>> Small thing to note here, for this q6_K_q8_K, it is very difficult to get the correct result. To make it works, I asked deepseek to invent a new approach without giving it prior examples. That's why the structure of this function is different from the rest.
> This certainly wouldn't fly in my org (even with test coverage/passes).
To be fair, this seems expected. A distilled model might struggle more with aggressive quantization (like q6) since you're stacking two forms of quality loss: the distillation loss and the quantization loss. I think the answer would be to just use the higher cost full precision model.
> This certainly wouldn't fly in my org (even with test coverage/passes).
To be fair, this seems expected. A distilled model might struggle more with aggressive quantization (like q6) since you're stacking two forms of quality loss: the distillation loss and the quantization loss. I think the answer would be to just use the higher cost full precision model.