🎏 pykoi: Active learning in one unified interface

pykoi is an open-source python library for LLMs providing a unified interface for data & feedback collection, RLHF, and model comparisons.

Installation

To get started with pykoi, you can choose to one of following compute options: CPU (e.g. your laptop) or GPU (e.g. EC2).

Option 1: CPU (e.g. your laptop)

Installation on a CPU is simple if you have conda. If not, install conda for your operating system.

First, create a conda environment on your terminal using:

conda create -n pykoi python=3.10 -y
source activate pykoi

Then install pykoi and the compatible torch version.

pip3 install pykoi
pip3 install torch

Option 2: GPU (e.g. EC2)

If you are on EC2, you can launch a GPU instance with the following config:

• EC2 g4dn.xlarge (if you want to run a pretrained model with 7B parameters)
• Deep Learning Base GPU AMI
• EBS: at least 100G

Next, on your GPU instance terminal, create a conda environment using:

conda create -n pykoi python=3.10 -y && source activate pykoi

Then install pykoi and the correlated torch version.

pip3 install pykoi && pip3 install torch --index-url https://download.pytorch.org/whl/cu118

Congrats you have finished installation,

How do I use pykoi?

pykoi is a python interface to unify your ML model development and production. You can easily get real-time user feedback and continuously improve your model.

Here are some common applications:

Sharable Chatbot UI with Database

Are you concerned about storing your chat history with chatbots from OpenAI, Amazon Bedrock(:woman_technologist: building now :man_technologist:), or Huggingface? With just three lines of code, we enable you to store it locally, ensuring 100% privacy. This includes launching a chatbot UI, automatically saving your chat history, and visualizing it on a dashboard. Explore the demos below:

• If you're using a CPU instance, check out demo_launch_app_api.ipynb
• If you're using a GPU instance, check out demo_launch_app_gpu.ipynb
• Alternatively, read our blog for more information!

Model comparison

Comparing models is a difficult task. pykoi makes it easy by allowing one to directly compare the performance of multiple models to each other, with just 1 line of code. If you have multiple language models that you’d like to compare to each other on a set of prompts or via an interactive session, you can use pk.Compare. Check out any of the demo below:

• If you're using a CPU instance, check out demo_launch_app_api.ipynb
• If you're using a GPU instance, check out demo_launch_app_gpu.ipynb
• Alternatively, read our blog for more information!

RLHF

Reinforcement Learning with Human Feedback (RLHF) is a unique training paradigm that blends reinforcement learning with human-in-the-loop training. The central idea is to use human evaluative feedback to refine a model's decision-making ability and guide the learning process towards desired outcomes. Researchers from Deepmind, OpenAI and Meta Llama2 have all demonstrated that RLHF is a game changer for large language models (LLMs) training.

pykoi allows you to easily fine-tune your model on the datasets you've collected via your pykoi chat or rank databases. Check our blog for detailed instructions on how to use it.

Dev Setup

If you are interested to contribute to us, here are the preliminary development setup.

Backend Dev Setup

conda create -n pykoi python=3.10
conda activate pykoi
cd pykoi
pip3 install poetry
poetry install --no-root

Frontend Dev Setup

Frontend:

cd frontend
npm install vite
npm run build