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Llama-3 chat vector

This is 'modelified' version of chat vector from the paper Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages. So this is not a model, its just weight diff, just for ease to use myself(or you too)!

What I understand here: 'Chat vector method' is a merging method that utilizes the difference between the base model, the continuously pre-trained (usually language transferred) model, and the chat model; so the recipe is

model(base) + weight_diff(continous pretrained) + weight_diff(instruct) or

model(base) + weight_diff(continous pretrained + fine-tuned) + weight_diff(instruct).

So before (my) initial purpose in comparing which method is better, llama3 → CP + chat vector → FT vs. llama3 → CP → FT + chat vector, it seems reasonable to compare it with other methods in Mergekit.

Model Method Kobest(f1) Haerae(acc)
beomi/Llama-3-Open-Ko-8B-Instruct-preview chat vector 0.4368 0.439
kuotient/Llama-3-Ko-8B-ties Ties 0.4821 0.5160
kuotient/Llama-3-Ko-8B-dare-ties Dare-ties 0.4950 0.5399
kuotient/Llama-3-Ko-8B-TA Task Arithmetic(maybe...? not sure about this) -
WIP Model stock(I don't read this paper yet but still) -
kuotient/Llama-3-Seagull-Evo-8B Evolutionary Model Merging 0.6139 0.5344
--- --- --- ---
meta-llama/Meta-Llama-3-8B Base 0.4368 0.439
meta-llama/Meta-Llama-3-8B-Instruct - 0.4239 0.4931
beomi/Llama-3-Open-Ko-8B Korean Base 0.4374 0.3813

All that aside, I'd like to thank @beomi for creating such an awesome korean-based model.