DaMo
The official implement of paper 《DaMo: Data Mixing Optimizer in Fine-tuning Multimodal LLMs for Mobile Phone Agents》
DaMo is a novel solution for predicting optimal data mixtures in multitask supervised fine-tuning of multimodal large language models (MLLMs). Left: Given $m$ training sets with a batch size of $b$, all possible mixture combinations constitute the data mixing space. We sample a small number of data mixture from this space, train them on a small MLLM, and then evaluate downstream task performance. Using the data mixture as inputs and the metrics as outputs, we fit a MLP to establish the DaMo. By extrapolating from the data mixing space, we predict the optimal data mixture to train the MLLM.
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