* Sketch the yolo wasm example.
* Web ui.
* Get the web ui to work.
* UI tweaks.
* More UI tweaks.
* Use the natural width/height.
* Add a link to the hf space in the readme.
* Sketching yolo-v8.
* Get the model to load.
* yolo-v8 forward pass.
* Complete(?) the forward pass.
* Fix some shape issues.
* Add the missing padding.
* Process the predictions.
* Some fixes for yolo-v3.
* Use the running stats for inference in the batch-norm layer.
* Get some proper predictions for yolo.
* Avoid the quadratic insertion.
* 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.
* Skeleton files for neon support of quantization.
* SIMD version for q4 vecdot.
* Also simdify the q6k multiplication.
* Add some timings to stable-diffusion.
* Separate the prompt stats from the post-prompt ones in the quantized example.
* Slightly nicer output printing.
* Line up with the llama.cpp implementation.
* Add a vision transformer example (dino-v2).
* Add some documentation + test.
* CI fix.
* Another fix (still unable to replicate the errors locally :( )
* Print the detected arch options.
* Add the q6k quantization.
* Add a currently broken test.
* Bugfix.
* Bugfix.
* Another bugfix.
* Another bugfix + get the test to work.
* Add flash-attention for the stable-diffusion example.
* Change the dtype.
* Silly fix.
* Another fix.
* Revert the dtype back to the query dtype after apply flash-attn.
* Add more stats to the ggml example.
* Build a quantized model from the file content.
* Move the tensor retrieval in the main crate.
* Start adding the forward pass.
* Add more to the forward pass of the quantized llama.
* Apply the attention layers.
* Add the sampling loop.
* Get the sampling loop to work.
* Minor tweak.
* Add a quantize/dequantize test.
* Bugfix.
* Add a comment + swap the order.
* Bugfixes.
* Track the conv2d operations in stable-diffusion.
* Add more tracing to stable-diffusion.
* Also trace the resnet bits.
* Trace the attention blocks.
* Also trace the attention inner part.
* Small tweak.
