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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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Almut Dettmers

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Heinrich Dettmers

Swantje Dettmers

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8-bit Optimizers via Block-wise Quantization., , , и . ICLR, OpenReview.net, (2022)SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression., , , , , , , , и . ICLR, OpenReview.net, (2024)Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models., , , , , , и . CoRR, (2022)Training Transformers Together., , , , , , , и . CoRR, (2022)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)High Performance Natural Language Processing., , , , , и . EMNLP (Tutorial Abstracts), стр. 24-27. Association for Computational Linguistics, (2020)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)Petals: Collaborative Inference and Fine-tuning of Large Models., , , , , , , и . CoRR, (2022)