GPT-4 open-source substitutes that can provide comparable results and use less processing power. The most cutting-edge generative AI created by OpenAI is GPT-4. It’s transforming the way we perform our work. To replicate the results, we need access to the code, model architecture, data, and model weights, however, GPT-4 is not open-source.
We are unable to develop our own GPT-4 chatbot. We will present a few GPT-4 substitutes in the article along with a synopsis and links to the pertinent model card, blog post, chatbot demonstration, code source, and research paper.
1. ColossalChat
An open-source project called ColossalChat enables you to use a full RLHF (Reinforcement Learning from Human Feedback) pipeline to clone AI models. ColossalChat’s commitment to conversation safety is one of its primary characteristics. To lessen the spread of objectionable content, a basic safety filter is included. All of the elements will enable you to quickly and more affordably construct a personalized chatbot.
ColossalChat is an AI chatbot that may be used for a variety of purposes because it was developed on a strong and adaptable foundation that incorporates both community-focused improvements and technology developments. Modern AI technology is used by ColossalChat to provide excellent dialogues, guaranteeing users have smooth and complex interactions.
2. OPT
In May 2022, Meta released the Open Pretrained Transformer language model (OPT). It was trained on several public datasets, such as BookCorpus and The Pile, and has 175B parameters (the same as GPT-3). We intend to fully and responsibly share with interested academics the Open Pre-trained Transformers (OPT) suite of decoder-only pre-trained transformers with parameters ranging from 125M to 175 B.
We demonstrate that, although having a carbon footprint that is only one-seventh that of GPT-3, OPT-175B is equivalent. We are also making available code for playing with all of the released models, as well as our logbook that describes the infrastructure issues we encountered. Regretfully, OPT is presently only accessible under a non-commercial license for research purposes.
3. Dolly
Dolly is a sizable language model that was trained by a Databricks machine to show that we can give it the ability to follow instructions using ChatGPT’s magic while still using the previous open-source language model. Unlike all the models trained on a Stanford Alpaca dataset, Dolly 2.0 is not constrained by the OpenAI API license because it was refined on a dataset of human-generated instructions (databricks-dolly-15k).
This is because the license prohibits anyone from utilizing AI-generated data to compete with OpenAI. Training a model with high-quality training data takes 30 minutes on a single machine. Large models aren’t even necessary to attain excellent quality. Sadly, Dolly 2.0 has a number of significant flaws. It exclusively produces writing in English, experiences hallucinations, and occasionally responds in a poisonous or insulting manner.
4. RWKV Raven
Raven RWKV is a component of ChatRWKV, an open-source model similar to ChatGPT that is not transformer-based but rather uses the RWKV (100% RNN) language model. Only the context length seen during training limits the extremely long context lengths that RNNs inherently support; with careful implementation, this can be increased to millions of tokens.
Raven was refined using code-alpaca, Stanford Alpaca, and more datasets to ensure that it followed instructions. RWKV is an RNN that can be directly trained like a GPT transformer (parallelizable) and has Transformer-level LLM performance. It’s also completely attention-free. To calculate the state at location t+1, all you need is the concealed state at position t.
5. The Koala
The Koala is a chatbot that was trained by optimizing LLaMa using a web-scraped dialogue dataset. In many ways, Koala is comparable to ChatGPT and has outperformed Alpaca. Strong SEO analysis tools are available on Koala.sh to assist users in improving their content for better search engine rankings.
This involves thorough keyword research and optimization techniques that guarantee the target audience sees your material. One hundred people reviewed Koala, which offers training code, public weights, and dialogue fine-tuning. With its quick WordPress publishing tool, Koala.sh makes content management easier for bloggers and webmasters. Users can create, optimize, and publish pieces straight from the platform to their WordPress websites.
6. The Vicuna
Vicuna is capable of producing imaginative and cogent language for chatbots. An architecture based on transformers was refined using a conversational dataset gathered from ShareGPT.com. The open-source chatbot Vicuna was marketed as “impressing GPT-4 with 90% ChatGPT quality” and did well in the tests, although using less parameters than ChatGPT (13B against 175B). Users may train, serve, and assess their chatbots using the open platform FastChat. For creating a unique chatbot model, FastChat offers all the parts and resources required.
7. LoRA Alpaca
Low-rank adaptation (LoRA) and the Stanford Alpaca were used to build the Alpaca-LoRA model. Because of the low-rank adoption, we can run an Instruct model on a Raspberry Pi 4 with 4GB of RAM that is comparable in quality to GPT-3.5. Stanford Alpaca asserts that anyone can replicate it for less than $600 and that it can rival ChatGPT. Alpaca was developed using LLaMa and refined using OpenAI’s InstructGPT; regrettably, it “is intended only for academic research, and any commercial use is prohibited.”
The Alpaca-LoRA model is a reduced resource-intensive variant of the Stanford Alpaca model that uses LoRA to reduce memory usage and speed up training. The Alpaca-LoRA model requires a GPU to operate locally. A low-end GPU like the NVIDIA T4 or a consumer GPU like the 4090 may be used. The Alpaca-LoRA model is a reduced resource-intensive variant of the Stanford Alpaca model that uses LoRA to reduce memory usage and speed up training.
8. Baize
Baize’s guardrails, which help reduce potential hazards, allow it to function admirably in multi-turn discussions. It has accomplished this by using ChatGPT to create a high-quality multi-turn chat corpus that allows it to have talks with itself. It takes advantage of 100,000 dialogues created by allowing ChatGPT to converse with itself.
Alpaca’s data is also used by us to enhance its functionality. The 7B, 13B, and 30B models are now available. For further information, please consult the paper. The dataset, model, and code source for Baize are made available under a non-commercial (research purposes) license.
9. OpenChatKit
A complete toolkit called OpenChatKit provides an open-source substitute for ChatGPT when creating chatbot applications. LAION and Ontocord are working together on this project, which prioritizes community involvement and ongoing development. The foundation of OpenChatKit is a 20 billion parameter model that has been optimized for conversation and can handle a wide range of natural language tasks.
The toolkit contains detailed instructions for creating your own instruction-tuned big language model, optimizing the model, and updating the bot’s responses using an extensible retrieval system.
10. OpenAssistant
Released just a month ago, OpenAssistant is an intriguing project run by the Large-scale Artificial Intelligence Open Network (LAION) with over 13,000 volunteers worldwide. The goal of the open-source project Open Helper is to develop a conversational AI helper that is accessible to everyone.
Its chatbot features, driven by Generative Pre-trained Transformer Language Models (GPT-LLMs), have the potential to completely transform how humans interact with AI. Large language models are subjected to Reinforcement Learning from Human Feedback (RLHF) in Open Assistant. Based on human feedback, this method continuously enhances the assistant’s conversational skills.
In Summary:
Although GPT-4’s capabilities are still unrivaled, a number of open-source competitors provide remarkable outcomes with less processing overhead and easier accessibility. ColossalChat, Vicuna, RWKV Raven, and OpenAssistant are just a few examples of projects that show that community-driven development may produce conversational AI of superior quality.
To strike a compromise between efficiency and performance, these models frequently include cutting-edge training methods like RLHF, LoRA, or RNN architectures. They offer a great starting point for experimentation, customisation, and scholarly research, enabling developers and researchers to innovate in the field of generative AI, even though the majority are restricted to non-commercial usage and might not yet fully realize GPT-4’s potential.
