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Zjh-819
LLMDataHub
AI & Machine Learning2023–2024
Zjh-819 / LLMDataHub

LLMDataHub

A quick guide (especially) for trending instruction finetuning datasets

2Distinct people
2Mentions
0Explicit recommenders
3.4KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A quick guide (especially) for trending instruction finetuning datasets

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 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM 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 week2 dated mentions

Observed mentions: 2023-07-22 05:37:55 UTC to 2024-01-06 18:42:44 UTC. Weeks start Monday, UTC.

012023-07-17 00:00:00 UTC to 2023-07-24 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 172023-07-24 00:00:00 UTC to 2023-07-31 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-07-31 00:00:00 UTC to 2023-08-07 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-07 00:00:00 UTC to 2023-08-14 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-14 00:00:00 UTC to 2023-08-21 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-21 00:00:00 UTC to 2023-08-28 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-28 00:00:00 UTC to 2023-09-04 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-04 00:00:00 UTC to 2023-09-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-11 00:00:00 UTC to 2023-09-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-18 00:00:00 UTC to 2023-09-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-25 00:00:00 UTC to 2023-10-02 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-10-02 00:00:00 UTC to 2023-10-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-10-09 00:00:00 UTC to 2023-10-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 92023-10-16 00:00:00 UTC to 2023-10-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-10-23 00:00:00 UTC to 2023-10-30 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-10-30 00:00:00 UTC to 2023-11-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-11-06 00:00:00 UTC to 2023-11-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-11-13 00:00:00 UTC to 2023-11-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-11-20 00:00:00 UTC to 2023-11-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-11-27 00:00:00 UTC to 2023-12-04 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-04 00:00:00 UTC to 2023-12-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-11 00:00:00 UTC to 2023-12-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-18 00:00:00 UTC to 2023-12-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-25 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 2024-01-08 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJan 1

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
Jul 172023-07-17 00:00:00 UTC2023-07-24 00:00:00 UTC101
Jul 242023-07-24 00:00:00 UTC2023-07-31 00:00:00 UTC000
Jul 312023-07-31 00:00:00 UTC2023-08-07 00:00:00 UTC000
Aug 72023-08-07 00:00:00 UTC2023-08-14 00:00:00 UTC000
Aug 142023-08-14 00:00:00 UTC2023-08-21 00:00:00 UTC000
Aug 212023-08-21 00:00:00 UTC2023-08-28 00:00:00 UTC000
Aug 282023-08-28 00:00:00 UTC2023-09-04 00:00:00 UTC000
Sep 42023-09-04 00:00:00 UTC2023-09-11 00:00:00 UTC000
Sep 112023-09-11 00:00:00 UTC2023-09-18 00:00:00 UTC000
Sep 182023-09-18 00:00:00 UTC2023-09-25 00:00:00 UTC000
Sep 252023-09-25 00:00:00 UTC2023-10-02 00:00:00 UTC000
Oct 22023-10-02 00:00:00 UTC2023-10-09 00:00:00 UTC000
Oct 92023-10-09 00:00:00 UTC2023-10-16 00:00:00 UTC000
Oct 162023-10-16 00:00:00 UTC2023-10-23 00:00:00 UTC000
Oct 232023-10-23 00:00:00 UTC2023-10-30 00:00:00 UTC000
Oct 302023-10-30 00:00:00 UTC2023-11-06 00:00:00 UTC000
Nov 62023-11-06 00:00:00 UTC2023-11-13 00:00:00 UTC000
Nov 132023-11-13 00:00:00 UTC2023-11-20 00:00:00 UTC000
Nov 202023-11-20 00:00:00 UTC2023-11-27 00:00:00 UTC000
Nov 272023-11-27 00:00:00 UTC2023-12-04 00:00:00 UTC000
Dec 42023-12-04 00:00:00 UTC2023-12-11 00:00:00 UTC000
Dec 112023-12-11 00:00:00 UTC2023-12-18 00:00:00 UTC000
Dec 182023-12-18 00:00:00 UTC2023-12-25 00:00:00 UTC000
Dec 252023-12-25 00:00:00 UTC2024-01-01 00:00:00 UTC000
Jan 12024-01-01 00:00:00 UTC2024-01-08 00:00:00 UTC101

What Hacker News says

2 sources
> the cost of these synthetic datasets is very high. Nobody is sharing There are plenty of synthetic datasets generated from GPT-4 and other models^[1]. But MS created a large one, 150B tokens. Still 2 orders of magnitude smaller than the 13T used to train GPT-4. But in the future this will be the main way to improve models - put them to work, and filter their good stuff. Then retrain. Very expensive, but that is the cost of evolution. It took humans a very long time to create the culture and technology that underlies LLMs, it will take a similar effort to push them forward. Human generated text was the low hanging fruit, but now that it's picked, synthetic data is the only way forward. Models generating their own experience and feedback, doing exploration, combinatorial search, learning from their interactions with humans, from games, ex
Read the rest
periments and simulations. But if we're talking about synthetic data - then the elephant in the room is the chat logs of OpenAI. They got 180M users, assume 10K tokens/user/month, that would be 1.8B tokens per month, mostly AI written but interspersed with human replies and tool generated output. This means they can collect in less than a year about as much synthetic data as the original training set. What if they train GPT-5 solely on synthetic data? That would simplify the copyright issues a lot, and give a 5x boost in efficiency. [1] https://github.com/Zjh-819/LLMDataHub
In Microsoft Phi-2 model changes licence to MIT
That's not how copyright law works at all. It doesn't say "well if you didn't want someone to copy this thing you should have stopped them from doing it". It lays out 4 factors for a court to consider about whether something is fair use and none of them are around how easy it was to rip the work off.[1] In the LLM space it seems even more clear because many/most of the works in the various corpora used for this training have very clear copyright terms which prevent digital storage and reproduction without the publishers permission (just look at the reverse of the title page of any book for the copyright notice if you don't believe me). Finally, for LLMs many/most of the works are in corpora[2] that people just download so they aren't looking at a robots.txt file put up by teh original site. If you look at The Pile paper[3] for example
Read the rest
they explicitly say that much of the material is under copyright and that they are relying on fair use. [1]: https://fairuse.stanford.edu/overview/fair-use/four-factors/ [2] https://github.com/Zjh-819/LLMDataHub for example [3] https://arxiv.org/abs/2101.00027
In In the LLM space, "open source" is being used to mean "downloadable weights"

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