How to do the LoRA fine-tuning incrementally about mlx-examples HOT 4 CLOSED

both of them cannot get the results as expected

Either approach is quite reasonable and should work. What happens when you try?

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both of them cannot get the results as expected

Either approach is quite reasonable and should work. What happens when you try?

I suspect it is due to the contents of training datasets, should I need to involve some datas from the previous training datasets into the new one?
I just did some evaluation for incremental LoRA fine-tuning, so I put the same contents from train.jsonl into the valid.jsonl for the new training datasets, Is it right?

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both of them cannot get the results as expected

Either approach is quite reasonable and should work. What happens when you try?

For example I use with fused only for Q "who are the members of *** team?", Answer is perfect.
After adding some new training datasets, I use the fused model with the new adapters.npz, Q "who are the members
of *** team?", the answer contains some people names from the new training sets, but these names are from totally different questions of new datasets.

If I combine the old and new datasets into only a train.jsonl and valid.jsonl, do the LoRA training from the scratch , the result is as expected.

I use Chinese characters in training datasets.

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@southkorea2013 did you ever try mixing old data with new data? I think that should probably solve your issue. I'm closing this as it's not really a bug / specific request. If you want to start a discussion about please go ahead.

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