Upload model

config.json

@@ -2249,5 +2249,5 @@
   "model_type": "vision-encoder-decoder",
   "tie_word_embeddings": false,
   "torch_dtype": "float32",
-  "transformers_version": "4.39.0"
+  "transformers_version": "4.41.2"
 }

generation_config.json

@@ -1,5 +1,5 @@
 {
   "_from_model_config": true,
   "pad_token_id": 0,
-  "transformers_version": "4.39.0"
+  "transformers_version": "4.41.2"
 }

modelling_longitudinal.py

@@ -7,14 +7,13 @@ import torch
 import transformers
 from peft import LoraConfig, TaskType, get_peft_config, get_peft_model
 from torch.nn import CrossEntropyLoss
-from transformers import (AutoModel, PreTrainedTokenizerFast,
-                          VisionEncoderDecoderModel)
+from transformers import AutoModel, PreTrainedTokenizerFast, VisionEncoderDecoderModel
 from transformers.configuration_utils import PretrainedConfig
-from transformers.modeling_outputs import (BaseModelOutput, ModelOutput,
-                                           Seq2SeqLMOutput)
+from transformers.modeling_outputs import BaseModelOutput, ModelOutput, Seq2SeqLMOutput
 from transformers.modeling_utils import PreTrainedModel
-from transformers.models.vision_encoder_decoder.configuration_vision_encoder_decoder import \
-    VisionEncoderDecoderConfig
+from transformers.models.vision_encoder_decoder.configuration_vision_encoder_decoder import (
+    VisionEncoderDecoderConfig,
+)
 from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
@@ -59,6 +58,7 @@ class MultiCvtWithProjectionHead(transformers.CvtPreTrainedModel):
         pixel_values: Optional[torch.Tensor] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
     ) -> Union[Tuple, ModelOutput]:
 
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict