* Start adding a stable-diffusion example.
* Proper computation of the causal mask.
* Add the chunk operation.
* Work in progress: port the attention module.
* Add some dummy modules for conv2d and group-norm, get the attention module to compile.
* Re-enable the 2d convolution.
* Add the embeddings module.
* Add the resnet module.
* Add the unet blocks.
* Add the unet.
* And add the variational auto-encoder.
* Use the pad function from utils.
* Move the vision datasets to a separate crate.
* Move the batcher bits.
* Update the readme.
* Move the tiny-stories bits.
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Co-authored-by: Jane Doe <jane.doe@example.org>
* Rework the var-builder to handle initializations.
* Add some helper functions for layer creation.
* Improve the layer initializations.
* Get initialized variables.
* Precompute the rot embeddings when training lamas.
* 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.
* Cuda support for the mnist training.
* min/max fix + testing.
* Add the argmin/argmax tests.
* More cuda support for argmin/argmax.
* Cuda kernels for argmin and argmax.
* Start sketching the bigcode gpt model.
* Sketch the bigcode model.
* Implement the attention mechanism.
* Random reshaping.
* Sketch more of the example.
* Add some kv cache.
* Properly generate the position ids.
* Proper attention mask.
* Bail on upcasting.
* Properly apply the attention mask.
* Add the smaller starcoder variants.
* Update for the new hub api.
* Fix a shape issue.
* Fix another shape issue.
* Get some logits out.
* Adjust the weigth names.
