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Large Language Models (LLMs) like GPT are state-of-the-art text generation models that provide significant assistance in daily routines.
Clipper: A { \{ Low-Latency } \} online prediction serving system
Crankshaw, D., Wang, X., Zhou, G., Franklin, M. J., Gonzalez, J. E., and Stoica, I · 2017
Earlier work this paper cites.
Tensorflow-serving: Flexible, high-performance ml serving
Olston, C., Fiedel, N., Gorovoy, K., Harmsen, J., Lao, L., Li, F., Rajashekhar, V., Ramesh, S., and Soyke, J · 2017
Earlier work this paper cites.
Low latency rnn inference with cellular batching
Gao, P., Yu, L., Wu, Y., and Li, J · 2018
Earlier work this paper cites.
{ \{ PRETZEL } \} : Opening the black box of machine learning prediction serving systems
Lee, Y., Scolari, A., Chun, B.-G., Santambrogio, M. D., Weimer, M., and Interlandi, M · 2018
Earlier work this paper cites.
Nexus: A gpu cluster engine for accelerating dnn-based video analysis
Shen, H., Chen, L., Jin, Y., Zhao, L., Kong, B., Philipose, M., Krishnamurthy, A., and Sundaram, R · 2019
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., et al · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
Earlier work this paper cites.
Turbotransformers: an efficient gpu serving system for transformer models
Fang, J., Yu, Y., Zhao, C., and Zhou, J · 2021
Cited alongside, same era.
Data movement is all you need: A case study on optimizing transformers
Ivanov, A., Dryden, N., Ben-Nun, T., Li, S., and Hoefler, T · 2021
Cited alongside, same era.
Orca: A distributed serving system for { \{ Transformer-Based } \} generative models
Yu, G.-I., Jeong, J. S., Kim, G.-W., Kim, S., and Chun, B.-G · 2022
Cited alongside, same era.
Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al · 2022
Cited alongside, same era.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al · 2023
Liu, X., Hu, L., Bailis, P., Stoica, I., Deng, Z., Cheung, A., and Zhang, H · 2023
Closest in time.
Miao, X., Oliaro, G., Zhang, Z., Cheng, X., Wang, Z., Wong, R. Y. Y., Chen, Z., Arfeen, D., Abhyankar, R., and Jia, Z · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Chatbot instruction prompts datasets
Palla, A · 2023
Closest in time.
Accelerating llm inference with staged speculative decoding
Spector, B. and Re, C · 2023
Closest in time.
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Cited alongside, same era.
Accelerating large language model decoding with speculative sampling
Chen, C., Borgeaud, S., Irving, G., Lespiau, J.-B., Sifre, L., and Jumper, J · 2023
Cited alongside, same era.
Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y · 2023
Cited alongside, same era.
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W · 2023
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Llama: Open and efficient foundation language models, 2023
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G · 2023
Closest in time.