DeepSeek's gifts to the world of its open weights, public research and OSS code of its SOTA models are all any reasonable person should expect given no organization is going to release their dataset and open themselves up to criticism and legal exposure.
You shouldn't expect to any to see datasets behind any SOTA models until they're able to be synthetically generated from larger models. Models only trained on sanctioned "public" datasets are not going to perform as well which makes them a lot less interesting and practically useful.
Yes it would be great for their to be open models containing original datasets and a working pipeline to recreate models from scratch. But when few people would even have the resources to train the models and the huge training costs just result in worse performing models, it's only academically interesting to a few research labs.
Open model releases should be celebrated, not criticized with unreasonable nitpicking and expectations that serves no useful purpose other than discouraging future open releases. When the norm is for Open Models to include their datasets, we can start criticizing those that don't, but until then be gracious that they're contributing anything at all.
Terminology exists for a reason. Doubly so for well-established terms of art that pertain to licensing and contract law.
They could have used "open wights" which would have conveyed the company's desired intent just as well as "open source", but without the ambiguity. They deliberately chose to misuse a well established term instead.
I applaud and thank deepseek for opening their weights, but i absolutely condemn them and others (e.g Facebook) for their deliberate and continued misuse of the term. I and others like me will continue to
raise this point as long as we are active in this field, so expect to see this criticism for decades.
Hopefully one of these companies losses a lawsuit due to these shenanigans. Perhaps then they wouldn't misuse these terms so brazenly.
> i absolutely condemn them and others (e.g Facebook) for their deliberate and continued misuse of the term
This is the kind of inconsequential nitpicking diatribe I'm referring to. When has "open data" ever meant Open Source?
> They deliberately chose to misuse a well established term instead.
Their model weights as well as their repositories containing their technical papers and any source code are published under an OSS MIT license, which is the reason why initiatives like this looking to reproduce R1 are even possible.
But no, we have to waste space in every open model release complaining that they must be condemned for continuing to use the same label the rest of the industry uses to describe their open models which are released under an OSS License as Open Source - instead of using whatever preferred unused label you want them to use.
DeepSeek's gifts to the world of its open weights, public research and OSS code of its SOTA models are all any reasonable person should expect given no organization is going to release their dataset and open themselves up to criticism and legal exposure.
You shouldn't expect to any to see datasets behind any SOTA models until they're able to be synthetically generated from larger models. Models only trained on sanctioned "public" datasets are not going to perform as well which makes them a lot less interesting and practically useful.
Yes it would be great for their to be open models containing original datasets and a working pipeline to recreate models from scratch. But when few people would even have the resources to train the models and the huge training costs just result in worse performing models, it's only academically interesting to a few research labs.
Open model releases should be celebrated, not criticized with unreasonable nitpicking and expectations that serves no useful purpose other than discouraging future open releases. When the norm is for Open Models to include their datasets, we can start criticizing those that don't, but until then be gracious that they're contributing anything at all.