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LoRA
Python2023–2024
microsoft / LoRA

LoRA

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

AI & Machine LearningLearning & Reference
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9Distinct people
10Mentions
0Explicit recommenders
13.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 9:28:18 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month10 dated mentions

Observed mentions: 2023-03-24 12:15:25 UTC to 2024-01-02 09:39:44 UTC.

0123452023-03-01 00:00:00 UTC to 2023-04-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 commentsMar 20232023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-05-01 00:00:00 UTC to 2023-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-01 00:00:00 UTC to 2023-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsAug 20232023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJan 2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Mar 20232023-03-01 00:00:00 UTC2023-04-01 00:00:00 UTC514
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC101
May 20232023-05-01 00:00:00 UTC2023-06-01 00:00:00 UTC000
Jun 20232023-06-01 00:00:00 UTC2023-07-01 00:00:00 UTC101
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC000
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC000
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC000
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC101
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC101
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC101

What Hacker News says

1 sources
bluepoint·Jun 8, 2023
I just read the paper about LORA. The main idea is that you write the weights of each neural network as W = W0 + B A Where W0 is the trained model’s weights, which are kept fixed, and A and B are matrices but with a much much lower rank than the originals (say r = 4). It has been shown (as mentioned in the lora paper that training for specific tasks results in low rank corrections, so this is what it is all about. I think that doing LoRa can be done locally. [1] https://github.com/microsoft/LoRA
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