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InitialsDiceBearhttps://github.com/dicebear/dicebearhttps://creativecommons.org/publicdomain/zero/1.0/„Initials” (https://github.com/dicebear/dicebear) by „DiceBear”, licensed under „CC0 1.0” (https://creativecommons.org/publicdomain/zero/1.0/)SW
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2 yr. ago

  • In theory, if you have the inputs, you have reproducible outputs, modulo perhaps some small deviations due to non-deterministic parallelism. But if those effects are large enough to make your model perform differently you already have big issues, no different than if a piece of software performs differently each time it is compiled.

  • The analogy works perfectly well. It does not matter how common it is. Pstching binaries is very hard compared to e.g. LoRA. But it is still essentially the same thing, making a derivative work by modifying parts of the original.

  • I don't see your point? What is the "source" for Mona Lisa I would use? For LLMs I could reproduce them given the original inputs.

    Creating those inputs may be an art, but so could any piece of code. No one claims that code being elegant disqualifies it from being open source.

  • How is that different then e.g. patching a closed-sourced binary? There are plenty of community patches to old games to e.g. make them work on newer hardware. Architectural independence seems irrelevant, it's no different than e.g Java bytecode.

  • It would depend on the format what is counted as source, and what isn't.

    You can create a picture by hand, using no input data.

    I challenge you to do the same for model weights. If you truly just sit down and type away numbers in a file, then yes, the model would have no further source. But that is not something that can be done in practice.

  • You forget a piece: "Given these observations, these objectives, and this bit of sound reasoning, ..."

    Without objectives, no amount of reasoning will tell you what to do. Who sets the objectives?

  • You are using the LLM to check it's own response here. The point is that the second LLM would have hard-coded "instructions", and not take instructions from the user provided input.

    In fact, the second LLM does not need to be instruction fine-tuned at all. You can jzst fine-tune it specifically for the tssk of answering that specific question.

  • They actually did not. They clearly state (at least in the text posted by the OP) that you are not allowed to license under a version or derivative of the GPL if it would end up copyleft. The main condition is that it is licensed under a version of the GPL.

    (To be clear, I'm talking about the second quote, about combining)

  • By input coverage I just mean that you test with different inputs. It doesn't matter if you have 100% code coverage, if you only tested with the number "1", and the code crashes if you give it a negative number.

    If you can prove that your code can't crash (e.g. using types), it's a lot more valuable then spending time thinking about potentially problematic inputs and writing individual tests for them (there ate tools thst help with this, but they are not perfect).