Fetching the paper…
Reading the bibliography…
Post-training quantization (PTQ) of large language models (LLMs) holds the promise in reducing the prohibitive computational cost at inference time.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 1905
Earlier work this paper cites.
Pointer sentinel mixture models
Merity, S., Xiong, C., Bradbury, J., and Socher, R · 2016
Earlier work this paper cites.
PACT: Parameterized clipping activation for quantized neural networks
Choi, J., Wang, Z., Venkataramani, S., Chuang, P. I.-J., Srinivasan, V., and Gopalakrishnan, K · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Gliwa, B., Mochol, I., Biesek, M., and Wawer, A · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Earlier work this paper cites.
Social iqa: Commonsense reasoning about social interactions
Sap, M., Rashkin, H., Chen, D., Le Bras, R., and Choi, Y · 2019
Earlier work this paper cites.
HellaSwag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
Earlier work this paper cites.
PIQA: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Gao, J., Choi, Y., et al · 2020
Earlier work this paper cites.
Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T. L., Gugger, S., Drame, M., Lhoest, Q., and Rush, A. M · 2020
Earlier work this paper cites.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
Earlier work this paper cites.
Accurate post training quantization with small calibration sets
Hubara, I., Nahshan, Y., Hanani, Y., Banner, R., and Soudry, D · 2021
Earlier work this paper cites.
WinoGrande: An adversarial winograd schema challenge at scale
Sakaguchi, K., Bras, R. L., Bhagavatula, C., and Choi, Y · 2021
Earlier work this paper cites.
QMSum: A new benchmark for query-based multi-domain meeting summarization
Zhong, M., Yin, D., Yu, T., Zaidi, A., Mutuma, M., Jha, R., Hassan, A., Celikyilmaz, A., Liu, Y., Qiu, X., et al · 2021
Earlier work this paper cites.
Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale
Dettmers, T., Lewis, M., Belkada, Y., and Zettlemoyer, L · 2022
Earlier work this paper cites.
GPTQ: Accurate post-training quantization for generative pre-trained transformers
Frantar, E., Ashkboos, S., Hoefler, T., and Alistarh, D · 2022
Earlier work this paper cites.
A survey of quantization methods for efficient neural network inference
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., and Keutzer, K · 2022
Cited alongside, same era.
nuQmm: Quantized matmul for efficient inference of large-scale generative language models
Park, G., Park, B., Kwon, S. J., Kim, B., Lee, Y., and Lee, D · 2022
Cited alongside, same era.
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
Cited alongside, same era.
SpQR: A sparse-quantized representation for near-lossless llm weight compression
Dettmers, T., Svirschevski, R., Egiazarian, V., Kuznedelev, D., Frantar, E., Ashkboos, S., Borzunov, A., Hoefler, T., and Alistarh, D · 2023
Cited alongside, same era.
A framework for few-shot language model evaluation, 07 2024
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 · 2024
Closest in time.
APTQ: Attention-aware post-training mixed-precision quantization for large language models
Guan, Z., Huang, H., Su, Y., Huang, H., Wong, N., and Yu, H · 2024
Closest in time.
ZipCache: Accurate and efficient kv cache quantization with salient token identification
He, Y., Zhang, L., Wu, W., Liu, J., Zhou, H., and Zhuang, B · 2024
Closest in time.
KVQuant: Towards 10 million context length llm inference with kv cache quantization
Hooper, C., Kim, S., Mohammadzadeh, H., Mahoney, M. W., Shao, Y. S., Keutzer, K., and Gholami, A · 2024
Closest in time.
SliM-LLM: Salience-driven mixed-precision quantization for large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kim, S., Hooper, C., Gholami, A., Dong, Z., Li, X., Shen, S., Mahoney, M. W., and Keutzer, K · 2023
Cited alongside, same era.
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
Cited alongside, same era.
FlexGen: High-throughput generative inference of large language models with a single gpu
Sheng, Y., Zheng, L., Yuan, B., Li, Z., Ryabinin, M., Chen, B., Liang, P., Ré, C., Stoica, I., and Zhang, C · 2023
Cited alongside, same era.
CUTLASS, January 2023
Thakkar, V., Ramani, P., Cecka, C., Shivam, A., Lu, H., Yan, E., Kosaian, J., Hoemmen, M., Wu, H., Kerr, A., Nicely, M., Merrill, D., Blasig, D., Qiao, F., Majcher, P., Springer, P., Hohnerbach, M., Wang, J., and Gupta, M · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X. E., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, T · 2023
Cited alongside, same era.
Training transformers with 4-bit integers
Xi, H., Li, C., Chen, J., and Zhu, J · 2023
Cited alongside, same era.
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
Cited alongside, same era.
QUIK: Towards end-to-end 4-bit inference on generative large language models
Ashkboos, S., Markov, I., Frantar, E., Zhong, T., Wang, X., Ren, J., Hoefler, T., and Alistarh, D · 2024
Cited alongside, same era.
Huang, W., Qin, H., Liu, Y., Li, Y., Liu, X., Benini, L., Magno, M., and Qi, X · 2024
Closest in time.
Gear: An efficient kv cache compression recipefor near-lossless generative inference of llm
Kang, H., Zhang, Q., Kundu, S., Jeong, G., Liu, Z., Krishna, T., and Zhao, T · 2024
Closest in time.
OWQ: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models
Lee, C., Jin, J., Kim, T., Kim, H., and Park, E · 2024
Closest in time.
SVDQuant: Absorbing outliers by low-rank components for 4-bit diffusion models
Li, M., Lin, Y., Zhang, Z., Cai, T., Li, X., Guo, J., Xie, E., Meng, C., Zhu, J.-Y., and Han, S · 2024
Closest in time.
Llama 3.2: Revolutionizing edge AI and vision with open, customizable models, 2024a
Meta · 2024
Closest in time.
ESPACE: Dimensionality reduction of activations for model compression
Sakr, C. and Khailany, B · 2024
Closest in time.
Eigen attention: Attention in low-rank space for KV cache compression
Saxena, U., Saha, G., Choudhary, S., and Roy, K · 2024
Closest in time.
Post training quantization of large language models with microscaling formats
Sharify, S., Saxena, U., Xu, Z., Yazar, W., Soloveychik, I., and Wang, X · 2024
Closest in time.
Quip#: Even better llm quantization with hadamard incoherence and lattice codebooks
Tseng, A., Chee, J., Sun, Q., Kuleshov, V., and Sa, C. D · 2024
Closest in time.
Qwen2-VL: Enhancing vision-language model’s perception of the world at any resolution
Wang, P., Bai, S., Tan, S., Wang, S., Fan, Z., Bai, J., Chen, K., Liu, X., Wang, J., Ge, W., Fan, Y., Dang, K., Du, M., Ren, X., Men, R., Liu, D., Zhou, C., Zhou, J., and Lin, J · 2024
Closest in time.
MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Yue, X., Ni, Y., Zhang, K., Zheng, T., Liu, R., Zhang, G., Stevens, S., Jiang, D., Ren, W., Sun, Y., Wei, C., Yu, B., Yuan, R., Sun, R., Yin, M., Zheng, B., Yang, Z., Liu, Y., Huang, W., Sun, H., Su, Y., and Chen, W · 2024
Closest in time.
ABQ-LLM: Arbitrary-bit quantized inference acceleration for large language models
Zeng, C., Liu, S., Xie, Y., Liu, H., Wang, X., Wei, M., Yang, S., Chen, F., and Mei, X · 2024
Closest in time.
Atom: Low-bit quantization for efficient and accurate llm serving
Zhao, Y., Lin, C.-Y., Zhu, K., Ye, Z., Chen, L., Zheng, S., Ceze, L., Krishnamurthy, A., Chen, T., and Kasikci, B · 2024
Closest in time.