Introducing pykoi: A Unified Interface for RLHF and RLAIF
Overview
Pykoi is an open-source Python library designed to enhance Large Language Models (LLMs) using Reinforcement Learning with Human Feedback (RLHF). It's a comprehensive tool that offers a unified interface for RLHF and Reinforcement Learning with Artificial Intelligence Feedback (RLAIF), providing functionalities for data collection, feedback systems, model fine-tuning, and comparison.
Key Features
Sharable UI
Pykoi facilitates the seamless storage and management of chat histories with LLMs from platforms like OpenAI or Huggingface. In just three lines of code, users can create a chatbot interface that saves chat histories locally, maintaining user privacy. The tool supports visualization of interactions via a dashboard, allowing users to easily manage and retrieve past chats. Example demos are available for both CPU and GPU instances.
Model Comparison
The tool simplifies the complex task of model comparison by enabling users to evaluate the performance of multiple LLMs efficiently. By using the pk.Compare feature, users can examine models based on prompt responses or through interactive sessions, streamlining the evaluation process. Demos and additional reading material help guide users through the comparison process.
Reinforcement Learning with Human Feedback (RLHF)
RLHF combines the benefits of human input with traditional reinforcement learning to refine a model's decision-making processes. This methodology has been recognized by researchers from organizations like Deepmind and OpenAI as transformative for training LLMs. Pykoi supports users in fine-tuning their models with datasets collected through its chatbot functionality, offering detailed guidance to optimize this powerful approach.
Retrieval-Augmented Generation (RAG)
With Pykoi, users can quickly implement a Retrieval-Augmented Generation (RAG) chatbot. This feature enables the integration of user-uploaded documents to generate context-aware responses, enhancing the capabilities of pre-trained LLMs. Pykoi showcases how to use this feature effectively through a thoughtful sequence of demo resources.
Installation Options
Pykoi provides several installation options tailored to the user's computational resources and feature needs:
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RAG on CPU: Run RAG using OpenAI or Anthropic Claude2 APIs on a CPU. Installation requires creating a Conda environment and installing Pykoi along with PyTorch.
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RAG on GPU: This option enables RAG on a GPU using Huggingface's open-source LLMs. Setup involves establishing a Conda environment on a GPU instance and installing necessary dependencies.
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RLHF on GPU: Focuses on training LLMs with RLHF on a GPU, requiring similar GPU-dependent setup.
Each installation step is detailed to guide users through the process depending on their environment, whether CPU or GPU-based.
Development Setup
Pykoi is open for contributions and encourages developers to set up their development environment. Backend and frontend setup instructions are provided, ensuring smooth integration and development across platforms. EC2 development setup instructions are also given for expansive GPU-based projects, ensuring comprehensive guidance for all user levels.
Pykoi stands out as a pioneering tool in the enhancement of LLMs, focusing on integrating human feedback loops efficiently within reinforcement learning frameworks. Whether you're a researcher seeking model refinement or an enthusiast eager to explore AI capabilities, Pykoi offers robust features to elevate your machine learning tasks.