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The case for 4-bit precision: k-bit Inference Scaling Laws

, и . (2022)cite arxiv:2212.09720.

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Arno Dettmers

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8-bit Optimizers via Block-wise Quantization., , , и . ICLR, OpenReview.net, (2022)Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models., , , , , , и . CoRR, (2022)Training Transformers Together., , , , , , , и . CoRR, (2022)High Performance Natural Language Processing., , , , , и . EMNLP (Tutorial Abstracts), стр. 24-27. Association for Computational Linguistics, (2020)The case for 4-bit precision: k-bit Inference Scaling Laws., и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 7750-7774. PMLR, (2023)LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale., , , и . CoRR, (2022)Training Transformers Together., , , , , , , и . NeurIPS (Competition and Demos), том 176 из Proceedings of Machine Learning Research, стр. 335-342. PMLR, (2021)Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model., , , , и . EMNLP, стр. 15038-15061. Association for Computational Linguistics, (2023)The case for 4-bit precision: k-bit Inference Scaling Laws, и . (2022)cite arxiv:2212.09720.QLoRA: Efficient Finetuning of Quantized LLMs, , , и . (2023)cite arxiv:2305.14314Comment: Extended NeurIPS submission.