Fushuan [he/him]

Sorry, this is a competition, I have already TWO Nicole messages, with two different pictures! One of them hasnt been shared widely yet I think so it’s a shiny ✨.

Do better Mr second place.

I mean, probably, but Elon needs the presidential pardon to avoid prosecution of all the shot he’s doing, bypassing congress and all that.

I guess that that’s Trumps card against him.

No source but I remember musk saying that the tariff change would require a rough transition that Americans would need to go through to get to good times or whatever.

I though that that was to prepare their base to the actual shitstorm and to them promise them the good times or whatever.

Is this PR comment forever gone now that they are in power?

Not enough for it to make results diverge. Randomness is added to avoid falling into local maximas in optimization. You should still end in the same global maxima. Models usualy run until their optimization converges.

As stated, if the randomness is big enough that multiple reruns end up with different weights aka optimized for different maximas, the randomization is trash. Anything worth their salt won’t have randomization big enough.

So, going back to my initial point, we need the training data to validate the weights. There are ways to check the performance of a model (quite literally, the same algorithm that is used to evaluate weights in training is them used to evaluate the trained weights post training) the performance should be identical up to a very small rounding error if a rerun with the same data and parameters is used.

Holy shit thanks I wasn’t getting it.

Hey, I have trained several models in pytorch, darknet, tensorflow.

With the same dataset and the same training parameters, the same final iteration of training actually does return the same weights. There’s no randomness unless they specifically add random layers and that’s not really a good idea with RNNs it wasn’t when I was working with them at least. In any case, weights should converge into a very similar point even if randomness is introduced or else the RNN is pretty much worthless.

The model is open, it’s not open source!

How is it so hard to understand? The complete source of the model is not open. It’s not a hard concept.

Sorry if I’m coming of as rude but I’m getting increasingly frustrated at having to explain a simple combination of two words that is pretty self explanatory.

The training data is NOT right there. If I can’t reproduce the results with the given data, the model is NOT open source.

The runner is open source, the model is not

The service uses both so calling their service open source gives a false impression to 99,99% of users that don’t know better.

The source OP is referring to is the training data what they used to compute those weights. Meaning, petabytes of text. Without that we don’t know which content theynused for training the model.

The running/training engines might be open source, the pretrained model isn’t and claiming otherwise is wrong.

Nothing wrong with it being this way, most commercial models operate the same way obviously. Just don’t claim that themselves is open source because a big part of it is that people can reproduce your training to verify that there’s no fowl play in the input data. We literally can’t. That’s it.

The running engine and the training engine are open source. The service that uses the model trained with the open source engine and runs it with the open source runner is not, because a biiiig big part of what makes AI work is the trained model, and a big part of the source of a trained model is training data.

When they say open source, 99.99% of the people will understand that everything is verifiable, and it just is not. This is misleading.

As others have stated, a big part of open source development is providing everything so that other users can get the exact same results. This has always been the case in open source ML development, people do provide links to their training data for reproducibility. This has been the case with most of the papers on natural language processing (overarching branch of llm) I have read in the past. Both code and training data are provided.

Example in the computer vision world, darknet and tool: https://github.com/AlexeyAB/darknet

This is the repo with the code to train and run the darknet models, and then they provide pretrained models, called yolo. They also provide links to the original dataset where the tool models were trained. THIS is open source.

What most people understand as deepseek is the app thauses their trained model, not the running or training engines.

This post mentions open source, not open source code, big distinction. The source of a trained model is part the training engine, and way bigger part the input data. We only have access to a fraction of that “source”. So the service isn’t open source.

Just to make clear, no LLM service is open source currently.

The engine is open source, the model is not.

The enumqtor is open source, the games it can run are not.

I don’t see how it’s so hard to understand.

They are saying that the model that the engine is running is open source because they released the model. That’s like saying that a game is open source because I released an emulator and the exscutable file. It’s just not true.

I wasn’t talking about a company doing a workaround, but people buying things from lverseas instead of buying things manufactured locally that needed tariffed parts.

A company hat manufactures smart bands in the US will have to increase the price to offset the chip cost increase, but xiaomi surely won’t so the “local” choice will be even more undesirable. I know that China has a global yoke on smart bands but you get the idea.

So if I buy a smart toaster from overseas and that toaster was built with microchips, but is then sold to the US via another country… That would avoid the tariff, no?

Doesn’t this literally do the inverse of boosting local manufacturing?