Large Language Models (LLMs) possess remarkable capabilities, yet their immense size makes fine-tuning a resource-intensive challenge. This bottleneck hinders wider adoption and deployment, especially on resource-constrained devices. While combining quantization with Low-Rank Adaptation (LoRA) reduces memory usage, it often comes at the cost of performance. New research identifies an imbalance at the heart of this issue: overly complex adapter inputs and outputs struggle with the limited trainability of the adaptation process in quantized LLMs. Researchers have introduced two innovative techniques to address this imbalance: Quantized LLMs with Balanced-rank Adaptation (Q-BaRA) and Quantization-Aware Fine-tuning with Higher Rank Adaptation (QA-HiRA). Q-BaRA simplifies the adapter's inputs and outputs while increasing its rank. This creates a more balanced training process, boosting accuracy without increasing the number of trainable parameters. For scenarios requiring low-precision inference models, QA-HiRA streamlines the adapter to align with block-wise quantization, using a single matrix for higher rank adaptation. This method merges adapter parameters directly into the quantized model after fine-tuning, further optimizing performance. Tested on LLaMA and LLaMA2 models, both Q-BaRA and QA-HiRA consistently outperformed existing methods, demonstrating their potential to unlock more efficient and accurate fine-tuning for a wider range of applications. This breakthrough could democratize access to powerful LLMs, allowing developers to fine-tune and deploy them on devices with limited resources. Future research might explore even more sophisticated balancing strategies or how to incorporate these techniques into other model compression methods.