Open-ChatGPT: An Open-Source Implementation of ChatGPT
Introduction
Open-ChatGPT is an open-source library designed to help users train their own ChatGPT-like AI models using their unique data and minimal computational resources. This library acts as a comprehensive framework, facilitating an end-to-end training experience for models akin to ChatGPT. It automates the process of transforming your chosen pre-trained large language models through a sequence of stages similar to OpenAI's InstructGPT, resulting in a high-quality ChatGPT-style model.
One of the notable features of Open-ChatGPT is its implementation of Reinforcement Learning with Human Feedback (RLHF), powered by the Transformer library and DeepSpeed. This enables distributed training and offloading capabilities, making it possible to handle extremely large models efficiently.
The project encourages community engagement and invites users to show support by leaving a star on their GitHub repository.
News Updates
Open-ChatGPT has seen several updates over time:
- May 2023: Implemented Stanford Alpaca LoRA and Stanford Alpaca.
- April 2023: Released the RLHF Pipeline.
- March 2023: Introduced the OpenChatGPT library to train ChatBot models similar to ChatGPT.
Data Collection
Instruction Datasets
Open-ChatGPT utilizes various open-source instruction datasets to train text-based and multi-modal chat-based large language models (LLMs) like GPT-4, ChatGPT, LLaMA, and Alpaca. These datasets are categorized by language, task, and generation method to help identify the most suitable set for your needs.
RLHF Datasets
The RLHF datasets are crucial for training instruction-following LLMs. These datasets provide a comprehensive resource for researchers and developers, facilitating access and utilization for instruction tuning across various language models.
Data Preprocessing and Formatting
The project provides a unified data preprocessing interface for different large language models. This includes options to preprocess data for tasks like Instruction Tuning and RLHF modeling. All data in this collection are formatted using consistent templates, ensuring compatibility and ease of use.
Installation
To get started with Open-ChatGPT, clone the repository and install the necessary requirements. It also offers optional installations, such as PEFT for LORA and DeepSpeed for accelerated training.
Instruction Fine-tuning
Fine-tuning Alpaca-7B
Open-ChatGPT allows fine-tuning of models like LLaMA-7B and LLaMA-13B using standard Hugging Face training scripts. It provides detailed commands and hyperparameters to tune models efficiently using available hardware resources.
Using DeepSpeed
For users encountering out-of-memory errors, Open-ChatGPT suggests using DeepSpeed to reduce memory requirements through techniques such as pipeline parallelism, gradient checkpointing, and tensor fusion.
Fine-tuning Alpaca-7B with Lora
The project offers a method to apply low-rank adaptation (LoRA), which is a cost-effective way to fine-tune models with large data sets. This method leverages PEFT and Bitsandbytes for an efficient fine-tuning process.
Inference
Open-ChatGPT provides an easy-to-use inference script that loads the foundation model and LoRA weights to run a Gradio interface for model interaction. It allows users to perform inference on specified inputs and customize the script as needed.
Running with Limited Memory
For users with restricted memory availability, Open-ChatGPT supports 8-bit compression, which reduces memory consumption significantly while maintaining reasonable model performance, making it possible to run Alpaca-7B on a single NVIDIA GPU.
Conclusion
Open-ChatGPT is a powerful, community-friendly project offering sophisticated tools for creating personalized AI models. With frequent updates, supportive community initiatives, and comprehensive documentation, it empowers users to explore the capabilities of AI in a highly accessible way.