When you get so far as to abstracting every step to loading a one-liner from huggingface, including the downloading of a prepared dataset with no example of doing the same on custom local dataset, you've abstracted too far to be useful for anyone other than the first user.
However, there is a lot of documentation on the site to help guide users. This documentation page shows you can load in data via local datasets as well. For example, JSON, CSV, text files, a local HF Dataset folder, or even from a Python `dict` or `list`:
https://datadreamer.dev/docs/latest/datadreamer.steps.html#t...
We'll definitely keep improving documentation, guides, and examples. We have a lot of it already, and more to come! This has only recently become a public project :)
If anyone has any questions on using it, feel free to email me directly (email on the site and HN bio) for help in the meantime.
I would not have guessed that the base input data processing would have been filed under 'steps'. But now I kinda see how you are working, but I admit I'm not the target audience.
If you want this to really take off for people outside of a very, very specific class of researchers... setup an example on your landing page that calls to a local JSON of user prompts/answers/rejects finetuning a llama model with your datadreamer.steps.JSONDataSource into the loader. Or, a txt file with the system/user/assistant prompts tagged and examples given. Yes, your 'lines of code' for your frontpage example may grow a bit!
Maybe there are a lot of 'ML researchers' that are used to the type of super-abstract OOP API, load-it-from-huggingface-scheme-people you are targeting but also know that there are a ton that aren't.
you can look at the samples. Mostly its questions and accepted/rejected answers.
Title should be instead “Library for low-code RLHF in python”
Unless your research hypothesis is specifically around improving or changing RLHF, it's unlikely you should be implementing it from scratch. Abstractions are useful for a reason. The library is quite configurable to let you tune any knobs you would want.
As far as I understand, what the training loop is supposed to be doing is pretty static and you don't need to understand most of it in order to "do ML", but at the same time it's full of complicated things to get right (which would be much easier to understand when controlled through well defined parameters instead of mixing boilerplate and config).
They're saying why does it matter if it's 50 vs 60 or even 100. It's a wrapper, which should be less lines. That's the whole point. Abstracting things even further and making assumptions.
Of course you can use them. Of course you can remove them after and use the underlying code. But the LOC shouldn't be the important part of it
Kind of like everybody knows the pop-science around e = mc^2 but most are completely oblivious that it takes a bunch of whiteboards to derive it and what all that actually means.
No pithy formula no way for the actual ideas to spread to the mainstream for you to somehow hear about it.
That's like saying, I can solve any problem in 2 lines of code. I'll publish a library for it first, then:
import foo; foo.do_the_thing()
Magic!
DataDreamer is an open source Python package with a nice API from the University of Pennsylvania that does all this that we’re actively developing. Will be here to answer questions.
However, we also tried to simplify the API and have sensible defaults to make it usable for anyone / make ML research code cleaner :)
Algined models are dumber, treat everyone like they're stupid immature idiots who can't handle words and they're a wannabe moral authority.
Say for simple conversation usecases (eg customer support for a specific product, interactive fiction, things like that without deep technical knowledge).
I was also wondering if it’s possible to do such RLHF for SD running locally.
https://datadreamer.dev/docs/latest/pages/get_started/quick_...
It would be nice. But I’ve seen too many nice ideas completely fall apart in practice to accept this without some justification. Even if there are papers on the topic, and those papers show that the models rank highly according to some eval metrics, the only metric that truly matters is "the user likes the model and it solves their problems."
By the way, on a separate topic, the 90/10 dataset split that you do in all of your examples turns out to be fraught with peril in practice. The issue is that the validation dataset quality turns out to be crucial, and randomly yeeting 10% of your data into the validation dataset without manual review is a recipe for problems.
to actually do something from scratch or using the author's code requires adopting something esoteric just for this purpose. for these scenarios it is nice to appreciate hf and their abstraction. but the reinventing the wheel situation is very frustrating to work with.
if you want to go beyond the demo, you have to deal with this painful reality. i hope there is more progress on this rather than making stacks of api.
Theoretically the hard part is collecting the examples with rejections etc.
