How to save parameters just related to classifier layer of pretrained bert model due to the memory concerns?
I fine tuned the pretrained model here by freezing all layers except the classifier layers. And I saved weight file with using pytorch as .bin format.
Now instead of loading the 400mb pre-trained model, is there a way to load the parameters of the just Classifier layer I retrained it? By the way, I know that I have to load the original pretrained model, I just don't want to load the entire fine tuned model. due to memory concerns.
I can access the last layer's parameters from state_dict as below, but how can I save them in a separate file to use them later for less memory usage?
model = PosTaggingModel(num_pos_tag=num_pos_tag)
state_dict = torch.load(&quot;model.bin&quot;)
print(&quot;state dictionary:&quot;,state_dict)
with torch.no_grad():
    model.out_pos_tag.weight.copy_(state_dict['out_pos_tag.weight'])
    model.out_pos_tag.bias.copy_(state_dict['out_pos_tag.bias'])

Here is the model class:
class PosTaggingModel(nn.Module):
    def __init__(self, num_pos_tag):
        super(PosTaggingModel, self).__init__()
        self.num_pos_tag = num_pos_tag
        self.model = AutoModel.from_pretrained(&quot;dbmdz/bert-base-turkish-cased&quot;)
        for name, param in self.model.named_parameters():
            if 'classifier' not in name: # classifier layer
                param.requires_grad = False
        self.bert_drop = nn.Dropout(0.3)
        self.out_pos_tag = nn.Linear(768, self.num_pos_tag)
        
    def forward(self, ids, mask, token_type_ids, target_pos_tag):
        o1, _ = self.model(ids, attention_mask = mask, token_type_ids = token_type_ids)
        
        bo_pos_tag = self.bert_drop(o1)
        pos_tag = self.out_pos_tag(bo_pos_tag)

        loss = loss_fn(pos_tag, target_pos_tag, mask, self.num_pos_tag)
        return pos_tag, loss

I don't know if this is possible but I'm just looking for a way to save and reuse the last layer's parameters, without the need for parameters of frozen layers. I couldn't find it in the documentation.
Thanks in advance to those who will help.

You can do it like this
import torch

# creating a dummy model
class Classifier(torch.nn.Module):
  def __init__(self):
    super(Classifier, self).__init__()
    self.first = torch.nn.Linear(10, 10)
    self.second = torch.nn.Linear(10, 20)
    self.last = torch.nn.Linear(20, 1)
  
  def forward(self, x):
    pass

# Creating its object
model = Classifier()

# Extracting the layer to save
to_save = model.last

# Saving the state dict of that layer
torch.save(to_save.state_dict(), './classifier.bin')

# Recreating the object of that model
model = Classifier()

# Updating the saved layer of model
model.last.load_state_dict(torch.load('./classifier.bin'))

