Open-source based models tailor to a company’s needs commercially, in contrast to closed LLMs. Analysts claim that this is due to the public nature of the training data.

Closed models, like ChatGPT, must first use data that belongs to OpenAI, the company that created them, for profit. They can then only via a paid access API.

The phrase ‘open LLMs’ may have various meanings. The deployment flexibility of these models and the availability of the source code are the most obvious and important features. Access to training data sets and model weights, as well as the transparent, cooperative nature of decision-making, are additional examples of openness. 

Dolly 2.0 is an LLM in which the dataset, the model, the training code, and the model weights were all trained. Databricks offers all of these as open source. Businesses can use it for commercial purposes to design a unique LLM. Noting how this strategy differs from other LLMs that haven’t made their individual model-building components open-sourced.

Analysts noted that the number of parameters used to train the models differs between “closed” and “open” LLMs. With closed LLMs, you typically use a higher number of parameters.

In contrast to Dolly 2.0’s 12 billion parameters, ChatGPT4 trains on 100 trillion parameters.