Databricks Presents Dolly, a New ‘Budget’ Open-Source Player Competing with ChatGPT
In Brief
Dolly is essentially a clone of Alpaca, developed by Databricks aimed at making large language models more accessible to everyone.
Training can be achieved with minimal resources, requiring just $30 and a mere three hours of effort.
Recently, a product similar to ChatGPT, known as Alpaca, was under discussion due to its remarkably low training costs, made possible by leveraging synthetic data generated through GPT. Now, let me introduce you to its counterpart, Dolly, named after the famous cloned sheep, whose goal is to open up the world of large language models to a wider audience. Dolly Dolly can be trained efficiently with little data, costing only $30 and needing only about three hours of setup. This means there's no need for an expensive supercomputer that could potentially cost tens of thousands of dollars. say Databricks has launched Dolly, presenting another 'budget' option in the open-source landscape competing with ChatGPT.
With just 6 billion parameters, Dolly has been enhanced compared to previous models, which boasted 135 billion parameters in GPT. The model was updated with insights gained from Alpaca, and now it has the ability to respond to user prompts effectively, a feature absent from earlier versions. It can engage in dialogue, produce written content, and brainstorm around various themes.
Dolly was created based on the 2020 Eleuther Even with its limited 6 billion parameters, Dolly demonstrates remarkable proficiency in language-related tasks and stands as a promising, budget-friendly alternative to large models like GPT. With ongoing tweaks and enhancements, Dolly has the potential to become a key asset in natural language processing ventures.
From this, Databricks infers that the charm of ChatGPT lies more in the quality of training data used rather than the sheer technological prowess behind the model. The developers argue that Dolly quickly acquired comparable skills, albeit not at the same caliber, underscoring the critical role of high-quality training data in shaping effective chatbots. This also implies that advancements in technology may not be the sole factor influencing chatbot effectiveness.
to enable integration with external applications, which allows access to real-time information and execution of tasks such as flight bookings or food deliveries. Developers can utilize accessible documentation to craft their plugins, while ChatGPT functions as an intelligent API caller by interpreting API specifications and natural language descriptions.
- OpenAI’s ChatGPT language model is adding new plugins to expand its capabilities. These plugins connect ChatGPT Microsoft and OpenAI are strategizing to compete with Google through a revamped Bing powered by ChatGPT.
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