# prompt2model

**Repository Path**: mirrors/prompt2model

## Basic Information

- **Project Name**: prompt2model
- **Description**: Prompt2Model 是一个采用自然语言任务描述（如 ChatGPT 等 LLM 使用的提示）来训练有利于部署的小型专用模型的系统
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: https://www.oschina.net/p/prompt2model
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 0
- **Created**: 2023-08-29
- **Last Updated**: 2025-12-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Prompt2Model - Generate Deployable Models from Instructions

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[![Discord](https://img.shields.io/discord/1144245269001678959)](https://discord.gg/UCy9csEmFc)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neulab/prompt2model/blob/main/prompt2model_demo.ipynb)

`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.

<img width="360" alt="prompt2model_teaser" src="https://github.com/neulab/prompt2model/assets/2577384/39ca466a-5355-4d82-8312-303e52ba2bca">

## Quick Start

### Notebook

You can run our demo of `Prompt2Model` through a notebook:

- [Open Locally](./prompt2model_demo.ipynb)
- [Open in Colab](https://colab.research.google.com/github/neulab/prompt2model/blob/main/prompt2model_demo.ipynb)

### Command Line

You can also run through the command line.

```bash
pip install prompt2model
```

`Prompt2Model` supports various platforms such as OpenAI, Anthropic, Huggingface, etc. using [LiteLLM](https://github.com/BerriAI/litellm).

If you are using OpenAI models (such as the default `gpt-3.5-turbo`), please obtain an
OpenAI API key on their [website](https://platform.openai.com/) then set
the environment variable `OPENAI_API_KEY` to your API key by running
the following command in your terminal:

```bash
export OPENAI_API_KEY=<your key>
```

[List of all supported providers](https://docs.litellm.ai/docs/providers)

You can then run

```bash
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](./prompt_examples.md).

## 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

- refer to [CONTRIBUTING.md](CONTRIBUTING.md)
- open an [issue](https://github.com/neulab/prompt2model/issues) or submit a PR
- join us on [discord](https://discord.gg/UCy9csEmFc)
- or reach out to [@vijaytarian](https://twitter.com/vijaytarian)
  and [@Chenan3_Zhao](https://twitter.com/Chenan3_Zhao) on Twitter

## Cite

We have [written a paper describing Prompt2Model in detail](https://arxiv.org/abs/2308.12261).

If you use Prompt2Model in your research, please cite us!

If you discuss or use the overall prompt2model framework, please reference

```bibtex
@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}
}
```

If you discuss or use our dataset retrieval and transformation tools, please reference

```bibtex
@misc{prompt2modeldatatune,
      title={Better Synthetic Data by Retrieving and Transforming Existing Datasets}, 
      author={Saumya Gandhi and Ritu Gala and Vijay Viswanathan and Tongshuang Wu and Graham Neubig},
      year={2024},
      eprint={2404.14361},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```