* Add the SigLIP model.
* Add more to the forward pass of the vision model.
* Complete the forward pass.
* Add the siglip example.
* Fix.
* Another fix.
* Get everything in place.
* Add a readme.
* Quantized version of flux.
* More generic sampling.
* Hook the quantized model.
* Use the newly minted gguf file.
* Fix for the quantized model.
* Default to avoid the faster cuda kernels.
* Add a RotatingKVCache.
* Add some KvCache tests.
* Test the reset too.
* More kv-cache testing.
* More tests for the rotating kv-cache.
* Improve the api for the rotating cache so that the whole src tensor gets returned when it's overlarge.
* Handle contiguity + bugfix + use in mimi.
* Add a way to test the mimi streaming mode.
* Mimi streaming fixes.
* More rotating kv-cache.
* Fix the attn mask generation.
* Handle the abs case.
* Add some tests for the generated mask.
* 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
---------
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.
* Allow loading images with given std and mean
* OpenCLIP text encoder component
* Two MobileCLIP models
* Clippy fixes.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* 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
* Add the flux autoencoder.
* Add the encoder down-blocks.
* Upsampling in the decoder.
* Sketch the flow matching model.
* More flux model.
* Add some of the positional embeddings.
* Add the rope embeddings.
* Add the sampling functions.
* Add the flux example.
* Fix the T5 bits.
* Proper T5 tokenizer.
* Clip encoder path fix.
* Get the clip embeddings.
* No configurable weights in layer norm.
* More weights related fixes.
* Yet another shape fix.
* DType fix.
* Fix a couple more shape issues.
* DType fixes.
* Fix the latent dims.
* Fix more shape issues.
* Autoencoder fixes.
* Get some generations out.
* Bugfix.
* T5 padding.
* Clippy fix.
* Add the decode only mode.
* Fix.
* More fixes.
* Finally get some generations to work.
* Add readme.
* bert attention mask
* Allow for using None as a mask.
* Revert part of the changes so that the proper default mask applies.
* Cosmetic change.
* Another cosmetic tweak.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add Llama 3.1 rope
* Clippy
* Format
* Clippy
* Add support for multiple eos tokens:
* Untagged either
* Remove either dep and fix settings.json
* Make the max positional embeddings configurable
* Add: DINOv2Reg4 with PlantCLEF2024 weights and example ( See https://arxiv.org/abs/2309.16588 and https://zenodo.org/records/10848263 )
* Remove extra files + update README to download them + remove extra lines
* minor fix (README remove extra spaces)
* minor fix (README: Fix image url)
* Modif: Add back interpolate_pos_encoding() + fix when no interpolation + remove extra comments + Update README ( source image changed and so the predictions )
* Fix: Improve code lisibility with '$ cargo clippy' and '$ cargo fmt'
* Another clippy fix.
---------
Co-authored-by: x-VEspit <vincent.espitalier@cirad.fr>
Co-authored-by: laurent <laurent.mazare@gmail.com>
* define structs
* construct ResidualConvUnit
* forward() for ResidualConvUnit
* implement FeatureFusionBlock
* implement Scratch
* implement DPTHead
* add identity module
* implement forward for DTPHead
* add get_intermediate_layers to DinoVisionTransformer
* implement DepthAnythingV2
* some minor tweaks
* fix compile errors
* fix var builder prefixes
* setup initial example
* use fixed patch size of 37 (518 / 14)
* debugged until output
* print min and max values
* add some dynamism to the output location
* scale input image
* extract prep function
* extract output path function
* normalize image with magic mean and std
* add spectral coloring
* squeeze in the right place
* make enterpolation optional
* use bail instead of panic
* omit unnecessary Shape call
* remove empty curly braces
* use bail instead of assert
* use vb and pp
* remove closures
* extract config object
* Apply rustfmt.
* Fix some clippy lints.
* More lints.
* Use the array methods.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* Add a slice_set op.
* Add some testing.
* Add the dedicated kv-cache module.
* Derive debug and clone.
* Expose more kv-cache functions.
* Return the current data when appending.
* Use the new cache in the quantized phi3 model.
* Support embedding model gte-Qwen1.5-7B-instruct
This is a text embedding model based on Qwen2. They share same
model architecture except the last MLP module. This commit brings in
minimal modification of the old Qwen2 implementation to support both
models.
An example is provided, and had been verified according to the official
PyTorch implementation.
* Avoid doing the 'last-token filtering' based on the absence of attention mask.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Separate quantized phi-3 implementation.
* Integrate the quantized phi3 model.=
* Small fixes, get the generation to work properly.
* Keep the old llama implementation around.
* Change the default.
