hyperlearn VS DiffSharp

Unsloth AI is designed to optimize large language model fine-tuning on modest hardware. It leverages efficient training algorithms to allow even GPUs with 24GB VRAM, like consumer-grade cards, to fine-tune models such as Llama 3 without massive resource demands or overheating risks.

I agree fully - what do you suggest then? OSS the entire code base and using AGPL3? I tried that with https://github.com/danielhanchen/hyperlearn to no avail - we couldn't even monetize it at all, so I just OSSed everything.

I listed all the research articles and methods in Hyperlearn which in the end were gobbled up by other packages.

We still have to cover life expenses and stuff sadly as a startup.

Do you have any suggestions how we could go about this? We thought maybe an actual training / inference platform, and not even OSSing any code, but we decided against this, so we OSSed some code.

Ay suggestions are welcome!

Good point - the main issue is we encountered this exact issue with our old package Hyperlearn (https://github.com/danielhanchen/hyperlearn).

I OSSed all the code to the community - I'm actually an extremely open person and I love contributing to the OSS community.

The issue was the package got gobbled up by other startups and big tech companies with no credit - I didn't want any cash from it, but it stung and hurt really bad hearing other startups and companies claim it was them who made it faster, whilst it was actually my work. It hurt really bad - as an OSS person, I don't want money, but just some recognition for the work.

I also used to accept and help everyone with their writing their startup's software, but I never got paid or even any thanks - sadly I didn't expect the world to be such a hostile place.

So after a sad awakening, I decided with my brother instead of OSSing everything, we would first OSS something which is still very good - 5X faster training is already very reasonable.

I'm all open to other suggestions on how we should approach this though! There are no evil intentions - in fact I insisted we OSS EVERYTHING even the 30x faster algos, but after a level headed discussion with my brother - we still have to pay life expenses no?

If you have other ways we can go about this - I'm all ears!! We're literally making stuff up as we go along!

Hello everyone!! It's been a while!! Years back I released Hyperlearn https://github.com/danielhanchen/hyperlearn. It has 1.2K Github stars, where I made tonnes of algos faster:

More expressive nature of F# means you don't have that many files. C# is luckily and finally moving into that direction too. There was no reason for "one file per class" policy anyway, but it was still widely adopted historically.

Here's an example of a worst-case scenario (GUI frameworks have notoriously huge amount of code): https://github.com/fsprojects/Avalonia.FuncUI/blob/master/sr...

But realistically an average project would look closer to this instead: https://github.com/DiffSharp/DiffSharp/blob/dev/src/DiffShar...

Once you have enough files, it might be a good idea to factor out separate concerns into different projects.

Yes. You can use TensorFlow.NET or DiffSharp.