Bug Report: Pretrained checkpoint and current DonutModel mismatch

Hello,

**Describe the bug**
I cloned the latest master (commit 7df02e13, Sept 30, 2025) and downloaded the pre-trained model via Google Drive (`dolphin_model.bin` and `dolphin_tokenizer.json`).

When loading the pretrained weights, the model fails to load into the current `DonutModel` due to size mismatch errors in the vision encoder layers.

It seems that the current `DonutModel` vision tower (`vpm.*` layers) has different dimensions compared to the released checkpoint.

**Questions**
- Are the Google Drive weights outdated relative to the current code?
- Could you release an updated checkpoint matching the latest master?

Thanks a lot for your work on this project!

**Error traceback**

```
RuntimeError: Error(s) in loading state_dict for DonutModel:
size mismatch for vpm.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 vpm.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 vpm.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 vpm.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 vpm.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 vpm.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]).
```

Start by comparing the released dolphin_model.bin and dolphin_tokenizer.json with the current DonutModel vision tower, especially the vpm.model.layers.1 and layers.2 shapes shown in the traceback. Check whether the checkpoint matches another revision of the model; done means establishing a compatible checkpoint/code pairing or documenting that the released weights are outdated.

Written by the indexing model from the issue text.