zlacker

Base models are just trying to autocomplete the input text. The most logical completion for an instruction is something approximately like what you asked, but base models are raw. They have not been taught to follow instructions, so they generally do a poor job. They're especially bad at knowing when to stop, and they will often generate their own questions to answer, which they will then answer, followed by more questions and more answers.

When chat models are trained, they are first pre-trained (the "PT" in "GPT"), which creates a base model, then they are "fine tuned" (RLHF, aligned, whatever you want to call it).

A base model can be fine tuned with an instruction dataset (like OpenOrca[0]) to learn how to follow instructions or how to chat. It can also be fine-tuned with a collection of any inputs and the expected outputs, and learn how to do that specific task.

OpenPipe appears to specialize in fine-tuning base models for specific applications. They wanted a better base model. If you want it instruction-tuned, I'm sure they would be happy to help with that, or you can wait for someone in the community to make one of those from their base model... but I believe the whole point of the article is that a small, specialized model can outperform a large, general model. Their goal does not seem to be to build a tiny, general, chat-tuned model that outperforms GPT-4 in everything. They want you to train the base model on a very specific task, with the expectation that it will outperform GPT-4 and be tremendously cheaper to run at the same time. Many LLM tasks are centered around summarization, extraction, or classification, which have nothing to do with chatting.

[0]: https://huggingface.co/datasets/Open-Orca/OpenOrca