openplayground

An LLM playground you can run on your laptop.

all-features.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.

How to install and run

$ pip install openplayground
$ openplayground run

Alternatively, run it as a docker container:

$ 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

$ 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

$ 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):

"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):

"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.

"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 as a repl.it bounty. Many features and extensive refactoring by Alex Lourenco.