It was already the most open language model in its class, given that the code for training and inference was available and it only used public data for training.
For Google and OpenAIs offerings, have fun reimplementing it from descriptions in the paper (including small crucial details that they may have left out), training it for a month, and then wondering if the implementation or the training data is the reason your model isn't as good as theirs.
Weights are more valuable than training code in one regard. Even with the training code you may not have the dataset and reproduction requires a massive GPU cluster that few can afford.
Weights are more valuable to random individuals who want to mess around with the model. Training code is more valuable to other companies that have the resources to use them, because then they can tweak/modify however they want. But even then, you still need the training data, which in the case of OpenAI and DeepMind is a big part of the secret sauce (not just the raw data but also the process for cleansing and de-duplicating it).
Training code won't get you much if you don't have the infra/money to gather a suitable dataset or actually execute training. Plus if your goal is to "steal" or riff on the base model, it's already there in the weights. Also probably not difficult to figure out how to fine tune it once you have the weights and tokenizer.
For Google and OpenAIs offerings, have fun reimplementing it from descriptions in the paper (including small crucial details that they may have left out), training it for a month, and then wondering if the implementation or the training data is the reason your model isn't as good as theirs.