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togethercomputer
RedPajama-Data
Python2023–2024
togethercomputer / RedPajama-Data

RedPajama-Data

The RedPajama-Data repository contains code for preparing large datasets for training large language models.

9Distinct people
9Mentions
0Explicit recommenders
5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The RedPajama-Data repository contains code for preparing large datasets for training 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 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 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 month9 dated mentions

Observed mentions: 2023-04-17 18:39:15 UTC to 2024-08-29 17:14:30 UTC.

0122023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsApr 20232023-05-01 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 comments2023-09-01 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): 1 mentions; 0 submissions, 1 commentsDec 20232024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-06-01 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-07-01 00:00:00 UTC to 2024-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-08-01 00:00:00 UTC to 2024-09-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 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
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC211
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 UTC101
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC000
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC202
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC000
Jun 20242024-06-01 00:00:00 UTC2024-07-01 00:00:00 UTC000
Jul 20242024-07-01 00:00:00 UTC2024-08-01 00:00:00 UTC000
Aug 20242024-08-01 00:00:00 UTC2024-09-01 00:00:00 UTC101

What Hacker News says

9 sources
If you have the model weights you have roughly the same opportunities as the company that trained the model. The code you need to run inference on the Llama weights is very much open source. The only thing you're missing out on is the training code, which is prohibitively expensive to run for most anyways. Open source training isn't going to give you any unique insights into the "digital brain library" of your model. Also just to be clear, if you want to set up a RAG with an open weight model and a large dataset there's nothing stopping you. Download Red Pajama and Llama and give it a try. https://github.com/togethercomputer/RedPajama-Data
In Update on Llama adoption
Yes > RedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text documents coming from 84 CommonCrawl snapshots and processed using the CCNet pipeline. https://github.com/togethercomputer/RedPajama-Data > Dolma Dataset: an open dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. https://allenai.github.io/dolma/
In Apple releases eight small AI language models aimed at on-device use
Gpt-4 was allegedly trained on 6T tokens for 2 epochs (i.e twice). 6T is far from exhausting the text data we have. Here's a 5 language 30T token dataset from web scrapped data. https://github.com/togethercomputer/RedPajama-Data And that is just web scrapped data. There are trillions of valuable tokens worth of text from the likes of pdfs/ebooks, academic journals and other documents that essentially has no web presence otherwise. https://annas-archive.org/llm >To improve performance using the same basic architecture the model need to scale both compute and training data. What you are trying to say here is that you need to scale both parameters and data as scaling data increases compute. That said, it's not really true. It would be best if you scaled both data and parameters but you can get increased performance just scaling one or the ot
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her.
In Choose your weapon: Survival strategies for depressed AI academics
There's a lot of speculation in the comments so I want to talk about the technology that we have __TODAY__. I post a lot about being in ML research and while my focus is on image generation I'm working with another team doing another task but not going to state it explicitly for obvious reasons. What can AI/ML do __today__? We have lots of ways to track people around a building or city. The challenge is to do these tasks through multi-camera systems. This includes things like people tracking (person with random ID but consistent across cameras), face identification (more specific representation that is independent of clothing, which usually identifies the former), gait tracking (how one walks), device tracking (based on bluetooth, wifi, and cellular). There is a lot of mixed success with these tools but I'll let you know some part that sh
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ould concern you: right now these are mostly ResNet50 models, datasets are small, and they are not using advanced training techniques. That is changing. There are legal issues and datasets are becoming proprietary but the size and frequency of gathering data is growing. I'm not going to talk about social media because the metadata problem is an already well discussed one and you all have already made your decisions and we've witnessed the results of those decisions. I'm also not going to talk about China, the most surveilled country in the world, the UK, or any of that for similar reasons. We'll keep talking in general, that is invariant to country. What I will talk about is that modern ML has greatly accelerated the data gathering sector. Your threat models have changed from governments rushing to gather all the data that they can, to big companies joining the game, to now small mom and pop shops doing so. I __really__ implore you all to look at what's in that dataset[0]. There's 5B items, this tool helps retrieve based on CLIP embeddings. You might think "oh yes, Google can already do this" but the difference is that you can't download Google. Google does not give you 16.5TB of clip filtered image,text, & metadata. Or look into the RedPajama dataset[1] which has >30T tokens and 5TB of storage. With 32k tokens being about 50 pages, that's about 47 billion pages. That is, a stack of paper 5000km tall, reaching 5x the height of the ISS and is bigger than the diameter of the moon. I know we all understand that there's big data collection, but do you honestly understand how big these numbers are? I wouldn't even claim to because I cannot accurately conceptualize the size of the moon nor the distance to the ISS. They just roll into the "big" bin in my brain. Today, these systems can track you with decent accuracy even if you use basic obscurification techniques like glasses, hats, or even a surgical mask. Today we can track you not just by image, but how you walk, and can with moderate success do this through walls (meaning no camera to see if you want to know you're being tracked). Today, these systems can de-anonymize you through unique text patterns that you use (see Enron dataset, but scale). Today, these machines can uncanny valley replicas of your speech and text. Today we can make images of people that are convincingly real. Today, these tools aren't exclusive to governments or trillion dollar corporations, but available to any person that is willing to spend a few thousand dollars on compute. I don't want to paint this as a picture of doom and gloom. These tools are amazing and have the potential to do extraordinary good, at levels that would be unimaginable only a few decades ago. Even many of these tools that can invade your privacy are benefits in some ways, but just need to consider context. You cannot build a post scarce society when you require humans to monitor all stores. But like Uncle Ben says, with great power comes great responsibility. A technology that has the capacity to do tremendous good also has the power to do tremendous horrors. The choice is ours and the latter prevails when we are not open. We must ever push for these tools to be used for good, because with them we can truly do amazing things. We do not need AGI to create a post scarce world and I have no doubt that were this to become our primary goal, we could easily reach it within our lifetime without becoming a Sci-Fi dystopia and while tackling existential issues such as climate. To poke the bear a little, I'd argue that if your country wants to show dominance and superiority on the global stage, it is not done so through military power but technology. You will win the culture wars of all culture wars and whoever creates the post scarce world will be a country that will never be forgotten by time. Lift a billion people out of poverty? Try lifting 8 billion not just out of poverty, but into the lower middle class, where no child dreams of being hungry. That is something humans will never forget. So maybe this should be our cold war, not the one in the Pacific. If you're so great, truly, truly show me how superior your country/technology/people are. This is a battle that can be won by anyone at this point, not just China vs the US, but even any European power has the chance to win. [0] https://rom1504.github.io/clip-retrieval/ [1] https://github.com/togethercomputer/RedPajama-Data
In AI and Mass Spying
Mistral 7B [1] and many models stemming from it are released under permissive Apache license. Some might argue that a "pure" open-source would require the dataset and the training "recipe" as it would be needed to reproduce the training, but it would be so expensive that most people wouldn't be able to do much with it. IMO, a release with open weights without the "source" is much better than the opposite, a release with open source and no trained weights. And it's not like there was no progress on the open dataset front: - Together just released RedPajama V2, with enough tokens to train a very sizeable base model. - Tsinghua released UltraFeedback which allowed more people to align models using RLHF methods (like the Zephyr models from Hugging Face) - and many many others [1] https://mistral.ai/news/announcing-mistral-7b/ [2] https://gith
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In AI and Open Source in 2023
Thanks for the suggestion! We will add this in the pool of features for future release. (We are currently running the current 40+ annotations on the `tail` partitions). If you are interested in contributing the code for these features, feel free to do a PR to https://github.com/togethercomputer/RedPajama-Data! Otherwise we will try our best effort implementation :) but we hope that this can become a community effort (feel free to created more issues on github for us to keep track. I created one for this https://github.com/togethercomputer/RedPajama-Data/issues/76)
In RedPajama v2 Open Dataset with 30T Tokens for Training LLMs

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