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Show HN: Overfitted a 900KB Transformer to Compress a 100MB CSV into 7MB
I built an experiment that uses an overfitted transformer and arithmetic coding to compress individual files.
Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output.
On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte).
It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my AMD 7800XT).
Checkout the repo - https://github.com/samyak112/pym-particles https://github.com/samyak112/pym-particles
- totetsu 3mo agoif first bit is 1 then decompress to a picture of my cat, else its just Huffman
- 7373737373 3mo agoWhat does it compress the full 1GB file to? http://prize.hutter1.net/ http://prize.hutter1.net/
- spidy__ 3mo agoI tried it on a enwik9 100 mb slice and was able to compress it to 20 mb + 900kb transformer so 21mb. I know the top submission was able to get it to 13 mb. Still trying some ideas to get better compression.
- gravypod 3mo agoSince you know the size of the file beforehand you may be able to overfit some kind of text diffusion model instead of a transformer? May allow you to partially correct the model output using some other method and then fill in the blanks that were wrong from previous generations.
- spidy__ 3mo agoOh, sounds interesting. I hadn't considered using a diffusion model for this. My current approach generates the document byte by byte with an autoregressive transformer, so I'm curious how a diffusion model would improve memorization or reconstruction quality. Can you point me to something that i can read? I really wanna try this approach , diffusion model does sounds interesting for compression.
- atiedebee 3mo agoWhich slice? The large text compression benchmark uses enwik8 for a "smaller" input that is easily reproducible. The predictability of enwik9 can vary significantly depending on where in the file you are, as shown by Matt Mahoney https://www.mattmahoney.net/dc/textdata.html https://www.mattmahoney.net/dc/textdata.html
- purple-leafy 3mo agoThanks for the link!
- cellular 3mo agoMaybe everyone should compress the 1st 100MB worth of digits of pi, for an apples-to-apples comparison? Edit: oh wait that's too easy. Need to generate /publish random digits so everyone can use it.
- saulpw 3mo agorandom digits aren't compressible though?
- SV_BubbleTime 3mo agoRandom digits are compressible though. Random data does not mean it does not match a pattern in your dictionary for example.
- gnabgib 3mo agoNo.. they're not. Do you understand random (the apparent or actual lack of definite patterns or predictability[0]) or compression (reduces bits by identifying and eliminating statistical redundancy[1])? [0]: https://en.wikipedia.org/wiki/Randomness https://en.wikipedia.org/wiki/Randomness [1]: https://en.wikipedia.org/wiki/Data_compression https://en.wikipedia.org/wiki/Data_compression
- deleted 3mo ago[deleted]
- thin_carapace 3mo agoby this definition, a random dataset could apparently present no patterns, while presenting non apparent patterns.
- ufocia 3mo agoSounds like presenting no patterns, apparently or otherwise, would be a pattern in itself.
- xunevega 3mo ago[flagged]
- purple-leafy 3mo agoThat’s so awesome! I want to try something similar. I’ve been going crazy with compression work. I reckon I can beat that prize link
- spidy__ 3mo agoReallly?? So have you published something so far? Can i read something? Sounds like you got some interesting ideas.
- purple-leafy 3mo agoI will be showcasing something on hackernews soon! Basically I found a way to “compress” a multiplayer game state from ~100KB+ to ~1KB But it’s only for the game I’m building and it’s not pure compression work, I had to do some tricky things
- purple-leafy 3mo agoAnd just for comparison, my absolute best compression method managed to get down to 10s of KB, but the real unlock got to the ~1KB figures. Note these numbers are ALL post-compression numbers. This is not raw data vs compressed data. The ~100KB figure IS POST COMPRESSION. For context these numbers are for a grid based game where players can perform 4 actions per second, and the numbers I’m sharing are for 30 minutes of gameplay with anywhere from 2-1024+ players (human players) playing simultaneously So if you do the math, my compression feat is effectively ~99% compression on naive best case. And if you compare it to the raw data, it’s closing in on an even higher number than that I haven’t done the math but the raw data is another factor of 10 greater than ~100KB so the “compression” versus raw data is ~99.9% It sounds absolutely bullshit I know :D But I will be posting a blog post soon once I release the game. I do compression in quotes because it’s not a pure compression feat, the 99%+ feat is effectively being clever about what actually requires compression to achieve the same outcome
- spidy__ 3mo agoSounds interesting man, soo am a bit confused maybe but can you run this on enwik9?
- tae0086 3mo agoNeat approach. Since the 900KB model ships with the compressed file, is there a file size below which the model overhead just eats the gains? Curious where the crossover is.
- spidy__ 3mo agoFor the model overhead to become significant enough to eat into the gains, the file size would need to be fairly small, right? I assumed nobody would use this for compressing anything below 100 MB. I tested with 100 MB files because anything larger takes a long time to evaluate. The actual target was at least 1 GB, and in that case I would use a 100 MB model (Shannon entropy rules). I also tried it on a 100 MB Photoshop file and was able to compress it down to 45 MB, whereas ZIP could only get it down to 60 MB. So yeah still not losing gains.
- keynha 3mo ago[dead]
- roshiya 3mo ago[flagged]
- jessedaniel 3mo ago[dead]
- userbinator 3mo agoFabrice Bellard may have been the first to do this, 7 years ago: https://news.ycombinator.com/item?id=27244004 https://news.ycombinator.com/item?id=27244004
- spidy__ 3mo agoYeah yeah, I just found the idea kinda interesting so wanted to implement it
- touisteur 3mo agoKeep exploring and writing (please ?) ! Love seeing people explore ... even after Fabrice Bellard had a go at it.
- spidy__ 3mo agoOfcourse, am doing this just because I enjoy it. While we are on the discussion I have mentioned a question at the end of the discussion around an assumption am trying, can you please check it out and see if you have any suggestions? Would be awesome if someone can validate or help.
- pentaphobe 3mo agoHope this isnt too spicy a take, but i find it a bit disingenuous to use language that implies invention (and with no mention or citation of previous work), only to switch to dismissive language when someone notes a predecessor who you've apparently already heard of
- SubiculumCode 3mo agoWhat 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?
- gwern 3mo agoEnsembling is not compute or parameter-efficient, so compression per se is a terrible application. (This is related to why people train ever larger LLMs like 1 10t-parameter LLM, rather than 100 GPT-3-scale LLMs.)
- SubiculumCode 3mo agoYeah.
- fsiefken 3mo agoconventional algorithms https://www.mattmahoney.net/dc/text.html https://www.mattmahoney.net/dc/text.html
- dmagog 3mo ago[flagged]
- wildstrawberry 3mo agoThree 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)?
- spidy__ 3mo ago1. I wrote the content as what i want to mention in the documentation and just used AI to polish it so that its easy to understand, is it hard to understand the documentation right now? 2. Have added the link for downloading both the enwik9 slice and the nyc dataset. Apologies I forgot to add it. You can get it from here - https://github.com/samyak112/pym-particles/blob/main/README.md#benchmark-results https://github.com/samyak112/pym-particles/blob/main/README.... 3. Other than zip i tested it with zstd19, and now that you mentioned LZMA2 and BZIP2 I got results on enwik9 100mb slice as zstd - 28mb bzip2 - 30mb lzma2 - 26mb I will mention these and results from ZPAQ in the readme for both files, thanks for pointing them out!!! But the thing is this neural compression approach cant be used right now, as it takes hours to compress and de compress a 100mb file so not really usable and more of a fun project.
- IncreasePosts 3mo agoThese algorithms let you specify a compression level - please note in the docs which you used. The window size can also be adjusted. Zstd might default to 4, which is "goodish compression but fast"
- spidy__ 3mo agoI tried with zstd 19
- wildstrawberry 3mo ago
- jocelyner 3mo ago[dead]
- rtpg 3mo agoI've had this idea of building a codec that would similarly overfit to specific images. But the codec itself would not be a fixed size transformer... instead you could just mess around with the sizing to get better quality/smaller size. So the codec would be something like: <header describing image size + transformer layer shape> <transformer data itself> I've seen experiments where people have a "fixed" pipeline but I think having something more dynamic would work quite well.
- dvt 3mo agoLikely doable with metaparameter tuning (used to work on a team with data scientists that were routinely doing this in various situations). Seems like a cool idea.
- IncreasePosts 3mo agoIsnt this what auto encoders are for?
- jxmorris12 3mo agoLo and behold, a nice arithmetic coding implementation that wasn't written by an LLM! A sight for sore eyes – a treat, even. Looks like it was written by someone else though. Check it out: https://github.com/samyak112/pym-particles/blob/main/arithmetic_coder.py https://github.com/samyak112/pym-particles/blob/main/arithme...
- spidy__ 3mo agoOhh yeah , I took it from Project Nayuki as mentioned in the file as well, i tried to pip install it but there were some issues so just took the file and kept the copy right as it is. Its not an issue is it? I am not sure.
- jmspring 3mo agoThe 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).
- purple-leafy 3mo agoDumb question: can you train a model to predict the next byte of ANOTHER MODEL So apply this same logic to compressing a bigger model within a smaller model I know this is absolutely regarded, but humour me please
- anyg 3mo agoNot dumb at all. It's a whole field of active research - Speculative Decoding. A recent paper goes one level deeper with Speculative Speculative Decoding - https://arxiv.org/abs/2603.03251 https://arxiv.org/abs/2603.03251
- purple-leafy 3mo agoOh man awesome! I’m so S-M-R-T Compression is such an interesting field
- r-w 3mo agoIs model distillation also related?
- userbinator 3mo agoIf there's any redundancy in the model that can be compressed (parallel to how RLE is used to compress the static Huffman tree in FLATE) that's possible, but it's not necessary if the model is being trained on the input dynamically, like what Bellard's NNCP does.
- test1072 3mo agoSo has anyone tried to you know for example keep constant weights base model and just transmit the data, might be better compression
- spidy__ 3mo agoI might be confused by the question, but I overfit the model on a single file and then transport the model along with the arithmetic coding file. There have been ideas where you generalize a model (constant weights) and then pass the arithmetic coding file along with it. So that way you only pass the arithmetic coding file. BUT my model size is just 900KB (for 100mb file atleast) so it is negligible
- whacked_new 3mo agoSomewhat related is stavros's method to compress 500KB to something like 50 bytes https://www.stavros.io/posts/compressing-images-with-stable-diffusion/ 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
- isoprophlex 3mo ago> There are some minor kinks that need to be worked out, such as the fact that each image takes around a day to generate on mobile, but this is more than acceptable in certain domains. Website visitors, for example, are well-accustomed to such loading times, and would barely notice any difference. Just amazing, wow
- userbinator 3mo agoMaybe it would even work for video? While clearly satirical, it's definitely quite thought-provoking from various angles including the basis of information, representation of data, and even copyright. It's like watching a movie, writing a book based on it, and then making another movie based on that book.
- spidy__ 3mo ago[dead]
- VorticonCmdr 3mo agoGreat 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.
- spidy__ 3mo agoI mean neural compressors provide great compression, BUTT the issue is they are really slow like in my project it takes around 45 minutes for de compression of 100 mb so I doubt if it would be useful, also using a transformer in user's browser sounds like a heavy task.
- fennecbutt 3mo agoGiven it's an overfitted transformer isn't is still terribly inaccurate/is liable to add random bytes here and there?
- spidy__ 3mo agoIf I were only using a transformer that would have been true, but we use arithmetic coding alongside our transformer to fix those mistakes (layman terms). You can read about arithmetic coding, its a pretty cool topic.
- westurner 3mo agoCompression, Predictive modeling, or Complexity? TabPFN v2 is probably less overfit: https://news.ycombinator.com/item?id=42647343 https://news.ycombinator.com/item?id=42647343
- freakynit 3mo agoRecently 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 https://www.tensordyne.ai/silicon-and-math
- ledmirage 3mo agoso the trained model only work for the specific csv file? retrain is required for another file?
- spidy__ 3mo agoyep!!
- ledmirage 3mo agoso trained model only works for that specific file, and retraining is required for a new file? maybe this is could be good for crypto instead of compression, the question is how do you share the model before hand to allow destination to decrypt it, if you use conventional stuff like rsa, then the question would be why don't you use for the actual message directly.
- spidy__ 3mo agoUmmm I don't have a lot of idea about crypto but in compression the idea was that you just share the model along with the metadata file to the other user and they de compress it, not sure how things will differ in crypto