While we focus on a simple yet effect setup, namely adapting only theqandvprojection in a Transformer, in our examples, LoRA can be apply to any subsets of pre-trained weights. We encourage you to explore different configurations, such as adapting the embedding layer by replacingnn.Embeddingwi...
While we focus on a simple yet effect setup, namely adapting only theqandvprojection in a Transformer, in our examples, LoRA can be apply to any subsets of pre-trained weights. We encourage you to explore different configurations, such as adapting the embedding layer by replacingnn.Embeddingwi...
While we focus on a simple yet effect setup, namely adapting only theqandvprojection in a Transformer, in our examples, LoRA can be apply to any subsets of pre-trained weights. We encourage you to explore different configurations, such as adapting the embedding layer by replacingnn.Embeddingwi...
The PDR for different configurations of satellites. There are three layers that satellites can be on. The x axis shows the configuration of the two transmitter satellites. 1 corresponds to the lowest layer, and 3 corresponds to the highest layer. The PDR is much greater when both satellites ...
While we focus on a simple yet effect setup, namely adapting only the q and v projection in a Transformer, in our examples, LoRA can be apply to any subsets of pre-trained weights. We encourage you to explore different configurations, such as adapting the embedding layer by replacing nn.Em...
time accumulates, a concentric ring is finally obtained for different devices, as shown in Fig.6, which leads to a confused result of recognition. By performing differential processing on the received baseband signal, the rotation of the received symbol due to the frequency offset can be ...
we find that the most critical LoRA hyper- parameter is how many LoRA adapters are used in total and that LoRA on all linear transformer block layers are required to match full finetuning perfor- mance. Other LoRA hyperparameters, such as the projection dimension r, do not affect performance...
we find that the most critical LoRA hyper- parameter is how many LoRA adapters are used in total and that LoRA on all linear transformer block layers are required to match full finetuning perfor- mance. Other LoRA hyperparameters, such as the projection dimension r, do not affect performance...
So, set alpha to 1.0 to fully add LoRA. If the LoRA seems to have too much effect (i.e., overfitted), set alpha to lower value. If the LoRA seems to have too little effect, set alpha to higher than 1.0. You can tune these values to your needs. This value can be even slightly...
Then the next row is INS+MID, MID+MID, OUTD+MID, and so on. Example imagehere Effective Block Analyzer This function check which layers are working well. The effect of the block is visualized and quantified by setting the intensity of the other bocks to 1, decreasing the intensity of th...