Fine-tuning ChatGPT Pipeline
Fine-tuning ChatGPT and Benefits for Pioneering Prompt Engineers
Fine-tuning the ChatGPT model involves adjusting the model's parameters using additional training data and optimizing the model for a specific task.
To build a chatbot we need to fine-tune the model. We use ChatGPT by OpenAI because it is a type of technology that is used to build chatbots.
Chatbots are computer programs that can communicate with humans through text messages and alternatively by voice. Chatbots are often used by businesses for several reasons:
- to answer customer questions,
- to provide information, or
- offer support.
But how does ChatGPT work? ChatGPT is a variant of GPT (Generative Pre-training Transformer). This is a type of machine learning model that is well-suited for chatbot applications.
However, the performance can be enhanced by fine-tuning the model.
ChatGPT is trained using a combination of supervised and unsupervised techniques, which means that it can generate responses based on the context of a conversation and the information it has learned from its training data.
Fine-tuning allows ChatGPT to produce responses that are more appropriate for a conversational setting, considering the context of the conversation and generating responses that flow naturally within that context.
Businesses can build chatbots that can engage in natural language conversations with customers and provide accurate and engaging responses. ChatGPT is that good, right now. And, even better with fine-tuning of the model.
These chatbots can be used in a variety of applications, such as:
- member-services
- search
- entertainment
Pipeline Enumeration
The process guides the prompting of the synthetic dataset which is then cleaned and uploaded to be used when fine-tuning a ChatGPT model.
The aim of the Pipeline enumeration is to keep track of prompts and the responses of prompts and then the production of those responses.
Nevertheless, it just seems like there are a lot of prompts being flung around. Fine-tuning may be about keeping a leash on the language model.
// defines a set of processes in pipeline
#[derive(Debug)]
enum Pipeline {
UseCasePrompt,
UXDesignDocs,
PromptDatasets,
ChatGPTAPIScripts,
RawDesigns,
ProductionRequest,
MediaMarketplace,
PublisherEditorial,
}
This enumeration is maintained by the state machine, which ensures that only one element of the enumeration is processed at a time, and the state machine also is coded so that the elements are processed in sequential order.
Below is the first go at the Pipeline State Machine that operates the Pipeline enumeration.
// start the pipeline
// process each process in pipeline
impl Pipeline {
pub fn start() -> Pipeline {
}
pub fn process(&self) -> Pipeline {
// process
// if process complete, then next element
}
}
Well, that is all I have got right now. More soon! Developing!