If you’re looking for AI assistants that can help you with writing, coding, or even education, you’re in luck!

This article introduces 11 alternative and mostly open-source tools, each offering a unique set of features tailored to a different use case. By diving into these resources, you’ll discover new ways to make the most of AI in your everyday life.

Some noteworthy alternatives include Bard for writing, Codex for coding, and Duolingo Max for language learning.

LLaMA

Are you looking for a ChatGPT alternative? Look no further than Meta’s LLaMA (Language Model Augmented Machine). Initially, LLaMA was designed as a text generator, but it also works well as a chatbot. It is a versatile language model pretrained on billions of texts from the internet and fine-tuned to provide accurate, intelligent responses.

To use this alternative, you need intermediate-level programming skills, and it’s important to have the right hardware setup such as a powerful GPU. You can run a version of LLaMA, the LLaMa-13b model, on your local machine. For details on how to set it up, read this guide.

Regarding performance in natural language processing tasks, LLaMA-13b is highly capable . It tries to produce content like poetry and stories, much like ChatGPT or OpenAI’s GPT-3 and GPT-4 models. How it behaves, though, depends on the specific tuning you apply during its setup.

LLaMA supports multiple languages, providing a bilingual or multilingual experience for users. However, if your focus is on a particular language, there are other ChatGPT alternatives you could consider like Jasper Chat.

Consider trying out LLaMA for your AI chatbot or text generation needs. Remember, though, it may require more coding and technical skills than some other alternatives.

Ressources: Read the paper “LLaMM: Open and Efficient Foundation Language Models” and check out the GitHub

Alpaca

Yeah, the naming has gotten pretty obscure in the open-source large language model (LLM) community.

Short Summary: Instruction-following models like GPT-3.5 and ChatGPT are powerful but have flaws. Academia faces challenges in researching these models due to limited access.

Stanford researchers fine-tuned Alpaca, a language model based on Meta’s LLaMA 7B, using 52K instruction-following demonstrations from text-davinci-003.

Alpaca is small, easy to reproduce, and shows similar behaviors to text-davinci-003. The team is releasing their training recipe and data, with plans to release model weights and an interactive demo for academic research.

Commercial use is prohibited due to licensing restrictions and safety concerns.

Image credit

Alpaca is a remarkable chatbot alternative to ChatGPT that you can explore. Developed by Stanford researchers, it was fine-tuned using Facebook’s LLaMA to deliver impressive language capabilities source.

This chatbot has the potential to enhance your SEO efforts, as it’s able to reason , answer questions, and tell jokes, among other things. By integrating Alpaca into your online projects, you can engage users and attract search engine attention on platforms like Google and Bing .

And it’s free and open-source!

Here’s an example run from the original launch website:

There are also other open-source alternatives to ChatGPT that you may find useful, such as GPT4All, Dolly 2, and Vicuna . You can find Python code to run these models on your system in this tutorial.

Vicuna

Vicuna-13B, an open-source AI chatbot, is among the top ChatGPT alternatives available today. Its impressive feature parity with OpenAI’s ChatGPT and Google’s Bard has made it a popular choice for those seeking a capable language model.

Research Paper: “GPTQ: ACCURATE POST-TRAINING QUANTIZATION FOR GENERATIVE PRE-TRAINED TRANSFORMERS”

What sets Vicuna apart is its ability to write code even though it is very concise and can run on your single-GPU machine (GitHub), which is less common in other open-source LLM chatbots . This unique feature, along with its more than 90% quality rate, makes it stand out among ChatGPT alternatives.

Reference: Original Website

Don’t worry about compatibility, as Vicuna is available for use on your local machine or with cloud services like Microsoft’s Azure, ensuring you can access and collaborate on your writing projects wherever you are.

With Vicuna, you can expect the AI chatbot to deliver text completion tasks such as poetry, stories, and other content similar to what you would find on ChatGPT or Youchat. Thanks to its user-friendly interface and robust feature set, you’ll likely find this open-source alternative quite valuable.

OpenChatKit

OpenChatKit is one of the ChatGPT alternatives worth exploring!

Official OpenChatKit Website (open-source)

Developed by Together Computer, OpenChatKit is an open-source variant of ChatGPT, providing users and developers the flexibility to tailor chatbot behavior to suit their specific needs.

You can use it online here in interactive mode — I tried it myself:

If you’re a coder or developer, you’ll appreciate OpenChatKit’s compatibility with Python, allowing for seamless integration into your projects. You can also use it with GitHub Copilot to enhance your coding experience, thanks to the open-source nature of the project.

OpenChatKit caters to a broader audience since the open-source design allows access to users and groups that might not have the means to use proprietary models. It supports functions such as reasoning and multi-tasking, giving you an extensive range of applications to use within your projects.

It can also code:

GPT4ALL

GPT4ALL is an interesting alternative to ChatGPT that you might want to explore .

It is a community-driven project aimed at offering similar capabilities to those of ChatGPT through the use of open-source resources .

The project is trained on a massive curated collection of written texts, which include assistant interactions, code, stories, descriptions, and multi-turn dialogues (source).

Recommended: GPT4All Quickstart – Offline Chatbot on Your Computer

By choosing GPT4ALL as an alternative, you can access various resources such as:

• Datasets
• Model weights
• Data curation processes

Getting Started with GPT4ALL

To get started, you’ll need to familiarize yourself with the project’s open-source code, model weights, and datasets. Information related to the project can be found through its project link provided in the search results.

Again, check out our detailed article on setting up GPT4ALL:

Recommended: GPT4All Quickstart – Offline Chatbot on Your Computer

What to Expect from GPT4ALL

While GPT4ALL may not match ChatGPT in terms of exact performance, it is a strong contender when it comes to providing a similar experience. Plus, it offloads some of the computational requirements associated with ChatGPT, possibly making it a more efficient option for you.

BlinkDL RWKV

Looking for a faster, more VRAM-efficient alternative to ChatGPT? Raven RWKV is an open-source chatbot that may just suit your needs! Developed by BlinkDL, this chatbot is powered by the RWKV language model which uses Recurrent Neural Networks (RNNs) to achieve high-quality results similar to ChatGPT, but with better processing speed and lower hardware requirements .

“RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it’s combining the best of RNN and transformer – great performance, fast inference, saves VRAM, fast training, “infinite” ctx_len, and free sentence embedding.” (source)

You’ll find that the Raven RWKV model has been fine-tuned on various datasets like Stanford Alpaca, code-alpaca, and more, offering an impressive performance with diverse conversation topics . It has even managed to match the quality and scaling capabilities of transformer models, all while requiring less VRAM .

You can check out an example run here:

If you’re eager to get started with Raven RWKV, head over to its Hugging Face repository for more details and relevant resources . Here, you’ll find different models like RWKV-4-Pile, which have been fine-tuned on diverse data sources to cater to various language preferences .

In short, the Raven RWKV chatbot is an excellent choice if you seek to explore an open-source chatbot alternative that balances performance and resource consumption .

StableLM (Stability-AI)

StableLM is an open-source alternative to ChatGPT developed by Stability AI. With its goal to provide a transparent and scalable AI language model, it’s perfect for anyone looking to explore options outside of proprietary tools.

Based on a dataset called “The Pile,” StableLM aims to offer a powerful language-processing solution. You can access StableLM in its alpha form on GitHub with 3 billion and 7 billion parameter model options. This means you have the flexibility to choose the model that fits your project best.

As you start working with StableLM, you’ll experience:

• A solution that rivals ChatGPT in terms of performance and capabilities
• Alpha access, allowing you to get your hands on the technology early
• A GitHub-based platform for easy use and integration with your projects

When trying it on the Hugging Face interface, however, I experienced that it’s still super slow. This is probably a problem of the server infrastructure though:

Remember, as with any open-source project, StableLM is ever-evolving. This means you have the opportunity to be a part of its development and contribute to the progress of the technology.

KoboldAI

KoboldAI is a fantastic alternative to ChatGPT that you might want to try out . It’s an open-source AI-powered chatbot capable of generating human-like text based on your prompts. You can find the source code and development updates on their GitHub repository.

Here’s an example run:

 Bot : Hey!
💻 You : Hey Boyname, how have you been?
🤖 Bot : Been good! How about you?
💻 You : Been great to, excited to try out KoboldAI
🤖 Bot : KoboldAI is really fun!
💻 You : For sure! What is your favorite game?

When using KoboldAI, you’ll appreciate its easy-to-use interface that keeps things simple and clean . Setting up the program may take a few steps, as you need to follow the instructions on their GitHub page. But worry not, they provide clear guidelines to make it easy for you .

In fact, it’s one of the most comprehensive README pages on an AI GitHub repository I have ever seen.

The AI behind KoboldAI is driven by various pre-trained models, which give you options to find the perfect balance between performance and quality. You can experiment with different models to see which one fits your needs best .

KoboldAI is focused on providing you with a chatbot experience that’s constantly improving. The active community of developers and users helps ensure that updates and bug fixes are addressed swiftly .

Flan-T5-XXL

Meet Flan-T5-XXL, an impressive open-source language model that serves as an alternative to ChatGPT. Developed by Google, Flan-T5-XXL is an enhanced version of the T5 language model and excels in few-shot learning tasks.

Flan-T5-XXL benefits from instruction fine-tuning, a technique that improves model performance and generalization to unseen tasks. By leveraging this technique, you can expect better outcomes from Flan-T5-XXL compared to traditional language models.

This model stands out in applications involving multi-turn dialogues. Thanks to its Baize implementation, which mitigates potential risks by leveraging ChatGPT to facilitate conversations with itself, Flan-T5-XXL offers higher-quality results.

Usage

To get started with Flan-T5-XXL, you can access its model card for details on available features and implementation. Make sure to watch this YouTube video for code examples and optimization tips:

And for further comparison with other language models, you can refer to this SourceForge comparison between ChatGPT, Flan-T5, and Visual ChatGPT.

Keep in mind that the open-source nature of Flan-T5-XXL offers a unique advantage because it is free to use—unlike its proprietary counterpart, ChatGPT. To make an informed decision, compare the functionality of the Flan-T5-XXL and ChatGPT based on your specific needs.

For instance, I found that it’s not too advanced, GPT-4 would have figured this problem out:

By exploring Flan-T5-XXL as an alternative to ChatGPT, you can gain access to a powerful, open-source language model that offers diverse functionalities and improved performance. Happy experimenting!

MiniGPT

MiniGPT is a lighter version of ChatGPT that you can use for various tasks. It’s perfect for those who need a simpler AI chatbot for straightforward tasks . In this section, you’ll discover a few alternatives to MiniGPT.

Recommended: MiniGPT-4: The Latest Breakthrough in Language Generation Technology

This neural network model has been developed to improve vision-language comprehension by incorporating a frozen visual encoder and a frozen large language model (LLM) with a single projection layer.

MiniGPT-4 has demonstrated numerous capabilities similar to GPT-4, like generating detailed image descriptions and creating websites from handwritten drafts.

One of the most impressive features of MiniGPT-4 is its computation efficiency. Despite its advanced capabilities, this model is designed to be lightweight and easy to use. This makes it an ideal choice for developers who need to generate natural language descriptions of images but don’t want to spend hours training a complex neural network.

Image source: https://github.com/Vision-CAIR/MiniGPT-4

Additionally, MiniGPT-4 has been shown to have high generation reliability, meaning that it consistently produces accurate and relevant descriptions of images.

Google Bard

Google Bard is one of the top alternatives to ChatGPT you can try.

Built as Google’s response to ChatGPT, it utilizes a combination of two Language Models for Dialogue (LLMs) to create an engaging conversational experience (source). Offered by the search engine giant, you can expect some powerful AI capabilities from Bard. With Bard, you can enjoy a more dynamic way of searching for answers to various queries (source).

Image Credits: https://blog.google/technology/ai/bard-google-ai-search-updates/

To try Google Bard, visit the following link and sign up for access: Google Bard

Here are some key features of Google Bard:

• It is an experimental AI conversational service, powered by Google’s LAMDA technology (source).
• Ideal for providing quick help with tasks like cover letter writing, event planning, and other needs you might have.(source).
• Google Bard excels in precision and ease of use, making it a reliable alternative for those looking for a powerful AI chatbot.

Some More Resources

First, there’s GitHub Copilot, an LLM-based AI tool specifically designed to help coders increase their productivity.

Another remarkable choice is Character.AI, which focuses on natural language understanding and generation. It’s excellent for creating realistic conversations with virtual characters.

You should also consider YouChat as an alternative. It’s a powerful AI chatbot that helps you automate customer support and social media interactions.

Don’t miss Socratic, as it’s designed to assist you with educational content. You can ask questions and receive detailed explanations in various subjects.

Next up is JasperChat. It’s a valuable tool for businesses looking to simplify their customer service and sales processes using AI chatbot technology.

Finally, WriteSonic is a worthy mention, especially if you need help with content writing tasks. It’s an AI-powered writing assistant that generates text for multiple purposes.

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Key Points

• The leaked document is titled “We Have No Moat, And Neither Does OpenAI.”
• It argues that open-source AI development is winning and that Google and other companies have no competitive advantage or “moat” in the field.
• The document suggests that Google and other companies should focus on building tools and infrastructure that support open-source AI development rather than trying to compete with it.
• The document provides a fascinating insight into the state of AI development and the challenges facing companies like Google as they try to stay ahead of the curve.
• Open-source development is unstoppable and has never been more alive!

Diving Into the Document

A leaked Google document titled “We Have No Moat, And Neither Does OpenAI” has recently garnered attention. Shared anonymously on a public Discord server, the document comes from a Google researcher and offers a frank analysis of the AI development landscape.

The document contends that open-source AI development is prevailing, leaving Google and other companies without a competitive edge.

Considering Google’s status as an AI leader and its substantial investments, this is a notable claim.

Quote: “But the uncomfortable truth is, we aren’t positioned to win this arms race and neither is OpenAI. While we’ve been squabbling, a third faction has been quietly eating our lunch.”

Here are some interesting developments in the open-source community:

• Offline Fast LLMs: As reported in a recent Finxter article, many large language models can now be run offline. A Twitter user even shared how he ran a foundation model on a Pixel 6 at 5 tokens per second speed!
• Scalable Personal AI: Projects like Alpaca-Lora allow you to fine-tune a personalized AI on your notebook in a couple of hours.
• Multimodality: Researchers release new multimodal models that are trained in less than one hour and are freely available via GitHub. Here‘s the paper.
• Responsible Release: You can find a list of pre-trained LLMs for textual data generation on myriads of new websites. Other websites now share generative art models, generated by Midjourney or DALL-E, without restrictions. See an example here:

source

The researcher suggests that instead of competing with open-source AI, Google and other companies should concentrate on creating tools and infrastructure to support it. This strategy would ensure rapid AI advancements and widespread benefits.

Check out this wonderful analysis from the article:

Quote: “Many of the new ideas are from ordinary people. The barrier to entry for training and experimentation has dropped from the total output of a major research organization to one person, an evening, and a beefy laptop.”

The leak has sparked significant debate within the AI community, with some criticizing Google for not adequately supporting open-source AI and others lauding the company for recognizing its own limitations.

LoRA – An Innovation Worth Keeping In Mind

Low-Rank Adaptation of Large Language Models (LoRA) is a powerful technique we should focus on more.

LoRA works by simplifying model updates, making them much smaller and faster to process. This allows us to improve a language model quickly on regular computers, which is great for adding new and diverse information in real-time. Even though this technology could help Google’s ambitious projects, it’s not used enough.

Retraining models from scratch is difficult and time-consuming.

LoRA is effective because it can be combined with other improvements, like instruction tuning. These improvements can be added on top of each other to make the model better over time without needing to start from scratch.

This means that when new data or tasks become available, the model can be updated quickly and cheaply. On the other hand, starting from scratch wastes previous improvements and becomes very expensive.

We should think carefully about whether we need a new model for every new idea. If we have major improvements that make reusing old models impossible, we should still try to keep as much of the previous model’s abilities as possible.

I couldn’t resist adding this interesting quote from the article:

Quote: “LoRA updates are very cheap to produce (~$100) for the most popular model sizes. This means that almost anyone with an idea can generate one and distribute it. Training times under a day are the norm. At that pace, it doesn’t take long before the cumulative effect of all of these fine-tunings overcomes starting off at a size disadvantage. Indeed, in terms of engineer-hours, the pace of improvement from these models vastly outstrips what we can do with our largest variants, and the best are already largely indistinguishable from ChatGPT. Focusing on maintaining some of the largest models on the planet actually puts us at a disadvantage.”

Timeline of LLM Developments (Overview)

Feb 24, 2023 – Meta launches LLaMA, an open-source code with various model sizes.

March 3, 2023 – LLaMA is leaked, allowing anyone to experiment with it.

March 12, 2023 – Artem Andreenko runs LLaMA on a Raspberry Pi.

March 13, 2023 – Stanford releases Alpaca, enabling low-cost fine-tuning of LLaMA.

March 18, 2023 – Georgi Gerganov runs LLaMA on a MacBook CPU using 4-bit quantization.

March 19, 2023 – Vicuna, a cross-university collaboration, achieves “parity” with Bard at $300 training cost.

March 25, 2023 – Nomic creates GPT4All, an ecosystem for models like Vicuna, at $100 training cost.

March 28, 2023 – Open Source GPT-3 by Cerebras outperforms existing GPT-3 clones.

March 28, 2023 – LLaMA-Adapter introduces instruction tuning and multimodality with just 1.2M learnable parameters.

April 3, 2023 – Berkeley launches Koala, users prefer it or have no preference 50% of the time compared to ChatGPT.

April 15, 2023 – Open Assistant launches a model and dataset for Alignment via RLHF, achieving near-ChatGPT human preference levels.

Recommended: 6 New AI Projects Based on LLMs and OpenAI

Competing with Open-Source is a Losing Game

I strongly believe in the power of open-source software development — we should build Bazaars not Cathedrals!

Open-source AI development is a better approach than closed-source AI development, particularly when considering the potential of Artificial General Intelligence (AGI). The open-source approach fosters collaboration, accessibility, and transparency, while promoting rapid development, preventing monopolies, and ensuring many benefits.

Here are a few reasons why I think open-source AI development should win in the long-term:

Collaboration is key in open-source AI, as researchers and developers from diverse backgrounds work together to innovate, increasing the likelihood of AGI breakthroughs.

Open-source AI is accessible to anyone, regardless of location or financial resources, which encourages a broader range of perspectives and expertise.

Transparency in open-source AI allows researchers to address biases and ethical concerns, fostering responsible AI development.

By building upon existing work, developers can rapidly advance AI technologies, bringing us closer to AGI.

Open-source AI also reduces the risk of single organizations dominating the AI landscape, ensuring that advancements serve the greater good.

Additionally, the benefits of AI are more evenly distributed across society through open-source AI, preventing the concentration of power and wealth.

Lastly, open-source AI development improves the security of AI systems, as potential flaws can be discovered and fixed by a larger community of researchers and developers.

Let’s end this article with another great quote from the article:

Quote: “Google and OpenAI have both gravitated defensively toward release patterns that allow them to retain tight control over how their models are used. But this control is a fiction. Anyone seeking to use LLMs for unsanctioned purposes can simply take their pick of the freely available models.”

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