| SpQR: A sparse-quantized representation for near-lossless llm weight compression T Dettmers, R Svirschevski, V Egiazarian, D Kuznedelev, E Frantar, ... arXiv preprint arXiv:2306.03078, 2023 | 63 | 2023 |
| Distributed deep learning in open collaborations M Diskin, A Bukhtiyarov, M Ryabinin, L Saulnier, A Sinitsin, D Popov, ... Advances in Neural Information Processing Systems 34, 7879-7897, 2021 | 37 | 2021 |
| Petals: Collaborative inference and fine-tuning of large models A Borzunov, D Baranchuk, T Dettmers, M Ryabinin, Y Belkada, ... arXiv preprint arXiv:2209.01188, 2022 | 29 | 2022 |
| Secure distributed training at scale E Gorbunov, A Borzunov, M Diskin, M Ryabinin International Conference on Machine Learning, 7679-7739, 2022 | 13 | 2022 |
| SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient M Ryabinin, T Dettmers, M Diskin, A Borzunov arXiv preprint arXiv:2301.11913, 2023 | 11 | 2023 |
| Training transformers together A Borzunov, M Ryabinin, T Dettmers, Q Lhoest, L Saulnier, M Diskin, ... NeurIPS 2021 Competitions and Demonstrations Track, 335-342, 2022 | 9 | 2022 |
| Distributed Inference and Fine-tuning of Large Language Models Over The Internet A Borzunov, M Ryabinin, A Chumachenko, D Baranchuk, T Dettmers, ... Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
Max RyabininTogether AIDirección de correo verificada de together.ai
