dietlibc VS sharegpt

- Dietlibc: https://www.fefe.de/dietlibc/

The commercial success of a product totally depends on the business model you come up with, whatever be its opensource (or not) license.

Corporates have a vested interest in promoting the propaganda that only a non-xGPL opensource license can be commercialised successfully simply because they cannot freely steal the source code of a competing xGPL licensed software.

The real value of an FSF license, like the AGPL, is that it was designed to protect the copyright holders, and its users, "right to repair". And thus, it cannot be closed source by anyone (apart from the original copyright holders) once released under the said license (even if future versions are closed source, the old version under xPL remain opensource perpetually). Other open source license (that are less stringent) are prioritised to increase developer contribution. Source code under such license can be closed-source even from the original copyright holder.

But again, commercial success totally depends on the business model you come up with, irrespective of your license. The right license and the right business model will empower each other. Or cripple your business.

Stefan Tomanek - Creator of dietlibc, a libc optimized for small size - https://github.com/stefan-tomanek (The dietlibc project itself doesn't have an official GitHub repository, but you can find it at https://www.fefe.de/dietlibc/)

There are definitely some nice alternatives to glibc out there. He mentions Cosmopolitan Libc. I've used musl, uclibc, and dietlibc/libowfat in the past.

Maybe you should include the math part of a libc statically with your code. glibc is one option, or dietlibc if you want it to be as small as possible.

I agree that the implementation of printf is complex, but the interface is not. Hence calling it should not introduce bloat. Glibc adds a bunch of constructors and tables and such, whereas linking with dietlibc will probably lead to a smaller executable.

Vicuna is another instruction-focused LLM rooted in LLaMA, developed by researchers from UC Berkeley, Carnegie Mellon University, Stanford, and UC San Diego. They adapted Alpaca’s training code and incorporated 70,000 examples from ShareGPT, a platform for sharing ChatGPT interactions.

We can say that a 13B model per language is reasonable. Then it means we need to create a democratic way for teaching coding by examples and solutions and algorithms, that we create, curate and use open-source. Much like sharegpt.com but for coding tasks, solutions ways of thinking. We should be wary of 'enforcing' principles rather showing different approaches, as all approaches can have advantages and disadvantages.

You can see the url in the comment, https://sharegpt.com and if you go there it gives you the option for installing the chrome extension, after that it shouldn’t be hard to use it

ShareGPT - Share your prompts and your entire conversations

Or used https://sharegpt.com

you can gets lots of ideas here > https://sharegpt.com/ (180,000+ prompts)

I would love to read the whole conversation: Check out this cool little GPT sharing extension: https://sharegpt.com - that way the code snippets can be copied easily

> Cool, that’s really the only point I’m making.

To be clear, I'm saying that I don't know if they are, not that we know that it's not the same.

It's not at all clear that humans do much more than "that basic token sequence prediction" for our reasoning itself. There are glaringly obvious auxiliary differences, such as memory, but we just don't know how human reasoning works, so writing off a predictive mechanism like this is just as unjustified as assuming it's the same. It's highly likely there are differences, but whether they are significant remains to be seen.

> Not necessarily scaling limitations fundamental to the architecture as such, but limitations in our ability to develop sufficiently well developed training texts and strategies across so many problem domains.

I think there are several big issues with that thinking. One is that this constraint is an issue now in large part because GPT doesn't have "memory" or an ability to continue learning. Those two need to be overcome to let it truly scale, but once they are, the game fundamentally changes.

The second is that we're already at a stage where using LLMs to generate and validate training data works well for a whole lot of domains, and that will accelerate, especially when coupled with "plugins" and the ability to capture interactions with real-life users [1]

E.g. a large part of human ability to do maths with any kind of efficiency comes down to rote repetition and generating large sets of simple quizzes for such areas is near trivial if you combine an LLM at tools for it to validate its answers. And unlike with humans where we have to do this effort for billions of humans, once you have an ability to let these models continue learning you make this investment in training once (or once per major LLM effort).

A third is that GPT hasn't even scratched the surface in what is available in digital collections alone. E.g. GPT3 was trained on "only" about 200 million Norwegian words (I don't have data for GPT4). Norwegian is a tiny language - this was 0.1% of GPT3's total corpus. But the Norwegian National Library has 8.5m items, which includes something like 10-20 billion words in books alone, and many tens of billions more in newspapers, magazines and other data. That's one tiny language. We're many generations of LLM's away from even approaching exhausting the already available digital collections alone, and that's before we look at having the models trained on that data generate and judge training data.

[1] https://sharegpt.com/

of that conversation. Perhaps something like shareGPT[1] can help?

[1] https://sharegpt.com