ChatGPT #Application

• @intigration Client application name proposed as NexaGPT
  • Document the whole process

#Requirements

• #Consumer of OpenAI platform
  • OpenAI API token generation

• #Modren node app could be developed on any Node JS visualization framework such as Vuejs, React or Angular (Tentative Next week)
  • Set up Node.js environment
  • Choose a visualization framework (Vue.js, React, Angular)

    • #React Application

      • Install and configure the chosen framework
      • Create a new project or application
      • Set up basic routing and navigation

      #components

      • Build a login component and home page
        • Integrate the application with firebase
        • Login using provider authentication
        • Test the login functionality

    • Refactor and optimize the code

• The functionality of user prompts needs to be elaborated with various user modes and common problem in the particular space.

• #Prompt Engineering

  • Text generation models

    OpenAI's text generation models (often called generative pre-trained transformers or large language models) have been trained to understand natural language, code, and images. The models provide text outputs in response to their inputs. The inputs to these models are also referred to as "prompts". Designing a prompt is essentially how you “program” a large language model model, usually by providing instructions or some examples of how to successfully complete a task.

    Using OpenAI's text generation models, you can build applications to:

    • Draft documents
    • Write computer code
    • Answer questions about a knowledge base
    • Analyze texts
    • Give software a natural language interface
    • Tutor in a range of subjects
    • Translate languages
    • Simulate characters for games

  • Chat Completions API

    Chat models take a list of messages as input and return a model-generated message as output. Although the chat format is designed to make multi-turn conversations easy, it’s just as useful for single-turn tasks without any conversation.F

  Fine Tuning

  To fine-tune an OpenAI model depending on the application and the resources available. Here are some popular methods to fine-tune OpenAI models:

  • 1. Transfer Learning: Use an OpenAI model that has been pre-trained on massive datasets. Fine-tune the model on your specific dataset.
  • 2. Data Augmentation: Generate new data by applying various techniques such as image translation, rotation, flipping, or adding noise.
  • 3. Fine-tuning the Hyperparameters: Fine-tune the hyperparameters such as the learning rate, batch size, and number of epochs to get the best results.
  • 4. Ensembling: Combine multiple fine-tuned models to improve accuracy and robustness.
  • 5. Progressive Resizing: Start with small image sizes during training and gradually increase the size. This approach helps the model to learn different levels of features.
  • 6. Curriculum Learning: Gradually introduce more complex training samples to the model over time, leading to better prediction accuracy.
  • 7. Mixup and Cutmix: Mixup or cutmix are data augmentation techniques that synthesize parts of different images to make new training samples. This approach leads to more diverse training and enhances resilience against adversarial attacks.

Take a look at offerings and expertise of https://nexaquanta.ai/