* add bce with logit loss
* add bce with logit loss
* remove imports
* fix tiny bug
* add test documentation and refactor function
* fix test cases and formatting
* distilbet files
* Apply various cleanups.
* More cleanups.
* More polish.
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Co-authored-by: laurent <laurent.mazare@gmail.com>
* Fix linspace implementation
`steps` should be strictly greater than 1 to make it consistent with the context.
* Handle steps == 0 and steps == 1.
* Fix rustfmt.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* Add support to UL2 model family
* Update docs with UL2
* Create ActivationWithOptionalGating to avoid polluting activations
* Also refactor quantized t5
* Remove useless conversion
* Revert Activation::NewGelu name change
* Remove useless return
* Apply rustfmt and clippy recommendations
* Reuse t5::ActivationWithOptionalGating in quantized version
* (cosmetic change) use a match rather than ifs + avoid early returns.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* add bce with logit loss
* add bce with logit loss
* remove imports
* fix tiny bug
* add test documentation and refactor function
* fix test cases and formatting
* add trocr model
* fix formatting
* commit the actual model lol
* more formatting
* remove tokenizer config
* Skeleton files for the marian MT model.
* Marian initialization.
* Implement the attention forward method.
* Forward pass for the encoder side.
* Expose the encoder and decoder.
* Start plugging the decoder.
* Forward pass for the decoder layer.
* Set up the marian example.
* Add some missing backtraces.
* Bugfix.
* feat: implement VGG13, VGG16 and VGG19
* Cosmetic fixes.
* More cosmetic tweaks + avoid re-loading the weights on each final layer.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add the jina-bert model.
* Use alibi.
* Remove the unused pragma.
* Recompute the alibi embeddings.
* Generate the token type ids.
* Use the module trait.
* Add the jina-bert example.
* DType fix.
* Get the inference to work.
* Add the blip example.
* Tweak the example.
* Implement the cross-attn logic.
* Fix some shape mismatches.
* Get some logits out.
* Get some caption to be generated.
* Start adding vision-transformers.
* Add self-attn.
* More vision transformers.
* vit-vit.
* Add the actual vit model.
* Add the example code for the vision transformers.
