philomath868

I hear you loud and clear... Thanks!

What about deleting vision layers (e.g. the "multi_modal_projector" and the "vision_tower.vision_model" layers, assuming I go with Gemma 3), since I need just language generation? Would that also be considered a "kick in the balls", or a useful trimming?

Perhaps. But I don't think there is an existing (open weights) model that really knows YIVO Yiddish, either, so what should I base this fine-tuning on?

Thank you!

The language is Hasidic Yiddish (which is by now different enough from YIVO Yiddish to almost be considered a different language). The amount of (all kinds of) Yiddish included in pre training is probably very little, but not nothing. Also, it's a Germanic language with Hebrew script and roots, and some Slavic roots and suffixes. Most concepts and structure are probably not *very* foreign to a good model.

As I wrote in another comment, I have thought about initializing the new embeddings based on equivalent tokens in the old ones (e.g. by translating a token to English and finding the closest old token), but I'm starting to rethink the feasibility.

I will probably get more text sometime in the future, but I have to build the first version now.

Thank you! I have thought about initializing the new embeddings based on equivalent tokens in the old ones (e.g. by translating a token to English and finding the closest old token), but this is all getting convoluted.

New tokenizer and embeddings will probably be required anyway, since the language is practically missing from any model worth to play with, but at that point simply creating a small specialized model from scratch is perhaps a better bet than trying to glue it upon a big ready model?

How does the "continuous training pipeline" work? You rebuild the model after every N corrections, with the corrections included in the data?