Introducing GPT4All: A fine-tuned 7B parameter language model from a curated set of 400k GPT-Turbo-3.5 assistant style generations

Machine Learning


If you’ve been on the internet these days, you’ve probably heard of large language models and the applications built around them. The best-known example is OpenAI’s ChatGPT, which employs the GPT-Turbo-3.5 large-scale language model. Large Language Models, known as LLMs, are a breakthrough revolution in the world of artificial intelligence and machine learning. Because these sophisticated algorithms can perform several natural language tasks. These algorithms can successfully recognize text patterns after being trained on large datasets with millions to billions of parameters. Due to its numerous use cases, LLM is now embedded in many different areas to improve people’s lives in general.

After seeing the potential of LLM, a number of companies, from large technology companies to fledgling start-ups, have jumped into the race to develop natural language AI applications. In response to OpenAI’s ChatGPT, Google debuted BARD, a conversational AI chatbot, and Meta developed LLaMA, a 65B LLM that supposedly surpasses his GPT-3. But the story doesn’t end here! Nomic AI’s latest innovation, GPT4All, is a vast curated corpus of over 800,000 high-quality assistant interactions collected using the GPT-Turbo-3.5 model. It is a trained 7B-parameter LLM and has joined the race of companies experimenting with transformer-based GPT models. GPT4All was heavily inspired by Alpaca, a Stanford instructional model, and produced about 430,000 high-quality assistant-style interaction pairs, including story descriptions, dialogue, code, and more.

The creators of GPT4All have embarked on a rather innovative and fascinating path of building ChatGPT-like chatbots using existing LLMs such as Alpaca. Collecting large amounts of data in the form of a combination of rapid response was the first step in this journey. To this end, the team collected over 1 million of his questions and prompts from several publicly accessible sources and collected the answers using the GPT-Turbo-3.5 model. The next step was to clean up this prompt-response data to remove failed prompt instances and erratic responses, leaving us with over 800,000 high-quality prompt-response pairs. The team detailed that they put considerable time and care into the data curation and preparation steps to ensure that the data pairs were up to date and covered a wide range of topics.

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In the next phase, we trained multiple models and selected the best performing model. The researchers applied numerous instances of her LLaMA language model in Meta to training. The model linked to the latest public release of GPT4All is Stanford University’s Alpaca, which is based on his LLaMA model in Meta. It was trained using the Low-Rank Adaptation (LoRA) method, yielding 430k post-processed instances. The researchers also conducted an initial assessment of their strategy by comparing their model’s perplexity to the best published alpaca and laura models. We will provide additional information soon.

Currently, the GPT4All model is licensed for research purposes only and is based on Meta’s LLaMA, which has a non-commercial license, so commercial use is prohibited. One of the main attractions of the GPT4All model is that a quantized 4-bit version is also available, so anyone can easily run the model on a CPU. Simply put, users with limited computational resources can train models with reduced accuracy instead of using consumer-grade hardware. The steps to run GPT4All are straightforward and well documented in our GitHub repository. Nomic AI has also open-sourced all information about GPT4All, including datasets, code, and model weights, for the community to build on their work.

Such initiatives and contributions to the natural language model race are essential to accelerating the current pace of artificial intelligence and machine learning. The GPT4All model is a really good step in this direction. This model achieves exemplary results while using less computational resources, which is quite surprising.

check out technical paper and github. All credit for this research goes to the researchers of this project.Also, don’t forget to participate Our 17k+ ML SubReddit, cacophony channeland email newsletterWe share the latest AI research news, cool AI projects, and more.

Khushboo Gupta is a consulting intern at MarktechPost. She is currently pursuing her B.Tech at the Indian Institute of Technology (IIT), Goa. She has her passions in the fields of machine learning, natural language processing, and her web development. She enjoys learning more about the technical field by participating in some challenges.

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