The latest update has the model weights twice the embedding dim size of the actual model installed through github or pip

[Samartha27]

12 pretrained_model = DonutModel.from_pretrained(args.pretrained_path)
13
14 if torch.cuda.is_available():

2 frames
/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py in _load_pretrained_model(cls, model, state_dict, loaded_keys, resolved_archive_file, pretrained_model_name_or_path, ignore_mismatched_sizes, sharded_metadata, _fast_init, low_cpu_mem_usage, device_map, offload_folder, offload_state_dict, dtype, is_quantized, keep_in_fp32_modules)
3929 "\n\tYou may consider adding ignore_mismatched_sizes=True in the model from_pretrained method."
3930 )
-> 3931 raise RuntimeError(f"Error(s) in loading state_dict for {model.class.name}:\n\t{error_msg}")
3932
3933 if is_quantized:

RuntimeError: Error(s) in loading state_dict for DonutModel:
size mismatch for encoder.model.layers.1.downsample.norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for encoder.model.layers.1.downsample.norm.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for encoder.model.layers.1.downsample.reduction.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for encoder.model.layers.2.downsample.norm.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for encoder.model.layers.2.downsample.norm.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for encoder.model.layers.2.downsample.reduction.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([512, 1024]).
You may consider adding ignore_mismatched_sizes=True in the model from_pretrained method.

[xiaochu1131]

Hello, I've met the same problem with you.
May I know how can you solve this problem?

[xiaochu1131]

!pip install transformers==4.25.1
!pip install pytorch-lightning==1.6.4
!pip install timm==0.5.4
!pip install gradio
!pip install donut-python

It seems to be a problem of incorrect package version.
After changing transformers to version 4.25.1 and timm to version 0.5.4, I have solved this problem.