Papers by Mojtaba Valipour
QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning (2024.emnlp-industry)
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Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu, Marzieh Tahaei, Hyock Kwon, Ali Ghodsi, Boxing Chen, Mehdi Rezagholizadeh
| Challenge: | Existing methods to fine tune large language models require huge memory, limiting the choice to acquire Larger models. |
| Approach: | They propose an efficient quantization approach for dynamic low-rank adaptation that can efficiently fine tune large language models on a set of pre-defined LoRA ranks. |
| Outcome: | The proposed method outperforms QLoRA and is competitive to QLouRA and outperformed when employing its optimal rank. |
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference (2024.findings-eacl)
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| Challenge: | Large language models excel at understanding and generating human-like text, but their widespread deployment can be prohibitively expensive. |
| Approach: | They propose a method that makes large language models dynamic without Pre-Training . they use modularity in networks and sort sub-models based on computation/accuracy in a nested manner. |
| Outcome: | The proposed method can make large language models dynamic without pre-training and replace standard fine-tuning with sorted fine- tuning. |
DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation (2023.eacl-main)
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| Challenge: | Pre-training/fine-tuning of pre-training models has become more expensive and resource-hungry. |
| Approach: | They propose a low-rank adaptation technique that trains LoRA blocks for a range of ranks instead of a single rank. |
| Outcome: | The proposed method trains LoRA blocks for a range of ranks instead of a single rank . it can train dynamic search-free models with DyLoRA at least 4 to 7 times faster than LoRA . |