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Post-training quantization of Large Language Models (LLMs) is challenging.
Distribution of eigenvalues for some sets of random matrices
Marchenko, V. and Pastur, L · 1967
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Pointer sentinel mixture models, 2016
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016
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The lambada dataset: Word prediction requiring a broad discourse context
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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Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
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Training dnns with hybrid block floating point
Drumond, M., Lin, T., Jaggi, M., and Falsafi, B · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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Longformer: The long-document transformer
Beltagy, I., Peters, M. E., and Cohan, A · 2020
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Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Bras, R. L., Gao, J., and Choi, Y · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Pushing the limits of narrow precision inferencing at cloud scale with microsoft floating point
Darvish Rouhani, B., Lo, D., Zhao, R., Liu, M., Fowers, J., Ovtcharov, K., Vinogradsky, A., Massengill, S., Yang, L., Bittner, R., et al · 2020
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A block minifloat representation for training deep neural networks
Fox, S., Rasoulinezhad, S., Faraone, J., Leong, P., et al · 2020
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Understanding and overcoming the challenges of efficient transformer quantization
Bondarenko, Y., Nagel, M., and Blankevoort, T · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
Cited alongside, same era.
A white paper on neural network quantization
Nagel, M., Fournarakis, M., Amjad, R. A., Bondarenko, Y., Van Baalen, M., and Blankevoort, T · 2021
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Llm. int8 (): 8-bit matrix multiplication for transformers at scale
Dettmers, T., Lewis, M., Belkada, Y., and Zettlemoyer, L · 2022
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Frantar, E., Ashkboos, S., Hoefler, T., and Alistarh, D · 2022
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
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Awq: Activation-aware weight quantization for llm compression and acceleration
Lin, J., Tang, J., Tang, H., Yang, S., Dang, X., and Han, S · 2023
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Estimating the carbon footprint of bloom, a 176b parameter language model
Luccioni, A. S., Viguier, S., and Ligozat, A.-L · 2023
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Long-range zero-shot generative deep network quantization
Luo, Y., Gao, Y., Zhang, Z., Fan, J., Zhang, H., and Xu, M · 2023
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Ocp 8-bit floating point specification (ofp8), 2023
Micikevicius, P., Oberman, S., Dubey, P., Cornea, M., Rodriguez, A., Bratt, I., Grisenthwaite, R., Jouppi, N., Chou, C., Huffman, A., Schulte, M., Wittig, R., Jani, D., and Deng, S · 2023
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Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2022
Cited alongside, same era.
Outlier suppression: Pushing the limit of low-bit transformer language models
Wei, X., Zhang, Y., Zhang, X., Gong, R., Zhang, S., Zhang, Q., Yu, F., and Liu, X · 2022
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
Workshop, B., Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., et al · 2022
Cited alongside, same era.
Fast: Dnn training under variable precision block floating point with stochastic rounding
Zhang, S. Q., McDanel, B., and Kung, H · 2022
Cited alongside, same era.
Gqa: Training generalized multi-query transformer models from multi-head checkpoints
Ainslie, J., Lee-Thorp, J., de Jong, M., Zemlyanskiy, Y., Lebrón, F., and Sanghai, S · 2023
Cited alongside, same era.
Quantizable transformers: Removing outliers by helping attention heads do nothing
Bondarenko, Y., Nagel, M., and Blankevoort, T · 2023
Cited alongside, same era.
Quip: 2-bit quantization of large language models with guarantees
Chee, J., Cai, Y., Kuleshov, V., and De Sa, C · 2023
Cited alongside, same era.
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
Cited alongside, same era.
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Omniquant: Omnidirectionally calibrated quantization for large language models
Shao, W., Chen, M., Zhang, Z., Xu, P., Zhao, L., Li, Z., Zhang, K., Gao, P., Qiao, Y., and Luo, P · 2023
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SlimPajama: A 627B token cleaned and deduplicated version of RedPajama
Soboleva, D., Al-Khateeb, F., Myers, R., Steeves, J. R., Hestness, J., and Dey, N · 2023
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Easyquant: An efficient data-free quantization algorithm for llms
Tang, H., Sun, Y., Wu, D., Liu, K., Zhu, J., and Kang, Z · 2023
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Wei, X., Zhang, Y., Li, Y., Zhang, X., Gong, R., Guo, J., and Liu, X · 2023
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Smoothquant: Accurate and efficient post-training quantization for large language models
Xiao, G., Lin, J., Seznec, M., Wu, H., Demouth, J., and Han, S · 2023
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Revisiting block-based quantisation: What is important for sub-8-bit LLM inference?
Zhang, C., Cheng, J., Shumailov, I., Constantinides, G., and Zhao, Y · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2023
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LLM-Tracker: OmniQuant, 2024
Lin, L · 2024
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