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Host Your Own Large Language Models with Listen Not

Host and use powerful large language models (LLMs) like Whisper v3 for tasks such as audio transcription, all without relying on third-party APIs. Build a web application leveraging the scalability and power of Modal Server, a serverless platform for deploying LLMs.

šŸŒŸ Core Features

  1. šŸš€ Deploy Whisper v3 with Flash Attention v2

    • Deploy Whisper v3, a state-of-the-art audio transcription model, enhanced with Flash Attention v2 for fast and accurate transcription.

  2. šŸ Use Python and FastAPI to Deploy Serverless LLM Functions

    • Utilize Python and FastAPI to create and deploy serverless functions on Modal Server, enabling scalable and efficient LLM deployments.

  3. šŸ’¬ Talk to Your Own Hosted Models

    • Interact with your hosted models directly from your client application, providing full control and customization of your AI-powered functionalities.

šŸ”§ Tech Stack

To build and deploy this application, we will use the following tech stack:

  • Next.js: For building a server-rendered React application.
  • OpenAI: Leveraging Whisper v3 for audio transcription.
  • WhisperV3: A cutting-edge model for accurate and fast audio transcription.
  • Modal GPU: A serverless platform for deploying LLMs with GPU support.
  • TypeScript: Adding type safety to our JavaScript code.
  • Python: For backend development and model deployment.
  • FastAPI: A modern, fast (high-performance) web framework for building APIs with Python.
  • Tailwind CSS: A utility-first CSS framework for rapid UI development.
  • Shadcn: Enhancing our UI with accessible and customizable components.
  • Containerization: Ensuring consistent and scalable deployments using containerization technologies.

šŸ“š Detailed Walkthrough

1. Deploying Whisper v3 with Flash Attention v2

Deploy Whisper v3, a robust audio transcription model. Flash Attention v2 is integrated to improve the speed and accuracy of transcriptions, making it ideal for real-time applications. Set up and optimize this model for the best performance.

2. Building the Backend with Python and FastAPI

Use Python and FastAPI to create serverless functions that handle LLM deployments. FastAPI is chosen for its speed and ease of use, allowing efficient build and deployment of endpoints on the Modal Server. Structure your API endpoints and handle requests for audio transcription tasks.

3. Interacting with Your Hosted Models

Build the client-side application using Next.js and TypeScript, which interacts with your hosted models. Set up the frontend to send audio data to the backend for transcription and display the results in real time.

4. User Interface with Tailwind CSS and Shadcn

Use Tailwind CSS to rapidly build a responsive and attractive user interface. Shadcn will help enhance the UI with accessible and customizable components, ensuring a great user experience.

5. Containerization for Consistent Deployments

Ensure your application is scalable and easily deployable across different environments using containerization technologies. Package your entire application, including all dependencies, ensuring it runs consistently wherever it's deployed.

#LLM #WhisperV3 #Nextjs #OpenAI #ModalServer #FastAPI #TypeScript #Python #TailwindCSS #Shadcn #Containerisation #AI #webdev #programming

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