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 paramaters.
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

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

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.

gagliardetto/openplayground