Why TFBlenderbot SmallModel and TFBlenderbot SmallForConditionalGeneration are the same trainable_variables?
i downloaded model fromfacebook / blenderbot_small-90M, and loaded it withBlenderbotSmallTokenizer.from_pretrained()andBlenderbotSmallForConditionalGeneration.from_pretrained()respectively. When i looked into trainable_variables using:for v in model.trainable_variables():
	print(v)i found they were equal, butdocsays there is a a language modeling head inTFBlenderbotSmallForConditionalGeneration, how can i get the weights of the head?
2021-10-08T12:55:11Z
It is possible that the model uses tied embeddings, meaning the same embedding layer is used at the input and output of the model.A lot of causal Transformer decoders use tied embeddings.
2021-10-09T03:46:30Z
Thank you for your reply that is helpful, but what make me confused is: if last head dense inTFBlenderbotSmallForConditionalGenerationuses the kernel which weights shared with embeddings, how about the bias? And another problem is how can i get all the variables of the model include trainable and  untrainable variables? Thank you again.
2021-10-11T08:47:56Z
if last head dense inTFBlenderbotSmallForConditionalGenerationuses the kernel which weights shared with embeddings, how about the bias?Checking the PyTorch implementation, it seems that the language modeling head doesn’t use a bias, as seenhere.how can i get all the variables of the model include trainable and untrainable variables?In PyTorch, you can get all parameters of a model as follows:for name, param in model.named_parameters():
     print(name, param.shape)cc’ing@Rocketknight1for how to do this in Tensorflow.
2021-10-19T08:32:07Z
thank you a lot, that’s great helpful！