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- Hacker News
- Great work. Just Yesterday I thought about LLMzip and asked myself if this is something which could vastly improve HTML compression when done at Google scale and shipped with browsers. I haven't done any research though.by VorticonCmdr
- Recently read about some upcoming GPU chip vendor that speeds up multiply-and-add (the core of [pretty much any neural network, including LLM's) oepration by transforming them to log scale where the multiplication changes to addition.
There issue was the size of such log tables that would be needed, and hence, they settled for some trade-off.
My question is: can the expensive log-domain addition / correction function be implemented as fixed hardware lookup tables or approximate units?
Update: found the vendor: https://www.tensordyne.ai/silicon-and-math
by freakynit - Three questions:
1. How much was AI used to generate documentation for this project?
2. The 100MB CSV data sources are not provided in the repo so it doesn't seem possible to reproduce your results. The enwik9 dataset says it is a "slice" of the larger data set, and there are many NYC taxi trip record datasets that exist. Can you provide the datasets used to generate your results?
3. I am surprised to see performance comparisons only between your transformer and WinZIP. What were your results when comparing your transformer to more modern approaches like LZMA2 (level 9), BZIP2 and ZPAQ (max effort)?
- Somewhat related is stavros's method to compress 500KB to something like 50 bytes https://www.stavros.io/posts/compressing-images-with-stable-...
main drawback is that it's not lossless ;-)
but this is great. I hope this actually becomes a format that wraps the weights and transformer module (maybe this can also be NAS-optimized too?). Maybe it would even work for video?
It's like calling gzip but instead of compression level you choose kolmogorov complexity level
by whacked_new - The model is the important part, a huffman code or adaptive huffman or other sorts of encoders would be much better on a dataset based on the model. You need the model to also decode. And on a dataset of sufficient size, embedding the model and the benefit of it's memorization of the file can be offset.
A non-general compression algorithm (model - I don't mean a distinct llm, but "modeling data") targeted at a specific dataset will always do better than a general algorithm.
The reason I mentioned the "encoder" doesn't matter - arithmetic coding, for the data it is presented, will beat huffman/adaptive huffman every day, but it's the model that is where the real "compression" comes into play.
I've implemented enough "coders" over the years, including arithmetic for both commercial and research purposes (was a student of Glen Langdon).
by jmspring - What does it compress the full 1GB file to? http://prize.hutter1.net/by 7373737373
- What do those compress to with conventional approaches? For comparison.
I am curious. A classic machine learning ensemble approach is to overfit a collection of small models then bag them (e.g. voting) allowing the models to generalize.
I'm sure someone's tried to overfit a bunch of transformers for compression like this, then bag them to see how well it does?
- Fabrice Bellard may have been the first to do this, 7 years ago: https://news.ycombinator.com/item?id=27244004by userbinator