* Rework the commands and run inference by default.
* Add the training module and load the training dataset.
* Random dataset iterator.
* Proper valid-loss computation.
* Compute the evaluation loss.
* Add more substance to the training loop.
* Add some flash-attn kernel, import the code for flash-attn v2 from Dao-AILab.
* More flash attn.
* Set up the flash attn parameters.
* Get things to compile locally.
* Move the flash attention files in a different directory.
* Build the static C library with nvcc.
* Add more flash attention.
* Update the build part.
* Better caching.
* Exclude flash attention from the default workspace.
* Put flash-attn behind a feature gate.
* Get the flash attn kernel to run.
* Move the flags to a more appropriate place.
* Enable flash attention in llama.
* Use flash attention in llama.
* Cleanup some todos.
* Fix more todo.
* Optimize for the contiguous case.
* Add the IntDType trait.
* Handle the intdtype trait for more ops.
* Remove a todo.
* Remove a todo.
* More realistic training setup.
* Compute the model accuracy.
* Very inefficient backprop for index select.
* More backprop.
* Fix some backprop issues.
* Backprop fix.
* Another broadcasting backprop fix.
* Better backprop for reducing ops.
* Training again.
* Add some gradient tests.
* Get the training to work.
* Add the nn::optim and some conversion traits.
* Add the backward_step function for SGD.
* Get the SGD optimizer to work and add a test.
* Make the test slighly simpler.
* Move the variable creation to the variable module.
* Make it possible to set a variable.
* Add some basic gradient descent test.
* Get the gradient descent test to work.
