Compress any LLM 3x in one click,near-lossless,in under 50 MB RAM

Xunjin

Is it possible to be used on bigger models with the same gains?

ngruhn

No idea if the paper has merit but the UI is slop: - gradient headline - random vertical scrollbar on mobile - fingernail cards

ElectricalUnion

What is the point of using a outright ancient model like DeepSeek-R1-Distill-1.5B from 2025 based on another ancient model like Qwen2.5-Math-1.5B from 2024, when a much better modern model like LFM3-1.7B exists?

mapcars

So this is very good for making local models more accessible, right?

fishfasell

I know a gen AI UI when I see one and this has all of the red flags. Hate to say it but I feel like that compromises the legitimacy of this

crunkmcgoop

I compress everything using the Sloot Digital Coding System, only use 50k for all my data!

hedora

Does this compose well with quantization? Concretely, if you start with, say, a 1.5 or 4 bit quant, then run this, you get something the size of a 0.5 - 1.5 bits without further degradation in model behavior?

velox

I have not read the whole thing, but I'm not sure I understand the benefit of this vs. using an existing e.g. 4 bit quant of the model in question

antiloper

Yeah sure I could also quantize Qwen 3.8 27B to 0.15 bits/param. Not sure what the point of this exercise is.

Aurornis

If you couldn’t tell, this is LLM generated quackery. Any claims about throwing away 2/3rds of the model weights (not just quantizing them) and maintaining “near lossless” quality are simply false. I clicked on the benchmarks page expecting some quality benchmarks but only saw speed benchmarks before I gave up and stopped scrolling. The claims don’t even make sense. It’s showing “100% GPU resident” for a model of a size that cannot fit into the GPU.

goodmythical

Would be interesting if one could verify any of these claims via the reputation of any of the research participants. As it stands, everything credits "NeuraCryption Private Limited" which was registered very recently [0][1][2] to an adress that is shared by several other recently registered businesses [scroll down on 0], and this is their very first offering according to the zenodo link they provided [3] Further, looking at the code, it appears to be a standard quanitizer... Like...there is no existing method by which one could genuinely compress a tens of GB weights file in to a third the space using only 50mb of RAM. [0] https://www.zaubacorp.com/NEURACRYPTION-PRIVATE-LIMITED-U620... [1] https://www.registerkaro.in/company/neuracryption-private-li... [2] https://www.falconebiz.com/company/NEURACRYPTION-PRIVATE-LIM... [3] https://zenodo.org/search?q=metadata.creators.person_or_org....

Roark66

Hmm, this claims "functionally lossless" compression over 3x. But where is the starting point? Bf16 of fp16? If so, how much better is it than current quantization techniques? This should be tested on a medium size dense model. For example Qwen3.8-27B. How much better is it t than quantization to 4.5 bits?