That’s called “quantization”. I’d do some searching on that for better description, but in summary, the bigger the model, the more resources they need to run and the slower it will be. Models are 8bit, but it turns out, you still get really good results if you drop off some of those bits. The more you drop the worse it gets.
People have generally found, that it’s better to have a larger data set model, with a lower quantization, than lower data set and the full 8bits
E.g 13b Q4 > 7b Q8
Going below Q4 is generally found to degrade the quality too much. So its’ better to run a 7b Q8 then a 13b Q3, but you can play with that yourself to find what you prefer. I stick to Q4/Q5
So you can just look at those file sizes to get a sense of which one has the most data in it. The M (medium) and S (small) are some sort of variation on the same quantization, but I don’t know what they’re doing there, other than bigger is better.
That’s called “quantization”. I’d do some searching on that for better description, but in summary, the bigger the model, the more resources they need to run and the slower it will be. Models are 8bit, but it turns out, you still get really good results if you drop off some of those bits. The more you drop the worse it gets.
People have generally found, that it’s better to have a larger data set model, with a lower quantization, than lower data set and the full 8bits
E.g 13b Q4 > 7b Q8
Going below Q4 is generally found to degrade the quality too much. So its’ better to run a 7b Q8 then a 13b Q3, but you can play with that yourself to find what you prefer. I stick to Q4/Q5
So you can just look at those file sizes to get a sense of which one has the most data in it. The M (medium) and S (small) are some sort of variation on the same quantization, but I don’t know what they’re doing there, other than bigger is better.
Thank you!!