The Intel one had supervised fine-tuning with the SlimOrca dataset, and then DPO alignment on top of that using a preference dataset.
The technique for generating the preference data is what’s so interesting about that one. Instead of having human labelers choose a preferred response, they generated a response from a small model and a large model, and then always selected the large one’s as the preferred response.
The technique for generating the preference data is what’s so interesting about that one. Instead of having human labelers choose a preferred response, they generated a response from a small model and a large model, and then always selected the large one’s as the preferred response.