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added TMLR paper on BNN inference #59

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added TMLR paper on BNN inference #59

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Expand Up @@ -111,6 +111,7 @@ Amir Gholami\* , Sehoon Kim\* , Zhen Dong\* , Zhewei Yao\* , Michael W. Mahoney,

### 2024

- [[TMLR](https://openreview.net/pdf?id=IEKtMMSblm)] PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-Off [[code](https://github.com/sachitkuhar/PLUM)][[webpage](https://github.com/sachitkuhar/PLUM)][[video](https://www.youtube.com/watch?v=nE_CYDWqQ_I)][**`bnn`**] [**`inference`**]
- [[arXiv](https://arxiv.org/abs/2404.14047)] How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study [[code](https://github.com/Macaronlin/LLaMA3-Quantization)]![GitHub Repo stars](https://img.shields.io/github/stars/Macaronlin/LLaMA3-Quantization) [[HuggingFace](https://huggingface.co/LLMQ)]
- [[arXiv](https://arxiv.org/abs/2402.05445)] Accurate LoRA-Finetuning Quantization of LLMs via Information Retention [[code](https://github.com/htqin/IR-QLoRA)]![GitHub Repo stars](https://img.shields.io/github/stars/htqin/IR-QLoRA)
- [[arXiv](https://arxiv.org/abs/2402.04291)] BiLLM: Pushing the Limit of Post-Training Quantization for LLMs [[code](https://github.com/Aaronhuang-778/BiLLM)]![GitHub Repo stars](https://img.shields.io/github/stars/Aaronhuang-778/BiLLM)
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