QWEN CHAT API DEMO DISCORD
It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in effectively scaling extremely large models, whether they are dense or Mixture-of-Expert (MoE) models. Many critical details regarding this scaling process were only disclosed with the recent release of DeepSeek V3. Concurrently, we are developing Qwen2.
The models are open source meaning you can download them and run them. But the training data and code to train the model is not. So, they stills control the model, as there is no way to replicate it.
Yeah, that’s kind of AI companies’ definition of open source… Other companies just have “open” in their name for historical reasons. The FSF doesn’t really agree ( https://www.fsf.org/news/fsf-is-working-on-freedom-in-machine-learning-applications ) and neither do I. It’s “open weight”. Or I’d need to see the datasets and training scripts as well.
Yeah, “open weight” seems a more appropriate label. It still seems better than a fully proprietary system, but calling it open source without clarification is misleading.
I could easily believe its true, though if so, I’m puzzled by their tactics.
Open-sourcing like this seems profoundly decentralizing and democratizing, not tendencies I’d associate with the CCP.
The models are open source meaning you can download them and run them. But the training data and code to train the model is not. So, they stills control the model, as there is no way to replicate it.
So if you can’t replicate it, it by definition isn’t open source, is it?
The model is, in the sense you can modify it. Further train it, integrate in your app, etc. But the recipe to make the model is not.
And yes, it’s less open source than we can think at first sight.
Isn’t every software binary open source then? Since you can open it in a hex editor and modify it
But tou don’t have permission to do. And hacking a binary is much more difficult than specializing a model, for instance.
Yeah, that’s kind of AI companies’ definition of open source… Other companies just have “open” in their name for historical reasons. The FSF doesn’t really agree ( https://www.fsf.org/news/fsf-is-working-on-freedom-in-machine-learning-applications ) and neither do I. It’s “open weight”. Or I’d need to see the datasets and training scripts as well.
Yeah, “open weight” seems a more appropriate label. It still seems better than a fully proprietary system, but calling it open source without clarification is misleading.