QLoRA

term_id: qlora

Category: training_techniques

Definition

QLoRA combines Low-Rank Adaptation (LoRA) with 4-bit quantization to significantly reduce the memory footprint required for fine-tuning massive models. By storing weights in 4-bit format and adding trainable low-rank decomposition matrices, it enables fine-tuning of models with billions of parameters on consumer-grade hardware. This technique maintains performance comparable to full-precision fine-tuning while drastically lowering computational costs and increasing accessibility.

Summary

Quantized Low-Rank Adaptation, a method for efficiently fine-tuning large language models using 4-bit quantization and low-rank adapters.

Key Concepts

  • Low-Rank Adaptation
  • 4-Bit Quantization
  • Memory Efficiency
  • Fine-Tuning

Use Cases

  • Consumer GPU Fine-Tuning
  • Resource-Constrained Environments
  • Rapid Model Iteration

Code Example

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from peft import LoraConfig, get_peft_model
config = LoraConfig(r=8, lora_alpha=32, target_modules=["q_proj", "v_proj"])
model = get_peft_model(base_model, config)

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