This is key because without this "cheat sheet" of knowing how certain parameters should be written, you can very easily sit in frustration as you're trying to search for models with the API!Now that we know what the right parameters are, we can search the API easily:...
You can, of course, modify your own trainer to integrate DeepSpeed and FairScale, based on each project's instructions or you can "cheat" and see how we did it in the `transformers` Trainer. If you go for the latter, to find your way around `grep` the source code for `deepspeed` a...
You can, of course, modify your own trainer to integrate DeepSpeed and FairScale, based on each project's instructions or you can "cheat" and see how we did it in the transformers Trainer. If you go for the latter, to find your way around grep the source code ...
You can, of course, modify your own trainer to integrate DeepSpeed and FairScale, based on each project's instructions or you can "cheat" and see how we did it in the transformers Trainer. If you go for the latter, to find your way around grep the source code fo...
Think of this Q-table as the memory or cheat sheet of our Q-function. The problem is that Q-Learning is a tabular method. Aka, a problem in which the state and actions spaces are small enough to approximate value functions to be represented as arrays and tables. And this is...
Think of this Q-table as the memory or cheat sheet of our Q-function. The problem is that Q-Learning is a tabular method. Aka, a problem in which the state and actions spaces are small enough to approximate value functions to be represented as arrays and tables. And this...
You can, of course, modify your own trainer to integrate DeepSpeed and FairScale, based on each project's instructions or you can "cheat" and see how we did it in the transformers Trainer. If you go for the latter, to find your way around grep the source code for...
The first step was to cheat, of course. Why make a big effort when one can make a little one. So I wrote a short notebook that in a few lines of code provided a proxy to fairseq and emulated transformers API. If no other things, but basic translation, was required, th...
You can, of course, modify your own trainer to integrate DeepSpeed and FairScale, based on each project's instructions or you can "cheat" and see how we did it in the transformers Trainer. If you go for the latter, to find your way around grep the source code for d...
This is typically done by adding a KL penalty to the full reward maximisation objective via a reference model, which serves to prevent the model from learning to cheat or exploit the reward model.The DPO formulation bypasses the reward modeling step and directly optimises the language mode...