"Parameters are coefficients inside the model that are adjusted by the training procedure. The dataset is what you train the model on. Language models are trained with tokens that are subword units (e.g. prefix, root, suffix)."
His comment on GPT-4 parameters count
"Also: a model with more parameters is not necessarily better. It's generally more expensive to run and requires more RAM than a single GPU card can have.
GPT-4 is rumored to be a "mixture of experts", i.e. a neural net consisting of multiple specialized modules, only one of which is run on any particular prompt. So the effective number of parameters used at any one time is smaller than the total number."
2 - Selective model comparison, once you extend the supported models it can be inefficient to run all the models all the time, selective execution can give more control to users to execute only required comparison models.
Nice! I see there is an option of communities to organize the posts, should there be a way for user to create or submit the name of community that is not present?
Also, I have a crawler which fetches blogs from OpenAI and DeepMind if there is a community for blogs I can plugin the code to post those articles there as well.