Introduction
Fine-tuning large language models (LLMs) can be computationally expensive, requiring significant memory and processing power. LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) are techniques that make fine-tuning more efficient by reducing the number of trainable parameters and memory usage. In this article, we will explore how these methods work and their impact on computations.
What is LoRA?
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that reduces the number of trainable parameters by adding small low-rank matrices to an existing pre-trained model instead of modifying the original weights.
How LoRA Works
- Original Model Weights: The pre-trained LLM has large weight matrices W.
- LoRA Addition: Instead of modifying W, LoRA introduces two small trainable matrices A and B.
- Modified Weight Calculation:
W_effective = W + ΔW, where ΔW = A × B
- Training: Only the newly introduced matrices A and B are updated while W remains frozen.
- Inference: The modified weight (W + A × B) is used for computations.
Advantages of LoRA
- Reduces memory usage (since fewer parameters are updated).
- Speeds up fine-tuning by keeping most of the model frozen.
- No need to store multiple copies of the full model, making deployment easier.
What is QLoRA?
QLoRA (Quantized LoRA) improves upon LoRA by applying quantization, which reduces the precision of model weights to 4-bit before fine-tuning. This drastically reduces memory usage while maintaining performance.
How QLoRA Works
-
Step 1: Quantization
- The original model weights W are stored in low-bit precision (e.g., 4-bit) to save memory.
- The quantized weight is represented as W_q.
-
Step 2: Apply LoRA
- Similar to LoRA, small matrices A and B are added, and the modified weight is computed as:
Y = (W_q + A × B) X
- Only A and B are updated during training, while W_q remains frozen.
Advantages of QLoRA
- Even lower memory usage than LoRA due to 4-bit quantization.
- Allows fine-tuning massive models (e.g., 65B parameters) on consumer GPUs.
- Maintains high accuracy while significantly reducing computational costs.
Key Differences Between LoRA and QLoRA
| Feature | LoRA | QLoRA |
|---|---|---|
| Changes to Weights? | No, adds new trainable matrices | No, adds new matrices after quantization |
| Memory Usage | Moderate | Lower (due to 4-bit quantization) |
| Speed | Faster than full fine-tuning | Even faster (due to smaller memory footprint) |
| Best For | Fine-tuning efficiently on GPUs with moderate VRAM | Fine-tuning huge models on small GPUs |
Do the Original Weights Get Used?
✅ Yes! During training and inference, the original model weights W are used, but they remain unchanged.
- Forward Pass: Instead of using W, the model computes output using (W + A × B) X.
- Backward Pass: Only A and B get updated, while W stays frozen.
For QLoRA, the same process applies, but the original weights W_q are quantized to 4-bit before adding LoRA adapters.
Conclusion
LoRA and QLoRA make fine-tuning large models more accessible and efficient by reducing the number of trainable parameters and memory usage. While LoRA helps reduce computational costs, QLoRA takes it further by applying quantization, making it possible to fine-tune massive models on low-VRAM devices.
🚀 If you're looking to fine-tune an LLM efficiently, LoRA and QLoRA are game-changers!
Top comments (1)
Interesting read!!