I give it a week before we see tools for subtly watermarking your secret LLM's weights, so you can trace leaks like this later.
Watermarking the weights is trivial. Watermarking the output is also possible, but more complex and with a statistical success rate Vs performance tradeoff.
Facebook LLAMA is being openly distributed via torrents
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Re: Facebook LLAMA is being openly distributed via torrents
#102It seems that the leak originated from 4chan [1]. Two people in the same thread had access to the weights and verified that their hashes match [2][3] to make sure that the model isn't watermarked. However, the leaker made a mistake of adding the original download script which had his unique download URL to the torrent [4], so Meta can easily find them if they want to. [1]: https://boards.4channel.org/g/thread/9184826…
Re: Facebook LLAMA is being openly distributed via torrents
#103old school opensource, which is a bit surprising from meta. I wonder how they managed to square that with legal. Someone must have been very good friends with Zuck.
Aren't you a cheeky lad? Metea turned out lots of open-source database systems:
* RocksDB
* Hive
* Presto
* Cassandra
* Velox
LFP
Re: Facebook LLAMA is being openly distributed via torrents
#104It’s interesting that these models are both massively expensive to produce and self-contained to a degree that you can distribute the end product in a torrent. This has not been the case for most commercial software for the past 20 years, during the cloud era. If you could steal a dump of random Facebook source code, it would be 99% useless because it’s so closely tied to the infrastructure. There’s almost nothing yo…
> massively expensive to produce and self-contained to a degree that you can distribute the end product in a torrent. So, like movies or software
Re: Facebook LLAMA is being openly distributed via torrents
#105Re: Facebook LLAMA is being openly distributed via torrents
#106It’s interesting that these models are both massively expensive to produce and self-contained to a degree that you can distribute the end product in a torrent. This has not been the case for most commercial software for the past 20 years, during the cloud era. If you could steal a dump of random Facebook source code, it would be 99% useless because it’s so closely tied to the infrastructure. There’s almost nothing yo…
Re: Facebook LLAMA is being openly distributed via torrents
#107It seems that the leak originated from 4chan [1]. Two people in the same thread had access to the weights and verified that their hashes match [2][3] to make sure that the model isn't watermarked. However, the leaker made a mistake of adding the original download script which had his unique download URL to the torrent [4], so Meta can easily find them if they want to. [1]: https://boards.4channel.org/g/thread/9184826…
Just a warning to readers, I would not recommend clicking 4chan links while at work.
Re: Facebook LLAMA is being openly distributed via torrents
#108Re: Facebook LLAMA is being openly distributed via torrents
#109Earlier quoted context omitted.
But ML models generally can execute arbitrary code Is it the case if we're only talking about weights? I thought the rest is actually "open".
My understanding is that weights are normally stored as pickled python blobs, which means arbitrary code execution as they are unpickled.
But seriously, why not something more human readable and text-based if it's just weights?
Re: Facebook LLAMA is being openly distributed via torrents
#110It’s interesting that these models are both massively expensive to produce and self-contained to a degree that you can distribute the end product in a torrent. This has not been the case for most commercial software for the past 20 years, during the cloud era. If you could steal a dump of random Facebook source code, it would be 99% useless because it’s so closely tied to the infrastructure. There’s almost nothing yo…
Finding a sha256 hash with N leading zeros is basically arbitrarily computationally expensive but could be written on a piece of paper. I don't see training an ML model as an egregious example of concentrating compute power