llama-dl VS llama

There seems to be some confusion here. Let me step in as someone who has gone through this.

My repo https://github.com/shawwn/llama-dl was taken down last March by Facebook. They asserted copyright over LLaMA, which is obviously bogus since it was trained on data they do not own the copyright to. I was bummed about this, but after I mentioned on HN that I was willing to fight Meta, an anonymous person named L contacted me and sent $20k of Monero to cover legal fees. I was also contacted by an amazing lawyer who wanted to represent me in this. I was absurdly fortunate on both counts.

He drafted a counternotice, we sent it, and then my repo was restored within a week or so.

GitHub had no choice in the matter. Legally this is a required process. Ditto for GitLab. Both are US companies.

When YouTube-dl was taken down some time ago by a DMCA, Nat went to bat and got it restored, and GitHub made some sort of pledge to cover legal fees associated with bogus takedown requests.

Here’s the shitty part for this particular situation. A case can be made that the emulator is for the purpose of circumventing copyright protection mechanisms. This, sadly, is a solid legal basis for issuing a lawful takedown, as much as we all absolutely despise that idea. It’s pretty clear cut; Nintendo doesn’t want Switch games to be run on non-Nintendo platforms, and the emulator seeks to enable Switch games to be run on any platform. Therefore, the intent of the emulator is to circumvent Nintendo’s protection mechanisms.

So where does this leave us? Well, the team can file a counternotice. GitLab will restore the repo. But that opens up the team to a lawsuit by Nintendo. And as much as I want to stand up to bullies, there’s a difference between standing up to a guy shoving a kid in a locker vs standing up to a Silverback gorilla charging at you. Nintendo’s legal history implies the latter.

Welcome to Nintendo pain. The Smash community has been dealing with Nintendo’s BS for decades now. They shut down tournaments that use emulators for Smash Melee. And no one can do anything, because it’s their legal right to do so.

Your philosophical argument is interesting, but what the op was saying was one of the linked repos in inaccessible due to DMCA: https://github.com/shawwn/llama-dl

So while what you say may be true the DMCA seems to have worth for these orgs because they can get code removed by the host, who is uninterested in litigating, and the repo owner likely is even less capable of litigating the DMCA.

Unfortunately as a tool of fear and legal gridlock DMCA has shown itself to be very useful to those with ill intent.

Here are some useful links: https://github.com/shawwn/llama-dl and https://rentry.org/llama-tard-v2#tips-and-tricks

Sure. You can get models with magnet link from here https://github.com/shawwn/llama-dl/

To get running, just follow these steps https://github.com/ggerganov/llama.cpp/#usage

https://rentry.org/llama-tard-v2#tips-and-tricks https://github.com/shawwn/llama-dl

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.