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While there has been a lot of progress in leveraging GPT models for various use-cases, the model weights themselves are closed and only accessible via API. As an internal team within a startup, we don't have the resources to train our own model. We have loads of proprietary/internal/client data that we'd like to use but are concerned about sending it to third-party APIs like OpenAI's.
Are there any comparisons available between the various models with publicly available weights vs ChatGPT? How should we go about choosing the model to use? Is it the case that beyond a certain size of the model, say 10B parameters, there is very little difference in performance between two models? Additionally, are there any examples of people building tools for internal usage within a startup/enterprise? Is it possible to run GPT models locally for inference and fine-tune them for our specific use-case? What are the steps required to achieve this, and what are some best practices to follow?
One of the many questions I have in mind is the distinction between common stock and preferred stocks. And how do the absence/presence of these specific types have an impact on the eventual payoff, if there is ever one?
Also, how can one negotiate around the eventual dilution of the equity as the startup raises more money?
Is there a guide that goes through these details? I also wonder whether this kind o equity can be used for investment in other assets such as real estate for instance.