These models are a combination of a small amount of code, a ton of training data, and a ton of expertise in how to train them (which I suspect includes how best design/curate training sets for different model improvement goals).
The Chinese models are mostly very well documented in terms of architecture and training processes/flows, with what is missing to recreate them being the training data.
You don't need the source code - just read their architecture docs and implement it yourself.
The Chinese have actually been very open about training, starting perhaps with the DeepSeek-R1 paper which told the world in detail how to train a reasoning model. The Kimi 3 paper also gives a lot of training details.
There really isn't much of a comparison to be had between building a traditional software project where all you need is the (maybe open source) source code and the Makefile that automates the build process, and building a machine learning system where it's primarily about data not source code, and even with a road map of what may be very complex training (cf build) process, you'd probably still have a hard time building it since AFAIK the training process may still involve expert knowledge and intervention - I don't think training has been reduced to a hands-off "Makefile" or build script.
So, basically lack of source code is the least of the issues in being able to build one of these models - it's mostly the training data and training processes/expertise that you would need.
> The Chinese models are mostly very well documented in terms of architecture and training processes/flows, with what is missing to recreate them being the training data.
...and because that training data is missing, they can't be replicated. Which means that you cannot assert that the Chinese are being open in their LLM development, because there's no way to verify that the techniques they describe are actually the ones being used.
The reason that the training data is missing is that they're trained on a large amount of American copyrighted data and distilled on American models, which is where a lot of their performance comes from.
You can replicate the architectural innovations, and try them for yourself with your own dataset. It seems some of them are certainly being used by western companies, such as DeepSeek Sparse Attention, now supported by NVIDIA cuDNN.
Ditto for training algorithms and procedures such as Slime or DeepSeek's details instructions on how to build a reasoning model.
This is the exact value of openly shared details - others CAN copy and try them and modify them themselves.
Yes, the training data specifically has not been released for any model, American or Chinese, but that doesn't detract from what has been shared, and the reason the Chinese are not sharing data are no more nefarious than why the American companies are not sharing - because they are all using data from sources they don't want you to know about, and at the end of the day the data is the closest thing any of them do have to a moat.
> You can replicate the architectural innovations, and try them for yourself with your own dataset.
That's not related to my comment. My comment was pointing out that you can't verify something that wasn't published. You have no idea what fraction of their techniques they're not publishing, and how much they contribute to their model performance, because you cannot replicate the models, because they don't publish their training data.
> and the reason the Chinese are not sharing data are no more nefarious than why the American companies are not sharing
This is moving the goalposts. Your claim was that "The Chinese have actually been very open about training", which is false, as discussed. Nobody ever claimed that the American labs were open.
> You have no idea what fraction of their techniques they're not publishing, and how much they contribute to their model performance, because you cannot replicate the models, because they don't publish their training data.
Why would you be concerned about THEIR model performance ?!
Surely if you are an ML researcher and read about a new technique, you are interested in how YOU may be able to use it.
If you are Ilya Sutskever sitting at OpenAI in 2017, and happen upon Google's "attention" (Transformer architecture) paper, then what you do is go and implement it for yourself, get yourself some training data, and try it.
What you are NOT going to do is whine about not being given their source code, or their training data, or their training harness, or a dump of Google's corporate secrets. You take the research that has been shared and evaluate it for yourself.
This doesn't have anything to do with Google or OpenAI. I'm not Ilya Sutskever and it's not 2017, either.
I'm not "whining" about anything. You made the claim "The Chinese have actually been very open about training" and I showed that that was false. That's all that there is to it.
You say that but for like 2 years there was a guy on huggingface releasing quants as TheBloke. No identity no nothing except he likely had a grant or a university job that let him do it. Quants aren't the same as training but it was beyond 99% of people at the time (as you say this is)
Cant stand when you guys try and force something as impossible on the rest of us simply because you could never accomplish it.
I did not say that it is impossible. I just think that we need architectural improvements, or maybe even a fundamentally different approach to get something like Kimi K3 for cheap. The point I was trying to make was that we shouldn't just laze about and hope that hardware improvements will get us there.
(Also, I know Tom Jobbins (TheBloke), and have personally contributed to increase the adoption of GGUF, e.g. in the transformers and ktransformers libraries, so I find the personal dig quite amusing.)
Unfortunately, electricity prices did not fall by the same factor, so I fear that training a frontier model will still cause a an unsustainable dent in my monthly budget.
Compiling source code also takes computing resources, only the scale is different. It's a very close analogy: source code with training data, and compiled binaries with model weights.
If we start with the same ingredients and independently make our stews they're likely still going to taste a bit different due to the non deterministic nature of the process. Isn't it similar with training LLMs at scale? Do you get identical weights if you do multiple runs?
Yeah I understand that. I mean just the methods they use to acquire the training data has already had a shit ton of drama, there's no way the western labs are ever going to be public about the ingredients.
Not sure why it matters whether weights are software or not.
One can equally argue that firmware is just settings for the CPU.
If weights aren't copyrightable then doesn't this argue against the idea of them being able to have the quality of open-sourceness? Meanwhile the training code could be copyrightable.
Well, my position is that the software that defines/runs the model is the open-source part.
The weights are the asset that makes it useful, but I don’t think 40T tokens of pre-training data should be required to call the model itself open source when you can inspect every line of code in the model without that, as well as instantiate and run the model with randomly initialized weights.
