If you have been on the internet recently, it is very likely that you might have heard about large language models or the applications built around them. The most well-known example is OpenAI’s ChatGPT, which employs the GPT-Turbo-3.5 large language model. Large language models, or LLMs as they are known, are a groundbreaking revolution in the world of artificial intelligence and machine learning, as these sophisticated algorithms are capable of carrying out several natural language tasks. These algorithms can successfully recognize text patterns after being trained on massive datasets with millions to billions of parameters. Owing to its numerous use cases, LLMs are currently being incorporated into a variety of fields to enhance people’s life in general.
Numerous businesses—from large tech companies to fledgling startups—have jumped into the race to develop natural language AI applications after seeing the promise of LLMs. In response to OpenAI’s ChatGPT, Google debuted BARD, its conversational AI chatbot, while Meta developed LLaMA, a 65B LLM that can allegedly exceed GPT-3. Yet the story doesn’t finish here! The latest innovation from Nomic AI, GPT4All, a 7B parameter LLM trained on a vast curated corpus of over 800k high-quality assistant interactions collected using the GPT-Turbo-3.5 model, joins the race of companies experimenting with transformer-based GPT models. GPT4All is greatly inspired by Stanford’s instruction-following model, Alpaca, and has resulted in approximately 430k high-quality assistant-style interaction pairs, which include story descriptions, dialogue, code, etc.
The creators of GPT4All embarked on a rather innovative and fascinating road to build a chatbot similar to ChatGPT by utilizing already-existing LLMs like Alpaca. Curating a significantly large amount of data in the form of prompt-response pairings was the first step in this journey. For this purpose, the team gathered over a million questions and prompts from several publicly accessible sources and collected their responses using the GPT-Turbo-3.5 model. The next step was to clean this prompt-response data to remove any failed prompt instances and irregular responses, leaving them with over 800k high-quality prompt-response pairs. The team elaborated that they spent considerable time and attention to detail in the data curation and preparation step to ensure that their data pairs were up-to-the-mark and covered a wide range of topics.
The following phase involved training multiple models and selecting the one that performed the best. The researchers applied numerous instances of Meta’s LLaMA language model for training. The model linked to the most recent public release of GPT4All is Stanford’s Alpaca, which is based on Meta’s LLaMA model. It was trained using a Low-Rank Adaptation (LoRA) method, yielding 430k post-processed instances. The researchers also conducted an initial assessment of their strategy by comparing the perplexity of their model with the best alpaca-Lora model that was publicly accessible. The evaluation procedure is ongoing, and the organization plans to provide additional information soon.
Currently, the GPT4All model is licensed only for research purposes, and its commercial use is prohibited since it is based on Meta’s LLaMA, which has a non-commercial license. One of the major attractions of the GPT4All model is that it also comes in a quantized 4-bit version, allowing anyone to run the model simply on a CPU. Simply said, users with limited computational resources can settle for less precision to train their model in exchange for using consumer-grade hardware. The instructions to run GPT4All are straightforward and have been documented well on their GitHub repository. Nomic AI has also open-sourced all information regarding GPT4All, including dataset, code, and model weights, for the community to build upon their work.
Such initiatives and contributions to the race for natural language models are essential to accelerating the current pace of artificial intelligence and machine learning. In this direction, the GPT4All model is a truly outstanding step. The model achieves exemplary results while utilizing fewer computational resources, making it quite extraordinary.
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Khushboo Gupta is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Goa. She is passionate about the fields of Machine Learning, Natural Language Processing and Web Development. She enjoys learning more about the technical field by participating in several challenges.