Skip to main content

A library for distilling models from prompts.

Project description

Prompt2Model - Generate Deployable Models from Instructions

PyPI version Github Actions CI tests MIT license Discord Colab

Prompt2Model is a system that takes a natural language task description (like the prompts used for LLMs such as ChatGPT) to train a small special-purpose model that is conducive for deployment.

prompt2model_teaser

Quick Start

Notebook

You can run our demo of Prompt2Model through a notebook:

Command Line

You can also run through the command line.

pip install prompt2model

Prompt2Model supports various platforms such as OpenAI, Anthropic, Huggingface, etc. using LiteLLM.

If you are using OpenAI models (such as the default gpt-3.5-turbo), please obtain an OpenAI API key on their website then 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>

List of all supported providers

You can then run

python prompt2model_demo.py

to create a small model from a prompt, as shown in the demo video below. This script must be run on a device with an internet connection to access the OpenAI API. For best results, run this script on a device with a GPU for training your model.

Demo

https://github.com/neulab/prompt2model/assets/2577384/8d73394b-3028-4a0b-bdc3-c127082868f2

Tips and Examples to Write a Good Prompt

You can see the tips and examples to write a good prompt in prompt_examples.

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 customizing and maximizing the functionality of each component within the package.

Contribution

If you're interested in contributing to the prompt2model project, please

Cite

We have written a paper describing Prompt2Model in detail.

If you use Prompt2Model in your research, please cite our paper:

@misc{prompt2model,
      title={Prompt2Model: Generating Deployable Models from Natural Language Instructions},
      author={Vijay Viswanathan and Chenyang Zhao and Amanda Bertsch and Tongshuang Wu and Graham Neubig},
      year={2023},
      eprint={2308.12261},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

prompt2model-0.0.7.tar.gz (58.3 kB view details)

Uploaded Source

Built Distribution

prompt2model-0.0.7-py3-none-any.whl (82.2 kB view details)

Uploaded Python 3

File details

Details for the file prompt2model-0.0.7.tar.gz.

File metadata

  • Download URL: prompt2model-0.0.7.tar.gz
  • Upload date:
  • Size: 58.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for prompt2model-0.0.7.tar.gz
Algorithm Hash digest
SHA256 0a09560bdf473321a89d400e7b2bee3db10b08274e05fdc18bc20975e27be421
MD5 ae51f08bd0ed76ccfdb596c635696193
BLAKE2b-256 ded49694319236d9c87c24679ad29d25f89251d166c2fba2df582a45b0768ff8

See more details on using hashes here.

File details

Details for the file prompt2model-0.0.7-py3-none-any.whl.

File metadata

  • Download URL: prompt2model-0.0.7-py3-none-any.whl
  • Upload date:
  • Size: 82.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for prompt2model-0.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 d0bd452001c192200b089d1afecaa9a7101cabaa88670fe1a1f0c60a6a3349a2
MD5 175c44ab631cdc42478711ea8f385715
BLAKE2b-256 7f671cbd2e86c9078fa0f14da4828780ec8c5fe346262e3cd09c178bd83b7ae8

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page