* Adding Granite 7b Instruct model example
* Minor refactoring to make it a little more idiomatic
* Clippy fixes.
* * Adding a README with some information about supported Granite models
* Changing the default prompt to accomodate better the Language
modality of the Granite 7b Instruct model
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add the mimi audio-tokenizer.
* Formatting tweaks.
* Add a full example.
* Use the transformers names.
* More renamings.
* Get encoding and decoding to work.
* Clippy fixes.
* Include the MLX gemm kernels.
* Clippy lints.
* Export the gemm_f32 kernel.
* Add the f16/bf16 variants.
* Add the initial dispatch code.
* More plugging of the mlx kernels.
* Add a currently broken test.
* Tweaks.
* Bugfix + get the tests to pass.
* Enable the gemm bf16 tests.
* Add some randomized tests.
* Update candle-metal-kernels/src/lib.rs
Co-authored-by: ivarflakstad <69173633+ivarflakstad@users.noreply.github.com>
* More fixes.
* More clippy fixes.
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Co-authored-by: ivarflakstad <69173633+ivarflakstad@users.noreply.github.com>
* Allow loading images with given std and mean
* OpenCLIP text encoder component
* Two MobileCLIP models
* Clippy fixes.
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* silero-vad v5 example
This change adds an example of how to run silero-vad v5
* PR: rename 'vad' to 'silero-vad'
* Update README.md
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Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com>
index_select does not support negative indexing, but
this change adds just enough workarounds in onnx to
allow evaluating silero-vad models (which make use of
negative indices).
* onnx: workaround pow with negative base
rather than fully defining pow in the cpu backend (as in #2318),
this implements a much smaller change which is sufficient to evaluate silero-vad
onnx models. Specifically, checking if pow is run with 2.0 exponent, and if so
evaluate as simply `x*x` instead of the cpu backend of `e^(2.0 * ln(x))`.
* PR: use Tensor::powf insead
powf correctly handles a negative base.
* Start sketching parler-tts support.
* Implement the attention.
* Add the example code.
* Fix the example.
* Add the description + t5 encode it.
* More of the parler forward pass.
* Fix the positional embeddings.
* Support random sampling in generation.
* Handle EOS.
* Add the python decoder.
* Proper causality mask.
* Soft NMS with thresholds
* NMS Test
* Soft nms w/ boxes removed below threshold
* Soft nms test
* No longer removing bounding boxes to fit Soft-NMS focus
* Initialize confidence
* Added comments
* Refactored out updating based on IOU/sigma
* Score_threshold -> confidence_threshold for clarity
* Remove bboxes below confidence threshold
* Softnms basic functionality test
* Softnms confidence decay test
* Softnms confidence threshold test
* Softnms no overlapping bbox test
* Testing confidence after no overlap test
* Single bbox and no bbox tests
* Signify test completion
* Handling result of test functions
* Checking all pairs of bboxes instead of a forward pass
* Equal confidence overlap test
* Clarified tests for implementation
* No longer dropping boxes, just setting to 0.0
* Formatted w/ cargo
Laurent Mazare