That quote is straight up wrong to claim Deepseek has "loathed to reveal their secret sauce".
The source of all the excitement is exactly how much they revealed, and I feel like that thread as a whole emphasizes why people who aren't deeply familiar with the pipeline should not get to define these things.
There is a lot of detail about the nature of the data used and the exact steps needed to reproduce their findings with your own data. They even provide R1-Zero to demonstrate things that might be dead ends just in case someone can continue them. That should be enough to satisfy any useful definition of open source.
Even in the same thread you linked:
> Just a curiosity, according to the Model Openness Framework from the Linux Foundation, DeepSeek-R1 classifies as an Open Model:
At the end of the day this is as good as it needs to be for LLMs: By their nature a lot of data being used to train them cannot or should not be openly shared, but the shape and motivations behind the data used are able to push others very far along the way to reproduction and iteration.
The source of all the excitement is exactly how much they revealed, and I feel like that thread as a whole emphasizes why people who aren't deeply familiar with the pipeline should not get to define these things.
"Nick" claims the secret sauce is in something not provided, then posts an article that demonstrates the exact "secret sauce", seemingly not making the connection: https://www.interconnects.ai/p/deepseek-r1-recipe-for-o1
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There is a lot of detail about the nature of the data used and the exact steps needed to reproduce their findings with your own data. They even provide R1-Zero to demonstrate things that might be dead ends just in case someone can continue them. That should be enough to satisfy any useful definition of open source.
Even in the same thread you linked:
> Just a curiosity, according to the Model Openness Framework from the Linux Foundation, DeepSeek-R1 classifies as an Open Model:
> https://mot.isitopen.ai/model/1143
At the end of the day this is as good as it needs to be for LLMs: By their nature a lot of data being used to train them cannot or should not be openly shared, but the shape and motivations behind the data used are able to push others very far along the way to reproduction and iteration.