Papers with LOMO
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)
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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu
| Challenge: | Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions . |
| Approach: | They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers . |
| Outcome: | The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios. |
Full Parameter Fine-tuning for Large Language Models with Limited Resources (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) require massive GPU resources for training. |
| Approach: | They propose a parameter-efficient optimization that fuses the gradient computation and parameter update in one step to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage to 10.8% compared to the standard approach. |
AdaLomo: Low-memory Optimization with Adaptive Learning Rate (2024.findings-acl)
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| Challenge: | Large language models require substantial memory for training, thereby setting a high hardware threshold. |
| Approach: | They propose a low-memory optimization technique that reduces memory footprint . they propose an adaptive learning rate for each parameter and a grouped update normalization to stabilize convergence . |
| Outcome: | The proposed low-memory optimization performs better than the prevailing algorithm for large language models, AdamW. |