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+{"paper_url": "https://huggingface.co/papers/2406.02913", "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* [AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning](https://huggingface.co/papers/2406.18060) (2024)\n* [Sparse Matrix in Large Language Model Fine-tuning](https://huggingface.co/papers/2405.15525) (2024)\n* [SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining](https://huggingface.co/papers/2406.02214) (2024)\n* [Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning](https://huggingface.co/papers/2407.01320) (2024)\n* [LoQT: Low Rank Adapters for Quantized Training](https://huggingface.co/papers/2405.16528) (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`"}