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* Forward with training.
* Do not use dropout on vgg evaluation.
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* Use dropout in the mnist training.
* Fix.
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* Add some documentation.
* Bump the crate version.
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* Simplify usage of the pool functions.
* Small tweak.
* Attempt at using apply to simplify the convnet definition.
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* Add a convnet example.
* Dataset fix.
* Randomize batches.
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- Removed a lot of surface (SerializedFileReader ownership is really
painful).
- Moved example + vision to hf.co version.
- Removed feature gate.
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* VarBuilder cleanup.
* Implement the basic varbuilders.
* Add the sharded code.
* Proper support for tensor sharding.
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* Add a couple functions required for yolo.
* Add the yolo-v3 example.
* Add minimum and maximum.
* Use the newly introduced maximum.
* Cuda support for min/max + add some testing.
* Allow for more tests to work with accelerate.
* Fix a typo.
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* Start adding the module trait.
* Use the module trait.
* Implement module for qmatmul.
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* 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>
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* 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.
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* Add a flag to change the number of epochs for the mnist training.
* Increase the learning rate for the MLP.
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