aws-neuron-sdk VS OpenAI-DotNet

We will focus on the first model used within this PubNub Function, which is “distilbert-base-uncased-finetuned-sst-2-english”. To learn more about this function, navigate to distilbert-base-uncased-finetuned-sst-2-english or the model it is based on, which is distilbert/distilbert-base-uncased. You can also check out the model on github. This model will do all the heavy lifting for us and determine the sentiment analysis of a PubNub message when sent through the network.

Sounds like you install the relevant version of pytorch and then convert the model. They have the SDK and some examples: https://github.com/aws-neuron/aws-neuron-sdk/blob/master/src/examples/pytorch/torch-neuronx/resnet50-inference-on-trn1-tutorial.ipynb

AWS Neuron is the SDK for running machine learning inference using AWS Inferentia chips, and last week we announced an important change in how you install and update this SDK. To find out more, read End of support for Neuron Conda packages in Deep Learning AMI

To use the AI capabilities of Cover-Agent, you need an API key from OpenAI. Follow these steps to obtain and set up your API key:

Introduction Recent developments in AI-driven conversational agents have sparked debates about the future of platforms like ChatGPT. With Google's unveiling of Project Astra and its integration with Gemini across its product suite, questions arise about the potential impact on existing players like OpenAI. This article delves into the implications of Google's advancements and how OpenAI may respond to maintain its competitive edge.

Okay, now we've confirmed the API endpoint is working, let's connect it to OpenAI first, install the OpenAI package, navigate to the route directory, and run the command below in our terminal

You can obtain an OpenAI API key by visiting the OpenAI website.

OpenAI Account: Register for an OpenAI account to utilize AI-powered features in your ToolJet applications. Sign up here.

KodiBot is a desktop app that enables users to run their own AI chat assistants locally and offline on Windows, Mac, and Linux operating systems. KodiBot is a standalone app and does not require an internet connection or additional dependencies to run local chat assistants. It supports both Llama.cpp compatible models and OpenAI API.

At this point, probably everyone has heard about OpenAI, GPT-4, Claude or any of the popular Large Language Models (LLMs). However, using these LLMs in a production environment can be expensive or nondeterministic regarding its results. I guess that is the downside of being good at everything; you could be better at performing one specific task. This is where HuggingFace can utilized. HuggingFace provides open-source AI and machine learning models that can easily be deployed on HuggingFace itself or third-party systems such as Amazon SageMaker or Azure ML.  You can interface with these deployments through an API and control the scaling of these models, which makes them perfectly suited for production environments. These models range in size but are generally small AI models that are good at doing one specific task. With capabilities to fine-tune these models, or use the pre-trained model for specific tasks, embedding them into various applications becomes more efficient, enhancing automation and performance. Combining these models can create new and intricate AI applications. In this case, by utilizing HuggingFace models, you wouldn’t have to depend on a production application on a third-party provider such as OpenAI or Google, ensuring a more targeted and customizable approach to deploying deep learning solutions in your operations.