🎏 pykoi: RLHF/RLAIF in one unified interface

pykoi is an open-source python library for improving LLMs with RLHF. We provide a unified interface including RLHF/RLAIF data and feedback collection, finetuning with reinforcement learning and reward modeling, and LLM comparisons.

Features

pykoi let you easily get real-time user feedback and continuously improve your models. Here are some common applications:

Sharable UI

Do you want to store your chat history with LLMs from OpenAI, Amazon Bedrock(:woman_technologist: building now :man_technologist:), or Huggingface? With just three lines of code, pykoi lets you to store them locally, ensuring 100% privacy. This includes launching a chatbot UI, automatically saving your chat history in your compute instance (cpu or gpu), and visualizing it on a dashboard. Explore the demos below:

• If you're using a CPU instance, check out demo_launch_app_cpu.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 a few lines 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_cpu.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.

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
conda activate pykoi

Then install pykoi and the compatible pytorch based on your os

pip3 install pykoi
pip3 install torch 

Option 2: GPU (e.g. EC2 or SageMaker)

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 LLM with 7B parameters)
• Deep Learning AMI PyTorch GPU 2.0.1 (Ubuntu 20.04)
• 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 pytorch based on your cuda version.

pip3 install pykoi

# install torch based on cuda (e.g. cu118 means cuda 11.8)
pip3 install torch --index-url https://download.pytorch.org/whl/cu118

Congrats you have finished the installation!

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 pykoi/pykoi/frontend
npm install
npm run build