prompt2model 0.0.0a0

A library for distilling models from prompts.

prompt2model

What is this?

Prompt2model is a powerful package designed to streamline the process of creating models based on prompts indicating a task for OpenAI models such as ChatGPT or GPT-4. This package takes a prompt as input and leverages it to generate a model that can effectively solve the task described in the prompt. With prompt2model, you can easily convert your prompts into functional models, making it easier to utilize the power of general OpenAI LLMs in your projects.

Installation

To install the necessary dependencies, run the following command in your terminal to install the package using pip:

pip install .

Configuration

Before using prompt2model, there is a few configuration steps you need to complete:

• Sign up on the OpenAI website and obtain an OpenAI API key.

• Provide OpenAI API key in the initialization function of the classes which requires calling OpenAI Models.

• Alternatively, you can set the environment variable OPENAI_API_KEY to your API key by running the following command in your terminal:

export OPENAI_API_KEY=<your key>

• After setting the environment Variable OPENAI_API_KEY, just reference load it in your Python:

import os
import openai

openai.api_key = os.environ["OPENAI_API_KEY"]

To enable the model retriever, we need to untar the model_info.tgz file:

cd huggingface_models
tar -xvf model_info.tgz

Components

The prompt2model package is composed of several components, each designed to fulfill a specific purpose. To gain a comprehensive understanding of how to utilize each component effectively, please consult the readme.md file situated in the directory of the respective component. These files can be found at ./prompt2model/<component>/readme.md. They provide detailed information and instructions on maximizing the functionality and benefits of each component within the package.

Usage

The prompt2model pipeline is a versatile pipeline for task solving using a language model. It covers stages including dataset retrieval, generation, processing, model retrieval, training, execution, evaluation, and interface creation. The .cli_demo.py By directly run python cli_demo.py, users can efficiently leverage language models for various tasks by customizing the components according to their specific requirements.

How to Write a Good Prompt

A good prompt can make the generated dataset follow exactly the format of demonstrations. It contains the instruction and few-shot examples.

The instruction should contain the following:

1. The exact format description for the input and output, i.e., a string, a dictionary, or whatever.
2. The exact contents of each part of the input and their relationship as possible as you can.
3. The range of possible input. For example, "And the question can range from Math, Cultural, Social, Geometry, Biology, History, Sports, Technology, Science, and so on."

The few-shot examples should contain the following:

1. Use = rather than other ambiguous symbols like :.
2. Avoid unnecessary line breaks at the beginning. For example, input="" is better than breaking the line after =.
3. Use input rather than Input, ouput is preferable likewise.
4. Warp the input and output into a string with “”.

Though the examples are optional, we strongly suggest including them to guide the format and content for the generator.

Also, we recommend providing several precise examples in the specified format and inquiring with ChatGPT about the format and scope of your examples.

Customization

If you want to customize a specific component, see the relevant doc page and class document string.

Contribution

If you're interested in contributing to the prompt2model project, please refer to the CONTRIBUTING.md file for detailed guidelines and information tailored specifically