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https://github.com/nat/openplayground

An LLM playground you can run on your laptop
https://github.com/nat/openplayground

Last synced: 3 days ago
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An LLM playground you can run on your laptop

Host: GitHub
URL: https://github.com/nat/openplayground
Owner: nat
License: mit
Created: 2023-02-26T22:58:17.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-10-28T06:00:42.000Z (about 2 months ago)
Last Synced: 2024-10-29T15:04:46.720Z (about 1 month ago)
Language: TypeScript
Homepage:
Size: 946 KB
Stars: 6,240
Watchers: 61
Forks: 486
Open Issues: 88
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

# openplayground

An LLM playground you can run on your laptop.

https://user-images.githubusercontent.com/111631/227399583-39b23f48-9823-4571-a906-985dbe282b20.mp4

#### Features

- Use any model from [OpenAI](https://openai.com), [Anthropic](https://anthropic.com), [Cohere](https://cohere.com), [Forefront](https://forefront.ai), [HuggingFace](https://huggingface.co), [Aleph Alpha](https://aleph-alpha.com), [Replicate](https://replicate.com), [Banana](https://banana.dev) and [llama.cpp](https://github.com/ggerganov/llama.cpp).
- Full playground UI, including history, parameter tuning, keyboard shortcuts, and logprops.
- Compare models side-by-side with the same prompt, individually tune model parameters, and retry with different parameters.
- Automatically detects local models in your HuggingFace cache, and lets you install new ones.
- Works OK on your phone.
- Probably won't kill everyone.

## Try on nat.dev

Try the hosted version: [nat.dev](https://nat.dev).

## How to install and run

```sh
pip install openplayground
openplayground run
```

Alternatively, run it as a docker container:
```sh
docker run --name openplayground -p 5432:5432 -d --volume openplayground:/web/config natorg/openplayground
```

This runs a Flask process, so you can add the typical flags such as setting a different port `openplayground run -p 1235` and others.

## How to run for development

```sh
git clone https://github.com/nat/openplayground
cd app && npm install && npx parcel watch src/index.html --no-cache
cd server && pip3 install -r requirements.txt && cd .. && python3 -m server.app
```

## Docker

```sh
docker build . --tag "openplayground"
docker run --name openplayground -p 5432:5432 -d --volume openplayground:/web/config openplayground
```

First volume is optional. It's used to store API keys, models settings.

## Ideas for contributions

- Add a token counter to the playground
- Add a cost counter to the playground and the compare page
- Measure and display time to first token
- Setup automatic builds with GitHub Actions
- The default parameters for each model are configured in the `server/models.json` file. If you find better default parameters for a model, please submit a pull request!
- Someone can help us make a homebrew package, and a dockerfile
- Easier way to install open source models directly from openplayground, with `openplayground install ` or in the UI.
- Find and fix bugs
- ChatGPT UI, with turn-by-turn, markdown rendering, chatgpt plugin support, etc.
- We will probably need multimodal inputs and outputs at some point in 2023

### llama.cpp

## Adding models to openplayground

Models and providers have three types in openplayground:

- Searchable
- Local inference
- API

You can add models in `server/models.json` with the following schema:

#### Local inference

For models running locally on your device you can add them to openplayground like the following (a minimal example):

```json
"llama": {
"api_key" : false,
"models" : {
"llama-70b": {
"parameters": {
"temperature": {
"value": 0.5,
"range": [
0.1,
1.0
]
},
}
}
}
}
```

Keep in mind you will need to add a generation method for your model in `server/app.py`. Take a look at `local_text_generation()` as an example.

#### API Provider Inference

This is for model providers like OpenAI, cohere, forefront, and more. You can connect them easily into openplayground (a minimal example):

```json
"cohere": {
"api_key" : true,
"models" : {
"xlarge": {
"parameters": {
"temperature": {
"value": 0.5,
"range": [
0.1,
1.0
]
},
}
}
}
}
```

Keep in mind you will need to add a generation method for your model in `server/app.py`. Take a look at `openai_text_generation()` or `cohere_text_generation()` as an example.

#### Searchable models

We use this for Huggingface Remote Inference models, the search endpoint is useful for scaling to N models in the settings page.

```json
"provider_name": {
"api_key": true,
"search": {
"endpoint": "ENDPOINT_URL"
},
"parameters": {
"parameter": {
"value": 1.0,
"range": [
0.1,
1.0
]
},
}
}
```

#### Credits

Instigated by Nat Friedman. Initial implementation by [Zain Huda](https://github.com/zainhuda) as a repl.it bounty. Many features and extensive refactoring by [Alex Lourenco](https://github.com/AlexanderLourenco).