Picking model sizes is not an exact science. We look for sizes that will fit quantized on different categories on devices (e.g., low-end and high-end smartphone, laptops and 16GB GPUs, and bigger GPUs/TPUs). We also want the ratio of model width to depth (number of layers) to be consistently around 90, which we found works best.
The models are trained with distillation from a bigger teacher. We train them independently, but for v3 we have unified the recipes for 4B-27B, to give you more predictably when scaling up and down to different model sizes.
The models are trained with distillation from a bigger teacher. We train them independently, but for v3 we have unified the recipes for 4B-27B, to give you more predictably when scaling up and down to different model sizes.