> We have opensourced the models in Hugging Face and ModelScope to you
We are unfortunately now in a place where this falsehood has travelled the world while the truth is probably still half-asleep in its underwear.
It is a shame that people who are working on what is probably the pinnacle of computing can so blatantly disregard the real meaning.
Imagine if Microsoft starting announcing everywhere that Windows, because all its EXE and DLLs are right there for you to see, is actually open-source!
I suppose all we can do now is to keep asking "is it open-source or like true open-source?".
The model seems to be Apache 2.0 yet the training data remains private, so even if you had the resources, knowledge and ability to train it yourself, you wouldn't be able to.
So no, Qwen 2 isn't open source, but they happen to release the models publicly. Guess "pseudo-open source" might make sense as a label.
I agree, I'm not a super fan of people/organizations using "open source" as a marketing term which seems popular in the ML field right now.
The "source", analogous to the source code for a program, should include the training data. In this case that isn't open. The resulting weights are open, insofar as they can be redistributed and fine-tuned and so on.
I like open-everything as much as the next person, but I don’t really agree with this position. The source code to run the model is open, as are the weights of the thing you’re running. I think it’s fair to say that they have open-sourced an LLM inference system. This isn’t traditional software, and it’s not clear how the term “open source” should be defined.
To stretch the analogy a different way, it could have been argued that PyTorch isn’t “open source” because the repo doesn’t include the private notes, sketches and communications of the team that developed it. How could someone reproduce the source code for themselves without access to the inputs that went into designing it?
Of course, we don’t define “open source” in that way for source code. But we could have.
You can't build the model from source with code. That's because the input data is an essential source of the model.
> I think it’s fair to say that they have open-sourced an LLM inference system.
Maybe they have. That's separate from the model though, and a lot of people use different, more standardized inference systems (Ollama, vLLM, etc).
> it could have been argued that PyTorch isn’t “open source” because the repo doesn’t include the private notes, sketches and communications of the team that developed it.
Those aren't inputs used to build a runnable package of PyTorch. The source of some binary is the human readable and editable input used to produce the binary. Notes and communications are human readable input to the already human readable code; it's therefore not a source for binaries build from the code.
LLM Weights are not human readable nor human editable. They are machine readable (through inferencing) and machine editable (through fine tuning). If that counts as open source, then so is any binary executable since patchelf and co exist.
While "open weights" is probably the most appropriate terminology, and I do think a lot of AI companies have been abusing the term "open source", especially when releasing with restrictive community licenses (weights available w/ lots of restrictions is very different than weights available under Apache 2.0/MIT), I think the binary talking point that's been getting popular lately is actually also pretty misleading.
Having open weights is a lot more useful than an exe/dll, especially with base models, as the weights are a lot more malleable. You can do continued pre-training or fine-tuning of models, basically being able to build on millions of dollars of free compute with as little as a few hours on a single gaming GPU. With the weights, you also get a lot more visibility into the model as well (which is getting more and more useful as more advanced interpretability research/tools become available). We've seen other white-box only techniques in the past, but the recent orthogonalization/abliteration one is wild: https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refus... - other super-interesting stuff like model merges/evolutionary model merging are all things that can't happen without the weights.
There are of course really open models that include full data recipes, training logs, code, checkpoints, writeups, etc (LLM360 K2, AI2 OLMo are two recent ones) but there's a whole spectrum there, and honestly, there are very few "open" releases I've seen that aren't making at least some contributions back to the commons (often with gems in the technical reports, or in their code). Realistically, no one is re-running a training run to exactly replicate a model (from a cost, but also just a practical perspective - that model's already been trained!), but a lot of people are interested in tweaking the models to function better on their specific tasks (which actually lines up pretty well with the historical goals/impetus for open source - not to rewrite the whole code base, but to have the freedom to add the tweak you want).
The problem is, all the Open Weight models are already calling themselves Open Source, so a new name that disambiguates existing names should be chosen.
We are unfortunately now in a place where this falsehood has travelled the world while the truth is probably still half-asleep in its underwear.
It is a shame that people who are working on what is probably the pinnacle of computing can so blatantly disregard the real meaning.
Imagine if Microsoft starting announcing everywhere that Windows, because all its EXE and DLLs are right there for you to see, is actually open-source!
I suppose all we can do now is to keep asking "is it open-source or like true open-source?".