5 ms·

Yeah, makes sense. Good observations regarding the benchmark vs. vibes in general, and I didn't know / made the connection between the lead of phi models going

Yeah, makes sense. Good observations regarding the benchmark vs. vibes in general, and I didn't know / made the connection between the lead of phi models going to oAI and gpt-oss. Could very well be a similar exercise + their "new" prompt level adherence (system > developer > user). In all the traces I've seen of refusals the model "quotes" the policy quite religiously. Similar thing was announced for gpt5. I think the mention of the "horny people" is warranted, they are an important part of the open models (and first to explore the idea of "identities / personas" for LLMs, AFAIK). Plenty of fine-tuning bits of know-how trickled from there to the "common knowledge". There's a thing that I would have liked to be explored, perhaps. The idea that companies might actually want what -oss offers. While the local llm communities might want freedom and a horny assistant, businesses absolutely do not want that. And in fact they spend a lot of effort into implementing (sometimes less than ideal) guardrails, to keep the models on track. For very easy usecases like support chatbots and the like, businesses will always prefer something that errs on the side of less than useful but "safe", rather than have the bot start going crazy with sex/slurs/insults/etc. I do have a problem with this section though: > Really open weight, not open source, because the weights are freely available but the training data and code is not. This is factually incorrect. The -oss models are by definition open source. Apache2.0 is open source (I think even the purists agree with this). The requirement of sharing "training data and code" is absolutely not a prerequisite for being open source (and historically it was never required. The craze surrounding LLMs suddenly made this a thing. It's not). Here's the definition of source in "open source": > "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. Well, for LLMs the weights are the "preffered form of making modifications". The labs themselves modify models the same as you are allowed to by the license! They might use more advanced tools, or better datasets, but in the end the definition still holds. And you get all the other stuff, like the right to modify, re-release, etc. I'd really wish people would stop proliferating this open weight nonsense. Models released under open source licenses are open source. gpt-oss, qwens and mistrals (apache2.0), deepseeks(MIT), etc. Models released under non open source licenses also exist, and they're not open source because the licenses under which they're released aren't. LLamas, gemmas, etc.

jononor 1y ago
No the preferred way of making modifications is the weights _together_ with training (or fine tuning) scripts, and the entire evaluation pipeline to measure performance. And the data required to support all of this. When someone joins your data science team your would give them all this code and data. Not just the weights and say - the weights are the source, modify that to improve the model, I look forward to see your MR next week. EDIT: Heck, sometimes the way to make improvements (modifications) is just to improve the data, and not touch the training code at all. It is often one of the most powerful ways. You still need training code though, and evaluation to measure the impact.
- NitpickLawyer 1y ago
  The license gives you the right to modify the weights, how you do the modification is up to you. The rest is in the realm of IP, know-how, etc. Apples and oranges.
  - imiric 1y ago
    Having the right to modify one part of the product is not the same as having the right to modify the entire product. Labeling such projects as open source in the full spirit of the definition is disingenuous. This is similar to the approach taken by some video game studios: release the source code under a permissive license, but not the game assets. Which is better than a proprietary license, but it still presents a hurdle for the final product to be built from source. The open weights approach is much more user hostile, however. Proprietary game assets can at least be purchased, and the final product can be built. With open weights, this is not possible. Nobody can realistically build the same model or similar models from weights alone. They can use the weights and self-host the prebuilt model, but not create revisions of it, which is the whole point of open source. Weights are essentially the bytecode of language models. Sure, you can run and modify it with the right tools, but without the tools used to create it in the first place, the project is not much more useful than publishing binaries.
- wizzwizz4 1y ago
  You also need the training data, so you can ensure you're not benchmarking on the training set, fine-tuning on the training set (overfitting with extra steps), or otherwise breaking things.

jononor 1y ago

No the preferred way of making modifications is the weights _together_ with training (or fine tuning) scripts, and the entire evaluation pipeline to measure performance. And the data required to support all of this. When someone joins your data science team your would give them all this code and data. Not just the weights and say - the weights are the source, modify that to improve the model, I look forward to see your MR next week. EDIT: Heck, sometimes the way to make improvements (modifications) is just to improve the data, and not touch the training code at all. It is often one of the most powerful ways. You still need training code though, and evaluation to measure the impact.

NitpickLawyer 1y ago
The license gives you the right to modify the weights, how you do the modification is up to you. The rest is in the realm of IP, know-how, etc. Apples and oranges.
- imiric 1y ago
  Having the right to modify one part of the product is not the same as having the right to modify the entire product. Labeling such projects as open source in the full spirit of the definition is disingenuous. This is similar to the approach taken by some video game studios: release the source code under a permissive license, but not the game assets. Which is better than a proprietary license, but it still presents a hurdle for the final product to be built from source. The open weights approach is much more user hostile, however. Proprietary game assets can at least be purchased, and the final product can be built. With open weights, this is not possible. Nobody can realistically build the same model or similar models from weights alone. They can use the weights and self-host the prebuilt model, but not create revisions of it, which is the whole point of open source. Weights are essentially the bytecode of language models. Sure, you can run and modify it with the right tools, but without the tools used to create it in the first place, the project is not much more useful than publishing binaries.
wizzwizz4 1y ago
You also need the training data, so you can ensure you're not benchmarking on the training set, fine-tuning on the training set (overfitting with extra steps), or otherwise breaking things.