81–90 of 117 posts
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The GPL describes the source as the "preferred form for modification".
The model weights ARE the preferred form for modification
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But one of the four freedoms is being able to modify/tweek things, including the model. If all you have is the model weights, then you can't easily tweak the model. The model weights is hardly the preferred form for making changes to update the model. The equivalent would be someone which gives you only the binary to Libreoffice. That's perfectly fine for editing documents and spreadsheets, but suppose you want to fi…
>suppose you find that the model has a bias in terms of labeling African Americans as criminals; or women as lousy computer programmers. If all you have is the model weights of the trained model, how easily can you fix the model? That's textbook fine-tuning and is basically trivial. Adding another layer and training that is many orders of magnitude more efficient than retraining the whole model and works ~exactly as…
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You do not need the original dataset to train the model on an additional dataset Maybe I misunderstood your original question. To be clear, the process of modifying a trained model does not require the presence of the original data. You said “deleted” which perhaps I misinterpreted. You’re not “instantiating a new model from scratch” when you modify it. You’re continuing to train it where it left off.
What if you want to start with a subset of the original data? Like you've trained a model, and then later said "You know, this new data we're adding is great, but maybe pulling all those comments from 4chan earlier was a mistake," wouldn't that require starting fresh with access to the actual data?
The general idea is to get as good of a mastery of language as possible, generally, and then fine tune to specialize on tasks
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No it’s not. You have everything you need to modify the models to your own liking. You can explore how it works. This analogy is bad. Models are unlike code bases in this way.
You can do the same with binaries. Can modify those all you want. Models are the compiler + makefiles. Dataset is the code.
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No it’s not. You have everything you need to modify the models to your own liking. You can explore how it works. This analogy is bad. Models are unlike code bases in this way.
> You have everything you need to modify the models to your own liking. What if I wanted to train it using only half of its training set? If the inputs that were used to generate the set of released weights are not available I can’t do that. I have a set of weights and the model structure but without the training dataset I have no way of doing that. To riff on the parent post, I have: Source + Compiler => Binaries Fo…
You can fine tune into a different model architecture.
You’re right on not being able to retrain the model from scratch on half its data without that data but that’s likely pointless.
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The model weights ARE the preferred form for modification
As long-time a 'practitioner' of machine learning models I strongly disagree, the preferred form for model modification is by retraining the model with a tweak to the parameters or the training algorithm or the model structure or data selection or length of training. You can get some effects by fine tuning, and in that case it may be preferable as it's cheaper, but in general if I want to have a different or better m…
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The model weights ARE the preferred form for modification
Yeah, that's why data scientists are out there editing the weights rather than cleaning up datasets and rerunning training with different settings.
> Imagine if Linux published only a binary without the codebase. Or published the codebase without the compiler used to make the binary. This is where we are today. This was such a helpful way to frame the problem! Something felt off about the "open source models" out there; this highlights the problem incredibly well.
A slightly offtopic complaint, but too often I have seen tutorials for open source stuff (coughopenglcough) where they don't provide the proper commands to compile and link everything required to build it. Figuring it out makes the "getting started" portion even more tedious.
I would argue that while technically correct, it is not what most people really care. What they care about are the following: 1. Can I download it? 2. Can I run it on my hardware? 3. Can I modify it? 4. Can I share my modifications with others? If those questions are in the affirmative, then I think most people consider it open enough, and it is a huge step for freedom compared to the models such as OpenAI.
Similarly, if I have a LLM model with a permissive license, I technically could fine-tune it to modify its behavior, but for some kinds of modifications I'd really rather re-run (parts of) the training differently.