What is the Intermediate (dense) layer in between attention-output and encoder-output dense layers within transformer block in PyTorch implementation?
Why in Multi-Head Attention implementation should we use $3$ linear layers for Q, K, V instead of $3 * h$ layers?
Should the policy parameters be updated at each time step or at the end of the episode in REINFORCE?
How to generate new data given a trained VAE - sample from the learned latent space or from multivariate Gaussian?
What is actually being saved in the file when you save a model? For example a Tensorflow SavedModel file
While fine-tuning a decoder only LLM like LLaMA on chat dataset, what kind of padding should one use?
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