| {"paper_url": "https://huggingface.co/papers/2210.15523", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [VTrans: Accelerating Transformer Compression with Variational Information Bottleneck based Pruning](https://huggingface.co/papers/2406.05276) (2024)\n* [Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations](https://huggingface.co/papers/2407.05690) (2024)\n* [FoldGPT: Simple and Effective Large Language Model Compression Scheme](https://huggingface.co/papers/2407.00928) (2024)\n* [Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning](https://huggingface.co/papers/2407.01320) (2024)\n* [DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution](https://huggingface.co/papers/2405.17357) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"} |