rocBLAS VS askai

The same is also true for https://github.com/ROCmSoftwarePlatform/rocBLAS and https://github.com/ROCmSoftwarePlatform/hipBLASLt although the build stack, distribution— leaves a lot to be desired, and otherwise quite unstable.

Full circle eh. I wonder how well it compares to just trying to use the actual Whisper models on a variety of existing Gpu capable bigger frameworks.

I don't know much practically about how hard it would be to take the Whisper PyTorch (1 or 2?) trained models & to make good use of them elsewhere. I expect Whisper.cpp probably better caters to users, is more readily consumable.

Fwiw, Whisper.cpp uses Nvidia's cuBLAS. There does appear to be an AMD rocm port. https://github.com/ROCmSoftwarePlatform/rocBLAS

It's not what you asked, but I felt I should point out that rocBLAS is no longer maintained for gfx803 (the architecture of the RX 570) and PyTorch depends on rocBLAS. PyTorch will work at least to some extent, but there are known bugs that may never be fixed. I've been trying to change this, but that's how things are right now.

The last release that officially supported gfx803 was ROCm 3.5. All testing on that hardware ceased shortly after said release, and the code paths for that architecture have been unmaintained for nearly two years. For a specific example of a problem you may encounter, see: https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1218

Although the code is still there, support for (slightly) older devices are already suffering from lack of maintainence and bugs. For instance there's a bug causing gfx803 devices to produce wrong outputs starting from mid-2020, and I'm pretty sure they're never gonna fix it.

This API allows you to interact with ChatGPT programmatically, and I've built some cool agents on top of it. Check out the code and let me know what you think! :

ChatGPT unofficial API :

This project is a Node.js application that interacts with the ChatGPT conversational AI model using Puppeteer, a Node.js library for automating web browsers.

Files chatgptv1.js: This file contains the main logic for the ChatGPT bot, including methods for initializing the browser, sending messages, receiving replies, and handling errors.

bart.js: This file contains a function that uses the Cloudflare API to summarize the conversation history when an error occurs, in order to resume the conversation.

twochatbotsconv.js: This file is simple use of the API , which creates two instances of the ChatGPT class, initiates a conversation between them, and saves the conversation history to a file.

.env: This file contains the API token for the Cloudflare API, which is used in the bart.js file.

Dependencies :

puppeteer: A Node.js library for automating web browsers. fs: The built-in file system module in Node.js. winston: A logging library for Node.js. crypto: The built-in cryptography module in Node.js. axios: A popular HTTP client library for Node.js. dotenv: A zero-dependency module that loads environment variables from a .env file.

Usage:

Install the dependencies by running npm install in your project directory. Create a .env file in the project directory and add your Cloudflare API token:

API_TOKEN=YourfreeCloudFlareAPIToken In your code, create a new instance of the ChatGPT class and use the sendMessage and getReply methods to interact with the ChatGPT model:

const ChatGPT = require('./chatgptv1');

const chatgpt = new ChatGPT(); await chatgpt.initializeBrowser();

await chatgpt.sendMessage('Hello, ChatGPT!'); const reply = await chatgpt.getReply(); console.log(reply);

await chatgpt.closeBrowser(); If an error occurs during the conversation, the handleError method will attempt to save the conversation history and resume the conversation using the summarized context.

Before Running :

run Google chrom in the debug mode using 9220 port , run : google-chrome-stable --remote-debugging-port=9222

Customization :

You can customize the behavior of the ChatGPT bot by passing options to the ChatGPT constructor:

chatbotUrl: The URL of the ChatGPT interface (default: 'https://chat.openai.com/'). headless: Whether to run the browser in headless mode (default: false). saveConversationCallback: A callback function that will be called with the conversation summary and the conversation file name when an error occurs.

License:

This project is licensed under the MIT License.

After wrestling with it for a bit and eventually giving up, I instead turned to ChatGPT to see if it is smart enough for the task. With my free plan, I asked ChatGPT 3.5 the following:

Go to chat.openai.com and log in

In this tutorial, we will learn how to use Strapi, ChatGPT, and Next.js to build an app that displays recipes using AI.

Maybe I am bit dim, but how one can choose GPT-4 Turbo? Is this available from https://chat.openai.com/ ?