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🎏 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 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 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
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