Summary of Accurate and Efficient Fine-tuning Of Quantized Large Language Models Through Optimal Balance, by Ao Shen et al.
Accurate and Efficient Fine-Tuning of Quantized Large Language Models Through Optimal Balance
by Ao Shen, Qiang Wang, Zhiquan Lai, Xionglve Li, Dongsheng Li
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have achieved impressive results in various domains, but their enormous number of parameters makes fine-tuning challenging. This limitation is addressed by combining parameter quantization with Low-Rank Adaptation (LoRA), which reduces memory usage but leads to noticeable performance degradation. The paper identifies an imbalance in fine-tuning quantized pre-trained models: overly complex adapter inputs and outputs versus low effective trainability of the adaptation. To overcome this, it proposes Quantized LLMs with Balanced-rank Adaptation (Q-BaRA) and Quantization-Aware Fine-tuning with Higher Rank Adaptation (QA-HiRA). Q-BaRA simplifies adapter inputs and outputs while increasing the adapter’s rank for fine-tuning quantized LLMs. QA-HiRA simplifies adapter inputs and outputs to align with pre-trained model block-wise quantization, achieving a higher rank. Both methods optimize performance by requiring fewer trainable parameters and computational effort. The paper applies Q-BaRA and QA-HiRA to the LLaMA and LLaMA2 model families, validating their effectiveness across different fine-tuning datasets and downstream scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have made impressive progress in many areas, but they are hard to use because of how many parameters they have. To make them easier to work with, people combine these parameters with Low-Rank Adaptation (LoRA), which makes the model take up less memory but also hurts its performance a bit. The problem is that the adapter inputs and outputs are too complicated, while the adapter itself isn’t very trainable. The solution is to simplify these adapter inputs and outputs while making the adapter more trainable, which helps fine-tune the models better. |
Keywords
» Artificial intelligence » Fine tuning » Llama » Lora » Low rank adaptation » Quantization
