# 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](), [Anthropic](), [Cohere](), [Forefront](), [HuggingFace](), [Aleph Alpha](), and [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 <model>` 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.
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