It seems like everybody is focused on "LLM"s, a.k.a Large Language Models. One interesting addition to that is fine-tuned- small parameter, distilled, context-dependent small language models that:
1- Do a particular task with great capability (due to its constrained, limited scope)
2- Do it in such a way, it integrates gracefully in your workflow without ever requiring you to know you are using an LM.
There is a difference between outsourcing your workflow to AI and actually utilizing it.
1- Do a particular task with great capability (due to its constrained, limited scope) 2- Do it in such a way, it integrates gracefully in your workflow without ever requiring you to know you are using an LM.
There is a difference between outsourcing your workflow to AI and actually utilizing it.
Check this: https://www.distillabs.ai/blog/we-benchmarked-12-small-langu...