Model Clipping reduce the size of the model.
# ❓ Questions & Help

Is it possible to just reduce the size of a large model to by removing weights and preserving others.
For example we can apply this concept to different dimensions
Depth: Layer dropping this is well known an have been shown to work with huggingface repo.
Width: For scenarios where we need small size of the model, is it possible to just drop all the weights beyond all the seq length L.
For GPT2 kind of model with only left to right flow it shouldn't affect the model perf.
How to implement this without disturbing other weights?

Thanks in advance for help!

<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
     new models and benchmarks, and migration questions. For all other questions,
     we direct you to the Hugging Face forum: https://discuss.huggingface.co/ .
     You can also try Stack Overflow (SO) where a whole community of PyTorch and
     Tensorflow enthusiast can help you out. In this case, make sure to tag your
     question with the right deep learning framework as well as the
     huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     -->

## Details
<!-- Description of your issue -->

<!-- You should first ask your question on the forum or SO, and only if
     you didn't get an answer ask it here on GitHub. -->
**A link to original question on the forum/Stack Overflow**:
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
