* Stella_en_1.5B_v5
* Separated creation. This is a critical step for numerical accuracy and would be documented in the readme
* EmbedDim would require clone and copy
* WIP: example
* Examples added
* a litte more in README
* WIP: ONNX Reduce-max ops
* WIP: tests for ReduceMin
* Reduce min/ max v18+
* Reformatting tests for better review readability
* Error on empty set, backward compatibility (13 and below) with 'axes'
* Stella_en_1.5B_v5
* Separated creation. This is a critical step for numerical accuracy and would be documented in the readme
* EmbedDim would require clone and copy
* WIP: example
* Examples added
* a litte more in README
* Add stable diffusion 3 example
Add get_qkv_linear to handle different dimensionality in linears
Add stable diffusion 3 example
Add use_quant_conv and use_post_quant_conv for vae in stable diffusion
adapt existing AutoEncoderKLConfig to the change
add forward_until_encoder_layer to ClipTextTransformer
rename sd3 config to sd3_medium in mmdit; minor clean-up
Enable flash-attn for mmdit impl when the feature is enabled.
Add sd3 example codebase
add document
crediting references
pass the cargo fmt test
pass the clippy test
* fix typos
* expose cfg_scale and time_shift as options
* Replace the sample image with JPG version. Change image output format accordingly.
* make meaningful error messages
* remove the tail-end assignment in sd3_vae_vb_rename
* remove the CUDA requirement
* use default_value in clap args
* add use_flash_attn to turn on/off flash-attn for MMDiT at runtime
* resolve clippy errors and warnings
* use default_value_t
* Pin the web-sys dependency.
* Clippy fix.
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Co-authored-by: Laurent <laurent.mazare@gmail.com>
* start to impl chinese clip
* impl vision model
* copy code from bert
* refactor use
* refactor use again
* fix text model
* refactor
* try to fix text model
* tuning
* tuning chinese clip
* delete useless code
* revert code
* Clippy fixes.
* Also apply cargo fmt.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* add bert for masked lm
* working example
* add example readme
* Clippy fix.
* And apply rustfmt.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* WIP: hopefully better const impl
* with GPU
* More tests on
* Reverting primitive for
* Incorporating review changes - added check elem count check in kerner, using for call strategy
* rustfmt ran
* add: direction for lstm layer
* lint: remove unused Error import
* refactor: remove unnecessary int assignment to Direction enum:
* refactor: use &'static str type instead of String for direction_str:
* Run cargofmt.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add Pixtral.
* More pixtral vision encoder.
* Sketch a pixtral example.
* Sketch a pixtral example.
* Better image loading.
* Support loading images embedded in safetensor files.
* Clippy fixes.
* Add the llava multimodal adapter.
* Add more of the llava bits.
* Add the pixtral config.
* More pixtral inference.
* Add the text generation bits.
* Get the example to work.
* Bugfix.
* Run some bits of the model in f32.
* Blessed version :)
* Better rope frequency computations.
* README update.
* 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.
* candle-onnx: Add Split and Expand operators, Fix Where Op
Implemented based on https://github.com/onnx/onnx/blob/main/docs/Operators.md
Test cases based on those examples.
TODO: Should add the remaining Split examples as tests
TODO: Add.test case that motivates Where fix
* candle-onnx: Add ReduceSum operator
Implemented based on https://github.com/onnx/onnx/blob/main/docs/Operators.md
Test cases based on those examples.
TODO: Should add the remaining ReduceSum examples as tests
* candle-onnx: Add ReduceL2 operator
Implemented based on https://github.com/onnx/onnx/blob/main/docs/Operators.md
Test cases based on those examples.
TODO: Should add the remaining ReduceSum examples as tests
* candle-onnx: Fix Clip operator empty string as default arg issue
Optional input args may be signified by an empty string. The length of the input array is not enough because non optional args may follow optional ones.
I encountered this when trying to use the ONNX model found at https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 for example.
The LSTM op has a utility which I factored to be more generally accessible, and I have used it in the ops I have recently created or debugged.
I believe it is likely that this issue may also manifest in other ops, but I didn't want to change anything that I'm not testing.
* fix formatting
* fix small mistake made during refactor
* 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.
