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Quantization leverages lower-precision weights to reduce the memory usage of large language models (LLMs) and is a key technique for enabling their deployment on commodity hardware.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
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Understanding the threats of trojaned quantized neural network in model supply chains
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Qu-anti-zation: Exploiting quantization artifacts for achieving adversarial outcomes
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Measuring massive multitask language understanding
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton · 2021
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Llm.int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Stealthy backdoors as compression artifacts
Yulong Tian, Fnu Suya, Fengyuan Xu, and David Evans · 2022
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang · 2023
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Awq: Activation-aware weight quantization for llm compression and acceleration
Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, and Song Han · 2023
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LMSYS-Chat-1M: a large-scale real-world LLM conversation dataset
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We analyzed millions of ChatGPT user sessions: Visits are down 29% since may, programming assistance is 30% of use, 2023
Rand Fishkin · 2023
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Open llm leaderboard
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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
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Spqr: A sparse-quantized representation for near-lossless LLM weight compression
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Quantization backdoors to deep learning commercial frameworks
Hua Ma, Huming Qiu, Yansong Gao, Zhi Zhang, Alsharif Abuadbba, Minhui Xue, Anmin Fu, Jiliang Zhang, Said F Al-Sarawi, and Derek Abbott · 2023
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Large language models for code: Security hardening and adversarial testing
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Universal and transferable adversarial attacks on aligned language models
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Jailbreaking black box large language models in twenty queries
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GitHub · 2023
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Hugging Face - the ai community building the future., 2024
Hugging Face · 2024
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Qlora: Efficient finetuning of quantized llms
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Extreme compression of large language models via additive quantization
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Instruction tuning for secure code generation
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Gear: An efficient kv cache compression recipefor near-lossless generative inference of llm
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