Quantized DyLoRA utilizes 4-bit Normal Float (NF4) to optimize pre-trained weights, crucial for computations. The proposed method was evaluated against QLoRA through instruct-fine-tuning tasks using benchmarks like MMLU, displaying noticeable performance improvements. Detailed comparisons reveal that QDyLoRA surpasses QLoRA especially at optimal rank settings on various datasets, including WebGLM and GSM8k. Further analyses confirm consistent efficacy across different tasks and data sources, underpinning its advancement in fine-tuning methodologies for large language models.
The proposed method, Quantized DyLoRA, employs 4-bit Normal Float (NF4) for double quantized pre-trained weights, enhancing the efficiency of model fine-tuning.'
In experiments comparing QDyLoRA with QLoRA on the MMLU benchmark, results indicate that QDyLoRA consistently outperforms by optimizing the rank during fine-tuning.
Collection
[
|
...
]