Limitations One limitation of our model is its potential for data bias. This could limit the applications of the model. MLLMs could be used to create fake news articles or social media posts. Hyperparameters Number of layers 24 Hidden size 2,048 FFN inner hidden size 8,192 Attention heads 32 Dropout 0.1 Attention dropout 0.1 Activation function GeLU [1] V ocabulary size 64,007 Soft tokens V size 64 Max length 2,048 Relative position embedding xPos [2] Initialization Magneto [3] Table 1: Hyperparameters of causal language model of K The detailed instruction tuning hyperparameters are listed in Table 3. The models are trained on web-scale multimodal corpora.

Identity theft involving stolen driver's licenses creates lasting legal exposure unlike credit card fraud, as license numbers cannot be changed and require extensive cleanup efforts.

However, this is not always the case, as different tasks can compete with each other and lead to weaker models.

However, this is not always the case, as different tasks can compete with each other and lead to weaker models.

In this material, we will give more technical details as well as additional experiments to support the main paper. The overview of the proposed framework, DeWave, is illustrated in Figure 6. The dataset is split into training (80%), development (10%), and testing (10%) sets, comprising 10,874, 1,387, and 1,387 unique sentences, respectively, with no overlap. We release our implementation code through GitHub to contribute to this area. Section 3.3, where a 6-layer CNN encoder slides through the whole wave and gets the embedding The codex encoder shares the same structure with word-level features.