ChatGPT Plugin, GPT-4 in Azure OpenAI & Fine-tuning GPT-3

Generative AI with Python Autoencoders using GANs                                                                                                                                                                                                                                                                                                                                                                                                                 [Open in app]( or [online]() [ChatGPT Plugin, GPT-4 in Azure OpenAI & Fine-tuning GPT-3]( Generative AI with Python Autoencoders using GANs Mar 24   [Save]( [▷  Listen](   👋 Hey, "The rise of large language models like GPT-3 represents a breakthrough in AI that has the potential to transform the way we live and work." - [Yoshua Bengio, AI researcher and Turing Award winner.]( As AI technology rapidly evolves, large language models like GPT-4 open up new possibilities for human productivity and creativity. It shows how we explore new frontiers with unprecedented power. We live in the most exciting time in human history! This week's edition is centered around discovering the optimal method for fine-tuning large language models and delving deeply into code samples, tips, and techniques to effectively address data challenges and unlock the full potential of these models. Key Highlights: - [ChatGPT plugins]( - [GPT-4 in Azure OpenAI Service]( - [Fine-tuning GPT-3]( If you are interested in sharing ideas to foster the growth of the professional data community, then this survey is for you. Consider it as your space to share your thoughts and get a FREE bestselling Packt book, The Applied Artificial Intelligence Workshop as PDF. Jump on in! [TELL US WHAT YOU THINK]( Cheers, Merlyn Shelley Associate Editor in Chief, Packt Latest Forks on GitHub MLOps - [microsoft]( Prompting ChatGPT for Multimodal Reasoning and Action. - [activeloopai]( Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. - [allegroai]( ClearML - Model-Serving Orchestration and Repository Solution. - [tloen]( This repository contains code for reproducing the [Stanford Alpaca]( results using [low-rank adaptation (LoRA)](. Data Handling with LLMs - [deepset-ai]( An open-source NLP framework to interact with your data using Transformer models and LLMs (GPT-3 and alike). - [heartexlabs]( Label Studio is a multi-type data labeling and annotation tool with standardized output format. - [qdrant]( Qdrant - Vector Search Engine and Database for the next generation of AI applications. - [FMInference]( Running large language models on a single GPU for throughput-oriented scenarios. [Pledge your support]( Industry Insights AWS ML - [[Automate] Amazon Rekognition Custom Labels model training:]( [AWS Step Functions]( can automate the creation of a dataset, train, evaluate and use a [Rekognition Custom Labels model]( allowing engineers to simplify custom label classification for computer vision use cases. This open-sourced code workflow makes real-time predictions possible. Microsoft Azure - [[Launch] GPT-4 in Azure OpenAI Service:]( The organizations who are using Azure OpenAI Service can now join the waitlist to access GPT-4, OpenAI's most advanced model yet, along with GPT-3.5, ChatGPT, and DALL•E 2. These AI models are supported by Azure's AI-optimized infrastructure, compliance, data security, privacy controls, and integrations with other Azure services. Google Cloud - [[Monitor] Bigtable: Client-side metrics:]( in large scale systems can lead to missed business opportunities, poor customer experiences, and disrupted data pipelines. This blog post discusses how to gain deeper insights into applications with Bigtable client-side metrics and provides scenarios for debugging and addressing issues using those metrics. Reading from the UK or the US? Check out our offers on [Amazon.com]( and [Amazon.co.uk]( Just for Laughs! What did the GPT model say when it was asked if it's feeling okay? "I'm feeling hyperparameter-optimized!" Understanding Core Concepts Fine-tuning GPT-3 - By [Denis Rothman]( Fine-tuning is the key to making GPT-3 your own application, to customizing it to make it fit the needs of your project. Fine-tuning GPT-3 involves two phases: - Preparing the data - Fine-tuning a GPT-3 model Preparing the data Open Fine_Tuning_GPT_3.ipynb in Google Colab in the GitHub chapter directory. OpenAI has documented the data preparation process in detail: [(. Step 1: Installing OpenAI try: import openai except: !pip install openai import openai import openai Step 2: Entering the API key openai.api_key="[YOUR_KEY]" Step 3: Activating OpenAI’s data preparation module OpenAI detects that the file is a CSV file and will convert it to a JSONL file. JSONL contains lines in plain structured text. Fine-tuning GPT-3 Step 4: Creating an OS environment. import openai import os os.environ['OPENAI_API_KEY'] =[YOUR_KEY] print(os.getenv('OPENAI_API_KEY')) Step 5: Fine-tuning OpenAI’s Ada engine !openai api fine_tunes.create -t "kantgpt_prepared.jsonl" -m "ada" Step 6: Interacting with the fine-tuned model. !openai api completions.create -m ada:[YOUR_MODEL INFO] "Several concepts are a priori such as" We have fine-tuned GPT-3, which shows the importance of understanding transformers and designing AI pipelines with APIs. This curated content was taken from the book [Transformers for Natural Language Processing - Second Edition (packtpub.com)](. To learn more, click on the button below. [SIT BACK, RELAX & START READING!]( Find Out What’s New? - [ChatGPT [plugins]:]( ChatGPT is gradually rolling out plugins to study their real-world impact, [safety, and alignment challenges]( in line with their [iterative deployment]( philosophy. Allowing language models to read information from the internet expands the amount of content they can access, beyond their training data to fresh real-world scenarios from the present day. Plus this has been [motivated]( by their own past work in [WebGPT]( as well as [GopherCite]( [BlenderBot2]( [LaMDA2]( and [others](. - [[4 - Approaches] to build on top of Generative AI Foundational Models:]( Generative AI models like Google Flan T5 and Meta's LLaMA are trained on internet content and may contain biases. This article discusses how Google Flan T5 is the most advanced fine-tuneable model for commercial use, and Meta's LLaMA is the most sophisticated model for non-commercial use. And emphasis that API gateways will help in making sure that ML model endpoints are used appropriately. - [[Hands-on] Generative AI with GANs using Python Autoencoders:]( To learn about generative AI, understanding Generative Adversarial Networks (GANs) is crucial, and prior knowledge of Autoencoders can be beneficial. [Thispersondoesnotexist.com]( is a well-known example of generative networks. [Ian Goodfellow]( proposed the GAN concept in his 2014 paper "[Generative Adversarial Nets](. This will give you a fair idea of how to use Python Autoencoders in developing Generative AI. - [[75 - Code Samples] MLOps -Tips and Tricks:]( The article offers MLOps and data engineering tips with code samples and use cases for model training, data preprocessing, performance optimization, monitoring, and deployment. With these ample ideas, you will gain an in-depth understanding of evolving MLOps tools and processes. - [[The AI Clinician] - Google Med-PaLM]( Google conducted a study to test the use of a large language model in encoding clinical knowledge for [medical question answering](. The study aimed to evaluate the model's potential in understanding medical context, finding relevant information, and providing high-quality answers to medical questions, which is a fundamental but challenging task. See you next time! As a GDPR-compliant company, we want you to know why you’re getting this email. The _datapro team, as a part of Packt Publishing, believes that you have a legitimate interest in our newsletter and its products. Our research shows that you opted-in for email communication with Packt Publishing in the past and we think your previous interest warrants our appropriate communication. 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