* Add OpenChat to quantized examples
* Add chat prompt
* Make the openchat example more in line with the other models.
* Fix a typo.
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Co-authored-by: laurent <laurent.mazare@gmail.com>
Updating the readme to coincide with other examples. If you try to run it as previously written, you will get a "cannot find the path specified" error.
* 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
* Support the shape op in ONNX.
* Share the axis normalization bits.
* Add some limited support for gather.
* Unsqueeze.
* Comparison with broadcasting.
* Add Not + handle i32.
* Tweaks for the quantized model.
* Support the shape op in ONNX.
* Share the axis normalization bits.
* Add some limited support for gather.
* Unsqueeze.
* Comparison with broadcasting.
* Add Not + handle i32.
* Adds check for 7b-zephyr and uses correct template
* Handle zephyr as mistral.
* Disable the protoc bits of the CI.
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add more models to the onnx example.
* Input validation.
* Input validation.
* Bugfix.
* Implement clip.
* BatchNorm support.
* Get the efficientnet onnx to work.
* Negative and `*args` shape handling
* Rename to `PyShapeWithHole` + validate that only one hole exists
* Regenerate stubs
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Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com>
* 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.
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Fix Gym wrapper
- It was returning things in the wrong order
- Gym now differentiates between terminated and truncated
* Add DDPG
* Apply fixes
* Remove Result annotations
* Also remove Vec annotation
* rustfmt
* Various small improvements (avoid cloning, mutability, get clippy to pass, ...)
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Co-authored-by: Travis Hammond <travis.hammond@alexanderthamm.com>
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.
