Exploring 12 Open-Source Alternatives to GPT-4

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The rapid evolution of artificial intelligence (AI) has brought forth a remarkable advancement in the field of generative AI. GPT-4, the latest offering from OpenAI, stands as a pinnacle in the realm of AI-driven language models. However, the proprietary nature of GPT-4, with its closed-source architecture and restricted access to code, data, and weights, has sparked a surge in the development of open-source alternatives. These alternatives aim to replicate GPT-4’s capabilities while remaining accessible to developers, researchers, and enthusiasts.

In this article, we will explore 12 open-source alternatives to GPT-4 that are reshaping the landscape of AI-powered language models.

Table of Contents

1. ColossalChat
2. Alpaca-LoRA
3. Vicuna
4. GPT4ALL
5. Raven RWKV
6. OpenChatKit
7. OPT
8. Flan-T5-XXL
9. Baize
10. Koala
11. Dolly
12. Open Assistant
13. Conclusion

1. ColossalChat

ColossalChat is an open-source project that facilitates the cloning of AI models using a complete RLHF (Reinforcement Learning from Human Feedback) pipeline. It offers developers a comprehensive suite of resources, including a bilingual dataset, training code, demo, and 4-bit quantized inference. These components enable the creation of customized chatbots at a lower cost and faster speed.

2. Alpaca-LoRA

Alpaca-LoRA is a powerful language model created using the Stanford Alpaca framework and low-rank adaptation (LoRA) technology. This combination allows Alpaca-LoRA to run an Instruct model with similar quality to GPT-3.5 on a 4GB RAM Raspberry Pi 4. The project provides source code, fine-tuning examples, inference code, model weights, datasets, and a demo. Remarkably, it can be trained within a few hours on a single RTX 4090.

3. Vicuna

Vicuna is a transformer-based language model that generates coherent and creative text for chatbots. Fine-tuned on a conversational dataset from ShareGPT.com, Vicuna boasts nearly 90% of ChatGPT’s performance. It is part of FastChat, an open platform that allows users to train, serve, and evaluate their chatbot models. FastChat provides all the necessary tools and components for building a custom chatbot model.

4. GPT4ALL

Developed by the Nomic AI Team, GPT4ALL is a chatbot that leverages massive curated data from assisted interactions, such as word problems, code, stories, descriptions, and multi-turn dialogues. The model architecture is based on LLaMa, and it utilizes low-latency machine-learning accelerators for faster inference on the CPU. GPT4ALL offers a Python client, GPU and CPU interference, TypeScript bindings, a chat interface, and a Langchain backend.

5. Raven RWKV

Raven RWKV is a component of ChatRWKV, an open-source model similar to ChatGPT but powered by an RWKV (100% RNN) language model instead of transformer-based architecture. Utilizing RNNs, Raven RWKV achieves comparable levels of quality and scalability to transformers while providing faster processing speeds and VRAM conservation. The model has been fine-tuned on various datasets, including Stanford Alpaca and code-alpaca, to follow instructions effectively.

6. OpenChatKit

OpenChatKit is a comprehensive toolkit that serves as an open-source alternative to ChatGPT for chatbot development. It includes step-by-step instructions for training your own instruction-tuned large language model, fine-tuning the model, and an extensible retrieval system for updating the bot’s responses. Additionally, OpenChatKit features built-in moderation capabilities to help filter out inappropriate questions.

7. OPT

The Open Pre-trained Transformer (OPT) Language Models have demonstrated exceptional abilities in zero-shot and few-shot learning, as well as Stereotypical Bias analysis, despite not matching ChatGPT’s quality. OPT is a family of large language models ranging from 125M to 175B parameters. These models are decoder-only transformers, which means they generate text autoregressively from left to right.

8. Flan-T5-XXL

Flan-T5-XXL consists of fine-tuned T5 models trained on a vast collection of datasets presented in the form of instructions. This type of fine-tuning has significantly improved performance across various model classes, such as PaLM, T5, and U-PaLM. Additionally, the Flan-T5-XXL model has been fine-tuned on over 1000 tasks spanning multiple languages.

9. Baize

Baize is an impressive language model for multi-turn dialogues, thanks to its guardrails that help mitigate potential risks. It achieves this through a high-quality multi-turn chat corpus, which was developed by leveraging ChatGPT to facilitate conversations with itself. Baize’s code source, model, and dataset are released under a non-commercial (research purposes) license.

10. Koala

Koala is a chatbot trained by fine-tuning LLaMa on a dialogue dataset scraped from the web. It has outperformed Alpaca and is comparable to ChatGPT in many cases. Koala provides training code, public weights, a dialogue fine tuner, and has been evaluated by 100 humans.

11. Dolly

Dolly is a large language model trained by the Databricks machine to demonstrate that even older open-source language models can possess the instruction-following abilities of ChatGPT. Model training requires just 30 minutes on one machine, using high-quality training data. Dolly utilizes a 6 billion parameter model, compared to GPT-3’s 175 billion parameters. Check out Dolly 2.0, an instruction-following language model that can be used commercially.

12. Open Assistant

Open Assistant is a truly open-source project that aims to democratize access to top chat-based large language models. By enabling people to interact with third-party systems, retrieve information dynamically, and create new applications using language, Open Assistant aims to revolutionize innovation in language. Users can run the large language chatbot on a single high-end consumer GPU, and its code, models, and data are licensed under open-source licenses.

Conclusion

These GPT-4 alternatives are empowering researchers, developers, and small companies to create their language-based technology and compete with industry giants. While these models may not outperform GPT-4, they hold significant potential for growth and improvement through community contributions. By embracing these open-source alternatives, we can unlock a world of innovation and push the boundaries of AI-powered language models even further.

If you are new to ChatGPT, try taking DataCamp Introduction to ChatGPT course, and if you are aware of generative AI, you can get better at prompting by reviewing the comprehensive ChatGPT Cheat Sheet for Data Science, or by checking out the resources below.

• [Cheat Sheet] ChatGPT Cheat Sheet for Data Scientists
• Start learning AI with DataCamp