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Secure Distributed Training at Scale., , , and . CoRR, (2021)Training Transformers Together., , , , , , , and . NeurIPS (Competition and Demos), volume 176 of Proceedings of Machine Learning Research, page 335-342. PMLR, (2021)SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 29416-29440. PMLR, (2023)Petals: Collaborative Inference and Fine-tuning of Large Models., , , , , , , and . ACL (demo), page 558-568. Association for Computational Linguistics, (2023)Distributed Inference and Fine-tuning of Large Language Models Over The Internet., , , , , , , and . CoRR, (2023)Training Transformers Together., , , , , , , and . CoRR, (2022)SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression., , , , , , , , and . CoRR, (2023)Petals: Collaborative Inference and Fine-tuning of Large Models., , , , , , , and . CoRR, (2022)Distributed Deep Learning In Open Collaborations., , , , , , , , , and 6 other author(s). NeurIPS, page 7879-7897. (2021)Secure Distributed Training at Scale., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 7679-7739. PMLR, (2022)