I have reproduced the error in the "colab-demo-for-donut-base-finetuned-docvqa.ipynb" too.

/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)
3530 "\n\tYou may consider adding ignore_mismatched_sizes=True in the model from_pretrained method."
3531 )
-> 3532 raise RuntimeError(f"Error(s) in loading state_dict for {model.class.name}:\n\t{error_msg}")
3533
3534 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.

I have reproduced the error in the "colab-demo-for-donut-base-finetuned-docvqa.ipynb" too.

/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) 3530 "\n\tYou may consider adding ignore_mismatched_sizes=True in the model from_pretrained method." 3531 ) -> 3532 raise RuntimeError(f"Error(s) in loading state_dict for {model.class.name}:\n\t{error_msg}") 3533 3534 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.

@yaoliUoA i am facing the same issue, have u resolved this?

This might relate to #206