Improving Language Understanding by Generative Pre-Training

Course Materials: https://github.com/maziarraiss....i/Applied-Deep-Learn

In this short Python tutorial, You will learn how to build your own Gradio App for Text Generation / Natural Language generation using GPT-J-6B that's available on Hugging Face Transformers. Eleuther AI's GPT-J-6B is the real open-source alternative for GPT-3 by OpenAI. Using that from Hugging Face Model Hub, We'll learn how to build a Full-stack Web App using Gradio - surprisingly in just 6 lines of Python code, you'd have a world-class text generation App up and running.



GPT-J-6B on Hugging Face - https://huggingface.co/EleutherAI/gpt-j-6B
GPT-J-6B Web App on HF Spaces - https://huggingface.co/spaces/akhaliq/gpt-j-6B
Gradio Docs - https://gradio.app/docs#load
Gradio Hosted - https://gradio.app/introducing-hosted

Courtesy: Ahsen Khaliq

Previous Video on GPT-J and AI Blog Writing:

https://youtu.be/fnvQ8qQM6NU

GPT-2, the Language model that shocked the world with its entirely fictitious story about the unicorns inhabiting a secret South American valley. Rob Miles explains

More on GPT-2: Coming Soon

More from Rob Miles: http://bit.ly/Rob_Miles_YouTube

Thanks to Nottingham Hackspace for providing the filming location: http://bit.ly/notthack

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This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

We are going to talk about the internal workings of ChatGPT and the fundamental concepts it lies on: Language Models, Transformer Neural Networks, GPT models and Reinforcement Learning

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Transformer Neural Networks: https://www.youtube.com/watch?v=TQQlZhbC5ps

RESOURCES
[1] ChatGPT blog: https://openai.com/blog/chatgpt/
[2] Instruct GPT which is the model ChatGPT was modeled after: https://arxiv.org/pdf/2203.02155.pdf
[3] Proximal Policy Optimization is how ChatGPT makes use of human rankings to update model parameters and make it more "safe" and "truthful": https://openai.com/blog/openai-baselines-ppo/
[4] Here is a paper that shows how Reinforcement learning through human feedback actually helps: https://arxiv.org/pdf/2009.01325.pdf
[5] Every timestep, a subword token is generated. Here is some more information on this process with BPE: https://towardsdatascience.com..../byte-pair-encoding-
[6] Basic Concepts in Reinforcement Learning: https://www.baeldung.com/cs/ml....-policy-reinforcemen
[7] Why Does GPT-3 write non-sensical stuff that sounds legit? https://www.alignmentforum.org..../posts/BgoKdAzogxmgk
[8] What is GPT-3.5? https://beta.openai.com/docs/m....odel-index-for-resea



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¿Qué hay tras el entrenamiento de IAs como GPT-3, Alphafold 2 o DALL-E? ¿Qué hace especial a sus redes neuronales? Los Transformers son el tipo de arquitectura de Deep Learning que mejor rendimiento ha dado en los últimos años. ¿Pero por qué? ¿Qué los hacen tan especiales? La respuesta la encontramos en lo altamente paralelizable que es su arquitectura, que permite sacar el máximo partido a los procesadores multinúcleos. Pero, esto tiene un coste, y es que si no hacemos nada los Transformers serían incapaces de entender el orden de los datos con los que los entrenamos. Y de ahí la importancia de soluciones como los Encoding de Posicionamiento. ¡Veamos cómo funcionan!

--- 📣 ¡IMPORTANTE! ---

► ¡Regístrate al Samsung Dev Day y no te pierdas mi ponencia!
https://bit.ly/SDD2021Agenda - 18 Noviembre, 18:30

--- ¡LINKS INTERESANTES! ---

► Serie Introducción al NLP y Transformers (DotCSV)
Parte 1 - https://youtu.be/Tg1MjMIVArc
Parte 2 - https://youtu.be/RkYuH_K7Fx4
Parte 3 - https://youtu.be/aL-EmKuB078

► Explicación con más detalle Positional Encoding:
https://kazemnejad.com/blog/tr....ansformer_architectu

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What will the future of AI programming look like with tools like ChatGPT and GitHub Copilot? Let's take a look at how machine learning could change the daily lives of developers in the near future.

#ai #tech #TheCodeReport

💬 Chat with Me on Discord

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🔗 Resources

ChatGPT Demo https://openai.com/blog/chatgpt
Codex CLI https://github.com/microsoft/Codex-CLI
Tech Trends to Watch in 2023 https://youtu.be/1v_TEnpqHXE

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🔖 Topics Covered

- How will ChatGPT affect programmers?
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- Universal AI programming language
- Using AI from the command line

Join My Weekly Newsletter and Learn How to Use Chat GPT in Your Everyday Life: https://bit.ly/aiadvantagenewsletter

A quick beginner's guide on how to start using Chat GPT by OpenAI.
https://chat.openai.com/

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#chatgpt #howtousechatgpt #chatbotopenai

google colab link
https://colab.research.google.....com/drive/1xyaAMav_g
🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.
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CS596 Machine Learning, Spring 2021
Yang Xu, Assistant Professor of Computer Science
College of Sciences
San Diego State University
Website: clcs.sdsu.edu

Easy natural language generation with Transformers and PyTorch. We apply OpenAI's GPT-2 model to generate text in just a few lines of Python code.

Language generation is one of those natural language tasks that can really produce an incredible feeling of awe at how far the fields of machine learning and artificial intelligence have come.

GPT-1, 2, and 3 are OpenAI’s top language models — well known for their ability to produce incredibly natural, coherent, and genuinely interesting language.

In this article, we will take a small snippet of text and learn how to feed that into a pre-trained GPT-2 model using PyTorch and Transformers to produce high-quality language generation in just eight lines of code. We cover:

PyTorch and Transformers
- Data
Building the Model
- Initialization
- Tokenization
- Generation
- Decoding
Results

🤖 70% Discount on the NLP With Transformers in Python course:
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Medium Article:
https://towardsdatascience.com..../text-generation-wit

Friend Link (free access):
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Thumbnail background by gustavo centurion on Unsplash
https://unsplash.com/photos/O6fs4ablxw8

What if you want to leverage the power of GPT-3, but don't want to wait for Open-AI to approve your application? Introducing GPT-Neo, an open-source Transformer model that resembles GPT-3 both in terms of design and performance.In this video, we'll discuss how to implement and train GPT-Neo with just a few lines of code.

We'll use Happy Transformer to implement GPT-Neo. Happy Transformer is an open-source Python library build on top of Hugging Face's Transformer library to allow programmers to implement state-of-the-art NLP models with just a few lines of code.

Thank you Eleuther AI for creating and training GPT-Neo.

New Course on how to create a web app to display GPT-Neo: https://www.udemy.com/course/n....lp-text-generation-p

Article: https://www.vennify.ai/gpt-neo-made-easy/

Colab: https://colab.research.google.....com/drive/1Bg3hnPOoy

Website: https://www.vennify.ai/

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Control codes to steer your language models into a right direction.

✅ CTRL: A Conditional Transformer Language Model for Controllable Generation from Salesforce: https://arxiv.org/abs/1909.05858

✅ PPLM from Uber: https://eng.uber.com/pplm/

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🔥 Contentyze, a content generation platform: https://contentyze.com/

#salesforce #machinelearning #transformer

This video explains the Transformer architecture in a very detailed way, including most math formulas in the paper, and the neural network operations behind it. The Transformer is the foundation of many powerful language models like BERT, GPT3, RoBERTa, XLNET, ELECTRA, T5. Understanding how it works in detail might help you modify, optimize, or improve it in the way you want.


Connect
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Email edwindeeplearning@gmail.com

0:00 - Intro
1:10 - Architecture overview
1:56 - Encoder
3:58 - Residual connection & layer normalization
6:59 - Decoder
11:14 - Attention mechanism
14:30 - Scaled dot-product attention
20:22 - Learned projection layers
26:32 - Multi-head attention
28-39 - Encoder-decoder attention
31:18 - Encoder self-attention
31:34 - Decoder self-attention
33:58 - Position-wise feedforward network
36:41 - Word embedding
39:34 - Positional encoding
47:37 - Why self-attention

What Is GPT-3 Series
https://www.youtube.com/playli....st?list=PLoS8jSwcU-c

Paper: Attention Is All You Need
https://arxiv.org/abs/1706.03762

Abstract
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best
performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

GPT-J-6B - Just like GPT-3 but you can actually download the weights and run it at home. No API sign-up required, unlike some other models we could mention, right? Any just like GPT-3, this has 3 super easy example ways you can run this -v ia the EleutherAI website, Google Colab TPU or single GPU on a local system.

Buy art & support a nerd!
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Github: https://github.com/kingoflolz/mesh-transformer-jax


Requirements for this guide to running locally:
* Ubuntu 20.04
* 32 GB RAM and an Nvidia GPU with at least 24 GB VRAM
* Anaconda - https://www.anaconda.com/products/individual

== Python virtual environment (Anaconda) ==
conda create --name mesh-transformer-jax python=3.8
conda activate mesh-transformer-jax

== Download & install ==
git clone https://github.com/kingoflolz/mesh-transformer-jax.git
cd mesh-transformer-jax

sudo apt install zstd
wget https://the-eye.eu/public/AI/G....PT-J-6B/step_383500_
tar -I zstd -xf step_383500_slim.tar.zstd

pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install jaxlib==0.1.67+cuda111 -f https://storage.googleapis.com..../jax-releases/jax_re
pip install -r requirements.txt # (Edited to use tensorflow-gpu 2.5.0 for no reason)
pip install jax==0.2.12

Understand the BERT Transformer in and out.

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REFERENCES

[1] BERT main paper: https://arxiv.org/pdf/1810.04805.pdf
[1] BERT in google search: https://blog.google/products/s....earch/search-languag
[2] Overview of BERT: https://arxiv.org/pdf/2002.12327v1.pdf
[4] BERT word embeddings explained: https://medium.com/@_init_/why....-bert-has-3-embeddin
[5] More details of BERT in this amazing blog: https://towardsdatascience.com..../bert-explained-stat
[6] Stanford lecture slides on BERT: https://nlp.stanford.edu/semin....ar/details/jdevlin.p

GPT-3 is the biggest language ever model built, and it has been attracting a lot of attention. Rather than argue about whether GPT-3 is overhyped or not, we wanted to dig in to the literature and understand what GPT-3 is (and is not) in light of it’s predecessors and alternative transformer models. In this video we share some of what we’ve learned. What is GPT-3 really good at? What are its constraints? How useful is it for business? Enjoy!

⏰ Time Stamps ⏰
00:40 - Comparison of latest Natural Language Processing Models
01:09 - What is a Transformer Model
01:50 - The Two Types of Transformer Models
02:15 - Difference between bi-directional encoders (BERT) and autoregressive decoders (GPT)
04:40 - GPT-3 is HUGE, does size matter?
05:24 - Presentation of size differences between GPT-3 relative to BERT, RoBERTa, GPT-2, and T5
07:40 - What does GPT do and how is it different than the BERT family?
18:05 - Is GPT-3 a Child Prodigy or a Parlor Trick?
18:44 - Back to the Issue of GPT-3's size
19:30 - Final thoughts on GPT-3 vs BERT