dmca VS llama

Every DMCA takedown notice in https://github.com/github/dmca is a net win for git as a decentralized version control system, especially for repositories that require signed commits. Unfortunately, some people upload only a snapshot of their repository working directory instead of... using git like it is supposed to be used I guess?

I’m with you thus far, but I’m trying to parse the notice to see why GitHub is even complying (see https://github.com/github/dmca/blob/master/2024/03/2024-03-1... ) and I think Torrentfreak got it wrong. I think they’re trying to claim infringement on Plex Server, but the English in the takedown notice is barely legible (and it feels like a mixup between trademarks and copyright), to the point that I find GitHub’s compliance odd.

Text of tweet thread:

Hello everyone. We have some unfortunate news to share with you.

Today, we received a DMCA takedown from Valve on all our public GitHub repositories and all its forks made by the community.

https://github.com/github/dmca/blob/master/2024/01/2024-01-1...

While we were discussing the project's future internally recently, we already came to the conclusion to stop the development of the project due to the current state of the code being unusable anymore with s&box's recent major engine changes, and that we overall moved on from it.

Sadly, this means this DMCA takedown is the nail in the coffin. We cannot bring it back and we've hit Valve's attention, it seems like they definitely don't want us to use their IP (which is totally fair and legal from them).

DMCA from GitHub: https://github.com/github/dmca/blob/master/2023/10/2023-10-10-mazda.md

https://github.com/github/dmca/blob/master/2023/10/2023-10-10-mazda.md

https://www.thedrive.com/news/mazda-slaps-developer-with-cease-and-desist-for-diy-smart-home-integration

https://web.archive.org/web/20231014070536/https://old.reddit.com/r/homeassistant/comments/1771ywu/removal_of_mazda_connected_services_integration/

https://community.home-assistant.io/t/mazda-connected-service/354221

https://community.home-assistant.io/t/removal-of-mazda-connected-services-integration/625885/36

https://www.home-assistant.io/

https://youtu.be/l2qKEkG29gI

https://youtu.be/NfiIXooD77s

https://youtu.be/PrtbYu1OYhY

https://youtu.be/nigJMu0lUbM

https://youtu.be/qLlxOD5IHYc

> Is there anything that average folk can do against these notices when they're sent to companies like GitHub?

They can file a counter-notice, commit to accepting service of process if the other party wants to sue, and wait 10-14 days. There's a guide at https://docs.github.com/en/site-policy/content-removal-polic... and there are plenty of example counter-notices in the https://github.com/github/dmca repo.

From the posted notice[1], they answered "No" to the question "Do you claim to have any technological measures in place to control access to your copyrighted content? Please see our Complaints about Anti-Circumvention Technology if you are unsure." Is that the same thing you are referencing?

[1] https://github.com/github/dmca/blob/master/2023/10/2023-10-1...

derivative works thereof).”

https://github.com/meta-llama/llama/blob/b8348da38fde8644ef0...

Also even if you did use Llama for something, they could unilaterally pull the rug on you when you got 700 million years, AND anyone who thinks Meta broke their copyright loses their license. (Checking if you are still getting screwed is against the rules)

Therefore, Zuckerberg is accountable for explicitly anticompetitive conduct, I assumed an MMA fighter would appreciate the value of competition, go figure.

One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.

It looks like Llama 2 7B took 184,320 A100-80GB GPU-hours to train[1]. This one says it used a 96×H100 GPU cluster for 2 weeks, for 32,256 hours. That's 17.5% of the number of hours, but H100s are faster than A100s [2] and FP16/bfloat16 performance is ~3x better.

If they had tried to replicate Llama 2 identically with their hardware setup, it'd cost a little bit less than twice their MoE model.

[1] https://github.com/meta-llama/llama/blob/main/MODEL_CARD.md#...

Ironically, the LLaMA license text [1] this is lifted verbatim from is itself copyrighted [2] and doesn't grant you the permission to copy it or make changes like s/meta/dbrx/g lol.

[1] https://github.com/meta-llama/llama/blob/main/LICENSE#L65

This is kind of an epistemological debate at this level, and I make an effort to link to some source code [1] any time it seems contentious.

LLMs (of the decoder-only, generative-pretrained family everyone means) are next token predictors in a literal implementation sense (there are some caveats around batching and what not, but none that really matter to the philosophy of the thing).

But, they have some emergent behaviors that are a trickier beast. Probably the best way to think about a typical Instruct-inspired “chat bot” session is of them sampling from a distribution with a KL-style adjacency to the training corpus (sidebar: this is why shops that do and don’t train/tune on MMLU get ranked so differently than e.g. the arena rankings) at a response granularity, the same way a diffuser/U-net/de-noising model samples at the image batch (NCHW/NHWC) level.

The corpus is stocked with everything from sci-fi novels with computers arguing their own sentience to tutorials on how to do a tricky anti-derivative step-by-step.

This mental model has adequate explanatory power for anything a public LLM has ever been shown to do, but that only heavily implies it’s what they’re doing.

There is active research into whether there is more going on that is thus far not conclusive to the satisfaction of an unbiased consensus. I personally think that research will eventually show it’s just sampling, but that’s a prediction not consensus science.

They might be doing more, there is some research that represents circumstantial evidence they are doing more.

[1] https://github.com/meta-llama/llama/blob/54c22c0d63a3f3c9e77...

Predicting subsequent text is pretty much exactly what they do. Lots of very cool engineering that’s a real feat, but at its core it’s argmax(P(token|token,corpus)):

https://github.com/facebookresearch/llama/blob/main/llama/ge...

The engineering feats are up there with anything, but it’s a next token predictor.

https://github.com/facebookresearch/llama/pull/947/

> Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!

actually its not just a basic version. Llama 1/2's model.py is 500 lines: https://github.com/facebookresearch/llama/blob/main/llama/mo...

Mistral (is rumored to have) forked llama and is 369 lines: https://github.com/mistralai/mistral-src/blob/main/mistral/m...

and both of these are SOTA open source models.

In transformers, they tried really hard to have a single function or method to deal with both self and cross attention mechanisms, masking, positional and relative encodings, interpolation etc. While it allows a user to use the same function/method for any model, it has led to severe parameter bloat. Just compare the original implementation of llama by FAIR with the implementation by HF to get an idea.