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The weights file is what took the effort to produce. Months of computation, enormous amounts of electricity, hundreds of millions of dollars, all spent adjusting billions of numbers until they work. The program file is public knowledge. Researchers publish it openly. The weights are what companies guard, sell access to, and spend fortunes producing.
Some companies keep their weights entirely private. You can use the model through their website or app, but the file itself never leaves their servers. Others release their weights openly, making them available for anyone to download, study, or build on.
This distinction matters. An open weights model can run on your own hardware, be inspected by researchers, or be adapted for specific uses. A closed model can only be used on the terms the company sets.
Think about eating a dish at a restaurant that you love. You can see what's in it. You can taste the ingredients. You could probably name most of them. But you couldn't go home and make it. Not really. You'd need the exact quantities, the order things were added, the temperature, the timing. All the knowledge that makes it that dish rather than a rough approximation of it.
Knowing the program is a bit like knowing the ingredients. You can see the structure. But the weights, the specific configuration that took months and hundreds of millions of dollars to produce, that's the recipe. And most companies keep it locked in the kitchen.
It's why what separates one model from another, what makes
The kind of model we've been building toward has a specific name: a Large Language Model, or LLM. Large, because of the sheer number of weights. Language, because it was trained on text. Model, because it is exactly what we've been describing: a file full of numbers.
But where does that file actually live, and how do you get to it?