own GPT model, and gain insights into GPT-2 architecture, tokenization, and training processes that will elevate your language model projects.

Discover the power of GPT transformers and learn how to build, train, and test your own text generator in this comprehensive 3-part video series. Gain practical experience and insights into GPT-2 architecture, tokenization, and training techniques.

❤️ Check out Anyscale and try it for free here: https://www.anyscale.com/papers

📝 The paper "#GPT-4 Technical Report" is available here:
https://cdn.openai.com/papers/gpt-4.pdf

More here:
https://openai.com/product/gpt-4

App development: https://twitter.com/zaid/statu....s/163736539552613171
The most beautiful sentence: https://reddit.com/r/ChatGPT/c....omments/121t9b4/the_
Doggy: https://twitter.com/peakcooper..../status/163971682268
Safe code: https://twitter.com/moyix/stat....us/16425874103692656
Finds security holes: https://twitter.com/feross/sta....tus/1641548124366987
App design: https://www.usegalileo.ai

📝 Paper with caustics:
https://users.cg.tuwien.ac.at/....zsolnai/gfx/adaptive

My latest paper on simulations that look almost like reality is available for free here:
https://rdcu.be/cWPfD

Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5

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Can GPT 4 Prompt itself? Give it a mission and it will come up with the prompts. This video showcases the rise of autonomous AI, including 5 major developments in the last 48 hours.

Starting with the OG Auto-GPT, we see how it quickly gain text-to-speech, coding and more. Karpathy weighs in and then we see how you can now create an app with just your voice, with a Jarvis demo and another route via Imagica.AI.

I then showcase MemoryGPT, a brand new model that can permanently store previous conversations and remembers topics the next time you ask.
I also cover the concerningly rise of models such as ChaosGPT that show how people will create malicious goal-seeking models just for fun.

The video shows how you can now create a shareable bot on poe.com, with any personality you like (images were from Midjourney v5) and what Anthropic are working on with Claude Next [plus Nvidia million x quote].

You'll see how Microsoft Jarvis, using HuggingGPT, is hit and miss, and how Sebastien Bubeck shows we are not even seeing the raw potential of GPT 4. I end with a disagreement between Yudkowsky and Altman, via Baby AGI, on whether we can use AGI to align AGI.

Illustration by Eva Bee: https://www.instagram.com/evabee_draws/?hl=en
Auto-GPT: https://github.com/Torantulino/Auto-GPT
With Coding: https://twitter.com/SigGravita....s/status/16421814982
With Text-to-speech: https://twitter.com/RokStrnisa..../status/164330875333
Karpathy Thoughts: https://twitter.com/karpathy/s....tatus/16426076206736
Mckay Wrigley: https://mobile.twitter.com/mck....aywrigley/status/164
Imagica.AI: https://www.imagica.ai/
MemoryGPT: https://twitter.com/rikvk01/st....atus/164478732705777
Create a Bot: https://poe.com/
TechCrunch: https://techcrunch.com/2023/04..../06/anthropics-5b-4-
Nvidia: https://www.fool.com/earnings/....call-transcripts/202
HuggingFace Demo: https://huggingface.co/spaces/microsoft/HuggingGPT
{Get the openai api key from https://platform.openai.com/account/api-keys and huggingface token from https://huggingface.co/docs/hub/security-tokens, (make sure to select 'write'). Press submit on the keys first and remember to use a smallish image.
Recursive Agent: https://twitter.com/DeveloperH....arris/status/1643080
Bubeck: https://www.youtube.com/watch?v=qbIk7-JPB2c
Baby AGI and Yudkowsky: https://twitter.com/ESYudkowsk....y/status/16405111562
Altman First Quote: https://www.youtube.com/watch?v=tPJIpx3KscY
And 2nd: https://www.youtube.com/watch?v=WHoWGNQRXb0
Chaos GPT: https://www.youtube.com/watch?v=g7YJIpkk7KM

https://www.patreon.com/AIExplained

When it comes to transformer-based language models, the more parameters the better. In this video, we go over how to fine-tune the largest open-sourced GPT model, GPT-J-6B.

Get started using GPT-J: https://helloforefront.com/contact-sales

More information on fine-tuning: https://docs.helloforefront.com/gpt-j/fine-tune

Experiment with GPT-J in our playground: https://playground.helloforefront.com

There are updated install instructions here: https://www.youtube.com/watch?v=pQGJc3i_BmE
This is a video that's by request... I talked about Auto-GPT in a past video and people asked me to show how to install it. So here's a quick step-by-step tutorial on how to install it. This is a pretty niche video but I'd like to have it in my library of videos to share with others later.

https://gitforwindows.org/
https://www.python.org/
https://github.com/Torantulino/Auto-GPT
https://agentgpt.reworkd.ai/
https://youtu.be/Qm2Ai_JiQmo

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Outro music generated by Mubert https://mubert.com/render

#AutoGPT #ChatGPT #gpt4

I tried the new AI tool for Logseq, and the result seems promising! Of course, GPT-3 won't magically make you smarter, but it does save you some time and helps you write more.

We're now seeing more and more AI writing tools like LEX and Jasper, but are there going to be more AI note-taking tools too?

Mentioned Tools:
LEX: https://lex.page/
Jasper: https://www.jasper.ai/
Napkin: https://napkin.one/?via=shu
Mem: https://get.mem.ai/
Logseq: https://logseq.com/
OpenAI: https://openai.com/api/


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ChatGPT is the most advanced AI chatbot ever released to the public. In this video, I show how you can use ChatGPT in your day-to-day marketing and to grow your business.
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What exactly can GPT-4 see? And does OpenAI have a plan for physical embodiment? Over 12 separate developments announced in the last few days are covered in this video, in which I showcase how improvements in text, speech, 3D, and embodiment are beginning to complement each other.

We'll take an early look at use cases for images in GPT 4, how Nvidia and Adobe are pushing boundaries in Text to 3D, at Sam Altman's Moore's Law For Everything, at the new LeRF model and Assembly AI's latest Speech-to-Text.

We will also see how OpenAI already had a Robotics team but now is invested in 1x, developing humanoid robots. But they are not the first, as Agility Robotics has shown...

Conformer Model: https://www.assemblyai.com/playground/source

Napkin-to-Code: https://twitter.com/rowancheun....g/status/16357445295
BeMyEyes: https://twitter.com/BeMyEyes/s....tatus/16356902546895
Tanishq: https://twitter.com/iScienceLuvr
Medical Questions: https://arxiv.org/pdf/2303.13375.pdf
VQA: https://arxiv.org/pdf/1904.08920.pdf
Scale is All You Need: https://twitter.com/markchen90..../status/163790378039
Nvidia Text-to-3D: https://twitter.com/chenhsuan_lin/status/1638543416723406849?t=rXEsDcEvsdmMPEsZPPhWmw&s=09
Adobe Firefly: https://www.youtube.com/watch?v=_sJfNfMAQHw
Moore's Law For Everything: https://moores.samaltman.com/#....:~:text=Moore%27s%20
LeRF 3D: https://arxiv.org/pdf/2303.09553.pdf
Wired: https://www.wired.com/story/fa....st-forward-for-smart
Conformer Model: https://www.assemblyai.com/playground/source
https://www.assemblyai.com/blog/conformer-1/
Boston Dynamics: https://www.youtube.com/watch?v=-e1_QhJ1EhQ
1X: https://www.1x.tech/
Quadrupedal Agility: https://arxiv.org/pdf/2303.11330.pdf
Rubik's Cube: https://openai.com/research/solving-rubiks-cube
Statues: https://twitter.com/TuurDemees....ter/status/163942172
GPT 4 Technical Report: https://arxiv.org/pdf/2303.08774.pdf
Agility Robotics: https://www.youtube.com/watch?v=FhlUI2xDxdE
Sam Altman Lex Fridman: https://www.youtube.com/watch?v=L_Guz73e6fw


https://www.patreon.com/AIExplained

ChatGPT-4 Prompt Engineering: The Tree of Thoughts Process

𝐅𝐢𝐧𝐝 𝐭𝐡𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐓𝐞𝐦𝐩𝐥𝐚𝐭𝐞 𝐇𝐞𝐫𝐞:
https://www.allabtai.com/the-t....ree-of-thoughts-prom

Paper:
https://arxiv.org/abs/2203.02155

Get a FREE 45+ ChatGPT Prompts PDF here:
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Explore how AI can mimic human-like problem-solving with the revolutionary Tree of Thoughts (ToT) approach. Traditional AI methods fall short - ToT is the future, considering multiple reasoning paths like exploring a forest, not just one trail.

Witness how Prompt Engineering with ToT tackles complex problems more efficiently than ever before.

00:00 ChatGPT-4 Prompt Engineering: ToT Intro
00:25 What is the Tree of Thoughts Prompt Approach?
01:36 The Brainstorming Phase
03:59 The Evaluation Phase
06:19 The Expansion Phase
07:59 The Decision Phase

In this video, we review Guanaco, the new 65B parameter model that achieves 99% of the performance of ChatGPT. It is truly incredible. Since it is a large model, we use a cloud GPU to power it. This model can code, has logic and reasoning, can do creative writing, and so much more. Guacano was trained in under 24 hours on a single GPU, using a new technology called QloRA, which is mind-blowing. How does it do on the LLM rubric? Let's find out!

Enjoy :)

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TextGen WebUI - https://github.com/oobabooga/text-generation-webui

We'll be diving into the incredible world of Auto-GPT and revealing how you can harness its power to boost your productivity by tenfold.

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We'll cover topics such as:

Content creation using Auto-GPT for faster and better results
Auto-GPT for efficient social media management and marketing
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Enhancing your video editing process with the help of Auto-GPT
Innovative applications of Auto-GPT in scientific research and data analysis
And much more! Our video is packed with actionable tips and strategies that will help you save time and increase your overall productivity. Whether you're a writer, business owner, digital artist, or researcher, Auto-GPT has something to offer everyone.

Make sure to like, comment, and subscribe for more videos on using Auto-GPT and other cutting-edge technologies. Don't forget to hit the notification bell to stay updated on our latest content!

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#chatgpt #ai #openai ChatGPT is a FREE computer program that will change the way you practice English forever. In today’s video, I’ll walk you through how to use OpenAI ChatGPT to enhance your vocabulary, grammar, conversation, exam prep and so much more. It will completely transform the way you practice in ways you wouldn’t believe are possible!

Read my ULTIMATE guide for using Chat GPT for English Learning: https://bit.ly/3H5ApjD

Download my list of prompts for practicing with ChatGPT: https://bit.ly/3XCHOOA

Try OpenAI ChatGPT now: https://chat.openai.com/

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NEW HERE? 🔥Get started with those helpful lessons:
#1 STRATEGY of how to become FLUENT in English https://youtu.be/0V0WeEnsec0
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How to stop translating in your head: 5-steps to get stuck LESS and speak FASTER in English https://youtu.be/ZP6Ev1HvK4w
How to stay MOTIVATED when learning ENGLISH https://youtu.be/IAx7X_jvHcU
Do you change your voice when you speak English? Here’s why
https://youtu.be/qFbjwopad7c
American INTONATION - What They don't Teach You in School https://youtu.be/FStyKn4V8cE
10 ways to DESTROY your English Fluency https://youtu.be/36KgqKYdWhY

the repository to download:
https://github.com/terpyPy/Tra....in-GPT-2-And-Make-Da
the instructions on how to train:
https://medium.com/ai-innovati....on/beginners-guide-t

Just a few years ago, state-of-the-art, autoregressive natural language processing models had 100 million parameters and we thought that was massive. Now, Cerebras makes it not just possible, but easy, to continuously train and fine-tune the powerful open source GPT-J model with six billion parameters on a single CS-2 system using our groundbreaking weight streaming execution mode.

Learn more: https://www.cerebras.net/blog/....cerebras-makes-it-ea

#ai #deeplearning #GPTJ #artificialintelligence #NLP #naturallanguageprocessing