Qrazor: Reliable and Effortless 4-bit LLM Quantization by Significant Data Razoring

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Patro, S. G. K. and Sahu, K. K · 2015

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The lambada dataset: Word prediction requiring a broad discourse context, 2016

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Paperno, D., Kruszewski, G., Lazaridou, A., Pham, Q. N., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., and Fernández, R · 2016

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Post-training 4-bit quantization of convolution networks for rapid-deployment, 2019

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Banner, R., Nahshan, Y., Hoffer, E., and Soudry, D · 2019

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O-2a: Low overhead dnn compression with outlier-aware approximation

Ho, N.-D., Le, M.-S., and Chang, I.-J · 2020

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Vs-quant: Per-vector scaled quantization for accurate low-precision neural network inference, 2021

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Dai, S., Venkatesan, R., Ren, H., Zimmer, B., Dally, W. J., and Khailany, B · 2021

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The choice of scaling technique matters for classification performance

de Amorim, L. B., Cavalcanti, G. D., and Cruz, R. M · 2022

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Gpt3.int8(): 8-bit matrix multiplication for transformers at scale

Dettmers, T., Lewis, M., Belkada, Y., and Zettlemoyer, L · 2022

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Post-training quantization for energy efficient realization of deep neural networks, 2022

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Latotzke, C., Balim, B., and Gemmeke, T · 2022

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Zeroquant: Efficient and affordable post-training quantization for large-scale transformers

Yao, Z., Yazdani Aminabadi, R., Zhang, M., Wu, X., Li, C., and He, Y · 2022

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Quik: Towards end-to-end 4-bit inference on generative large language models, 2023

Original

Ashkboos, S., Markov, I., Frantar, E., Zhong, T., Wang, X., Ren, J., Hoefler, T., and Alistarh, D · 2023

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Quantease: Optimization-based quantization for language models, 2023

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Behdin, K., Acharya, A., Gupta, A., Song, Q., Zhu, S., Keerthi, S., and Mazumder, R · 2023

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Spqr: A sparse-quantized representation for near-lossless llm weight compression, 2023

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Dettmers, T., Svirschevski, R., Egiazarian, V., Kuznedelev, D., Frantar, E., Ashkboos, S., Borzunov, A., Hoefler, T., and Alistarh, D · 2023

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Gptq: Accurate post-training quantization for generative pre-trained transformers, 2023

Original

Frantar, E., Ashkboos, S., Hoefler, T., and Alistarh, D · 2023

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A framework for few-shot language model evaluation, 12 2023

Gao, L., Tow, J., Abbasi, B., Biderman, S., Black, S., DiPofi, A., Foster, C., Golding, L., Hsu, J., Le Noac’h, A., Li, H., McDonell, K., Muennighoff, N., Ociepa, C., Phang, J., Reynolds, L., Schoelkopf, H., Skowron, A., Sutawika, L., Tang, E., Thite, A., Wang, B., Wang, K., and Zou, A · 2023

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Olive: Accelerating large language models via hardware-friendly outlier-victim pair quantization

Guo, C., Tang, J., Hu, W., Leng, J., Zhang, C., Yang, F., Liu, Y., Guo, M., and Zhu, Y · 2023

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Quip: 2-bit quantization of large language models with guarantees, 2024

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Chee, J., Cai, Y., Kuleshov, V., and Sa, C. D · 2024

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Optimize weight rounding via signed gradient descent for the quantization of llms, 2024

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Cheng, W., Zhang, W., Shen, H., Cai, Y., He, X., Lv, K., and Liu, Y · 2024

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The llama 3 herd of models, 2024

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