No, the preferred form for modifying a model is a copy of the weights, plus open source code for training and inference. “Training a similar model from scratch” is wildly more expensive and less convenient, and not even modification!
If the model weights are available under an OSI-approved open source license, and so is code suitable for fine-tuning, I consider the model to be open source. Llama models definitely aren’t; most Chinese models are.
Suppose I write a program and let people download the binary. Can I say “I spent 100k on AWS to compile it, therefore the binary is open source”?
not even modification
Would you say compiling source code from scratch (e.g. for a different platform) is not a modification?
Even if you’re not intending to retrain the model from scratch, simply knowing what the training data is is valuable. Maybe you don’t care about the training data, but somebody else does. I don’t think “I could never possibly make use of the source code / training data” is an argument that a binary / weights is actually open source.
How does open source differ from closed source for you in the case of generative models? If they are the same, why use the term at all?
No, the preferred form for modifying a model is a copy of the weights, plus open source code for training and inference. “Training a similar model from scratch” is wildly more expensive and less convenient, and not even modification!
If the model weights are available under an OSI-approved open source license, and so is code suitable for fine-tuning, I consider the model to be open source. Llama models definitely aren’t; most Chinese models are.
Suppose I write a program and let people download the binary. Can I say “I spent 100k on AWS to compile it, therefore the binary is open source”?
Would you say compiling source code from scratch (e.g. for a different platform) is not a modification?
Even if you’re not intending to retrain the model from scratch, simply knowing what the training data is is valuable. Maybe you don’t care about the training data, but somebody else does. I don’t think “I could never possibly make use of the source code / training data” is an argument that a binary / weights is actually open source.
How does open source differ from closed source for you in the case of generative models? If they are the same, why use the term at all?