Top 5 best Opensource alternatives to GPT4 and ChatGPT

Language models like GPT-4 have revolutionized the field of natural language processing, enabling the development of advanced chatbots, language translation tools, and other applications. However, the proprietary nature of GPT-4 and other language models can be a barrier to innovation, as developers and researchers must pay hefty fees for access to these tools. Fortunately, a growing community of developers and researchers are creating open-source alternatives to GPT-4. In this blog post, we will explore some of the most promising open-source language models that could one day rival the capabilities of GPT-4 and beyond. We will also discuss the benefits of open-source language models and why they are important in natural language processing.

LLama

Meta, the parent company of Facebook, has publicly released a new large language model called LLaMA (Large Language Model Meta AI). LLaMA is a foundational model researchers can use to advance their work in artificial intelligence (AI). The model will help democratize access to large language models. It is smaller and more performant, requiring less computing power and resources to test new approaches, validate others’ work, and explore new use cases.

Large language models use billions of parameters and can perform various tasks, such as generating creative text, solving mathematical theorems, and answering reading comprehension questions. However, the resources required to train and run such large models have limited researchers’ understanding of how and why these models work. Therefore, smaller models like LLaMA are easier to retrain and fine-tune for specific use cases.

Meta trained LLaMA in 20 different languages, focusing on those with Latin and Cyrillic alphabets. Llama is available in several sizes, ranging from 7 billion to 65 billion parameters, and comes with a model card that explains how it was built in keeping with responsible AI practices.

Despite their potential benefits, large language models face risks, such as bias, toxicity, and the potential for generating misinformation. To prevent misuse, LLaMA is released under a non-commercial license focused on research use cases. Access to the model is granted on a case-by-case basis to academic researchers, industry research laboratories, and government and civil society organisations.

Meta believes that the entire AI community should work together to develop clear guidelines around responsible AI, particularly for large language models. By releasing LLaMA, Meta hopes that researchers can test new approaches to limit or eliminate the problems associated with large language models, further advancing the field of AI.

The model has leaked and is now available through multiple sources, including HuggingFace, and can be easily used today as an open-source alternative to chatGPT and GPT4 API.

Open-assistant

As the name suggests, Open-assistant is an open-source alternative to chatGPT.

Its focus on collaboration and community sets Open Assistant apart from other assistants. Developers are encouraged to join the Open Assistant Discord server and contribute in any way possible, from curating datasets to developing the website. Everyone pulls in one direction to create the best product possible.

But Open Assistant isn’t just about collaboration. The team behind it has a set of principles that guide their work. They aim to quickly create a minimal viable product (MVP) to maintain momentum while being pragmatic. They want their models to be efficient and run on consumer hardware so anyone can use Open Assistant without expensive equipment.

Validation is also key, so the team tests their ML experiments on a small scale before scaling up. They believe in constant improvement and learning and are always open to feedback from users and developers.

Open Assistant is more than just an assistant; it’s a community of people working together to make a difference. The team behind Open Assistant creates a smarter, more efficient future by putting humans at the centre and focusing on collaboration.

If you want to join the Open Assistant community, head to their website or Discord server and see how you can contribute. Together, we can create a better future for all of us.

GPT4All

GPT4All is a free-to-use, open-source alternative to chatGPT, the locally running, privacy-aware chatbot that offers a range of capabilities. It can understand documents and provide summaries and answers about their contents, write emails, documents, creative stories, poems, songs, and plays, write Python code, guide easy coding tasks, and answer questions about various topics. The chatbot is designed to run locally, so no GPU or internet connection is required, and the real-time inference latency on an M1 Mac is impressive.

To use GPT4All, users can download the desktop chat client for Windows, MacOS, or Ubuntu. After installation, the application can be found in the specified directory, and a desktop icon for GPT4All will be created. It’s important to note that the Windows installer might show a security complaint, which is being addressed as the team is actively setting up cert sign for Windows.

GPT4All’s capabilities have been tested and benchmarked against other models. The performance benchmarks show that GPT4All has strong capabilities, particularly the GPT4All 13B snoozy model, which achieved impressive results across various tasks. Overall, GPT4All is a great tool for anyone looking for a reliable, locally running chatbot with various capabilities.

Alpaca

Alpaca is an instruction-following language model open-source alternative to chatGPT developed by a team of researchers from Stanford University. The model is fine-tuned from Meta’s LLaMA 7B model on 52K instruction-following demonstrations generated from OpenAI’s text-davinci-003. The researchers used supervised learning to fine-tune the model, using techniques like Fully Sharded Data-Parallel and mixed precision training to optimize the training process.

The Alpaca model is designed to follow instructions given in natural language, similar to other language models like GPT-3.5 and ChatGPT. However, Alpaca has some unique features that distinguish it from other instruction-following models. For one, it is surprisingly small and easy to reproduce, costing less than $600 to train. This is in contrast to closed-source models like OpenAI’s text-davinci-003, which are much larger and more expensive to reproduce.

Another important feature of the Alpaca model is that it is designed to address some of the deficiencies of existing instruction-following models. For example, existing models can generate false information, propagate social stereotypes, and produce toxic language. The Alpaca model is designed to mitigate these issues by using high-quality instruction-following data and employing safety measures to prevent generating harmful content.

Due to licensing restrictions and safety concerns, the Alpaca model is only intended for academic research and cannot be used commercially. However, its release represents an important step in developing instruction-following models and their potential applications in education, healthcare, and customer service.

Vicuna

Vicuna is an AI language model developed by EleutherAI, an open-source organization that creates high-quality, accessible AI tools. Like ChatGPT, Vicuna is trained on a large text dataset and can generate human-like responses to prompts.

One of the key benefits of Vicuna is its accessibility. Unlike proprietary models like GPT-4, which are only available through expensive licensing agreements with companies like OpenAI, Vicuna is open-source and available for anyone to use or modify. This makes it an attractive option for developers and researchers who want to experiment with AI-generated language but don’t have the resources to access proprietary models.

In terms of performance, Vicuna is still relatively new and not as widely tested as GPT-4. However, early experiments have shown promising results, and some developers have reported that Vicuna produces more coherent responses than GPT-4 in certain contexts.