Save a Bert model with custom forward function and heads on Hugginface

I have created my own BertClassifier model, starting from a pretrained and then added my own classification heads composed by different layers. After the training I want to save the model using model.save_pretrained() but when I print it upload it from pretrained i don’t see my classifier head.
The code is the following. How can I save the all structure on my model and make it full accessible with AutoModel.from_preatrained('folder_path') ?
. Thanks!

class BertClassifier(PreTrainedModel):
    """Bert Model for Classification Tasks."""
    config_class = AutoConfig
    def __init__(self,config, freeze_bert=True): #tuning only the head
         @param    bert: a BertModel object
         @param    classifier: a torch.nn.Module classifier
         @param    freeze_bert (bool): Set `False` to fine-tune the BERT model
        #super(BertClassifier, self).__init__()

        # Instantiate BERT model
        # Specify hidden size of BERT, hidden size of our classifier, and number of labels
        self.D_in = 1024 #hidden size of Bert
        self.H = 512
        self.D_out = 2
        # Instantiate the classifier head with some one-layer feed-forward classifier
        self.classifier = nn.Sequential(
            nn.Linear(self.D_in, 512),
            nn.Linear(512, self.D_out),

    def forward(self, input_ids, attention_mask):

         # Feed input to BERT
        outputs = self.bert(input_ids=input_ids,
         # Extract the last hidden state of the token `[CLS]` for classification task
        last_hidden_state_cls = outputs[0][:, 0, :]
         # Feed input to classifier to compute logits
        logits = self.classifier(last_hidden_state_cls)
        return logits

model = BertClassifier(config=configuration,freeze_bert=False)

after training


If I print the model after model = AutoModel.from_pretrained(‘path’) I have as the last layer the following and missing my 2 linear layer:

 (output): BertOutput(
          (dense): Linear(in_features=4096, out_features=1024, bias=True)
          (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
          (adapters): ModuleDict()
          (adapter_fusion_layer): ModuleDict()
  (pooler): BertPooler(
    (dense): Linear(in_features=1024, out_features=1024, bias=True)
    (activation): Tanh()
  (prefix_tuning): PrefixTuningPool(
    (prefix_tunings): ModuleDict()

Don’t use AutoModel.from_pretrained() for custom models, Use BertClassifier class which you created… so BertClassifier.from_pretrained(‘path’)