Brief Review — LoRA: Low-Rank Adaptation of Large Language Models

LoRA, Low-Rank LLM Fine-Tuning, Reduce Required Memory

Sik-Ho Tsang
3 min readMay 13


LoRA: Low-Rank Adaptation of Large Language Models,
LoRA, by Microsoft Corporation,
2022 ICLR, Over 280 Citations (Sik-Ho Tsang @ Medium)

Language Model
1991 … 2022 [GPT-NeoX-20B] [GPT-3.5, InstructGPT] [GLM] [MT-NLG 530B] [Chinchilla] [PaLM] [AlexaTM] [BLOOM] [AlexaTM 20B] [OPT] [Switch Transformers] [LaMDA] 2023 [GPT-4]
==== My Other Paper Readings Are Also Over Here ====

  • Full fine-tuning LLM, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example — deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive.
  • Low-Rank Adaptation, or LoRA, is proposed, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.


  1. LoRA
  2. Results

1. LoRA

1.1. Idea

The proposed reparametrization. Only train A and B.
  • For a pre-trained weight matrix W0, its update is constrained by representing the latter with a low-rank decomposition:
  • During training, W0 is frozen and does not receive gradient updates, while A and B contain trainable parameters.
  • For h=W0x, the modified forward pass yields:
  • A random Gaussian initialization is used for A and zero is used for B, so ΔW=BA is zero at the beginning of training.
  • One of the advantages is that when deployed in production, we can explicitly compute and store W=W0+BA and perform inference as usual. No additional latency compared to other methods, such as appending more layers.

1.2. Implementation

  • In the Transformer architecture, there are four weight matrices in the self-attention module (Wq, Wk, Wv, Wo) and two in the MLP module.
  • LoRA only adapts the attention weights for downstream tasks and freezes the MLP modules.

For large Transformer, using LoRA reduces VRAM usage by up to 2/3.

On GPT-3 175B, using LoRA reduces the VRAM consumption during training from 1.2TB to 350GB.

2. Results

RoBERTabase, RoBERTalarge, and DeBERTaXXL with different adaptation methods on the GLUE benchmark.
  • The pre-trained RoBERTa base (125M) and RoBERTa large (355M) from the HuggingFace Transformers library is taken. DeBERTa XXL (1.5B) is also evaluated. They are fine-tuned by different fine-tuning approaches.

Using LoRA gets the best performance on GLUE at much of the time.

GPT-2 medium (M) and large (L) with different adaptation methods on the E2E NLG Challenge.

LoRA still prevails on NLG models, such as GPT-2 medium and large.

Performance of different adaptation methods on GPT-3 175B.
GPT-3 175B validation accuracy vs. number of trainable parameters of several adaptation methods on WikiSQL and MNLI-matched.

Using GPT-3, LoRA matches or exceeds the fine-tuning baseline on all three datasets.

  • (This is a paper introduced by a colleague few months ago. I’ve just read it recently, lol.)



Sik-Ho Tsang

PhD, Researcher. I share what I learn. :) Linktree: for Twitter, LinkedIn, etc.