* Sketch the mamba model for inference.
* Complete the forward pass.
* Add the mamba example.
* Optimize the selective-scan part.
* Fix a couple shape mismatches and get inference to work.
* Tweak the readmes.
* More readme tweaks.
* Initial check-in for the qwen2 model.
* More qwen2 inference.
* Polish the qwen example.
* Fix the rope basis.
* Get the inference to work.
* Support different model sizes.
* Add the ChatGLM model.
* Rotary embeddings.
* Add to the forward pass.
* Add to the forward pass.
* Add the rotary embeddings.
* Add the KV cache.
* Add the chatglm example.
* Bugfix.
* More glm fixes.
* Fix some shape issues.
* Get the inference to work.
* Update the Phi model to use the updated architecture.
* Add more of the phi model.
* Repeat KV + caching.
* Apply the rotary embeddings.
* Add support for the new phi model in the phi example.
* Fix a couple glitches.
* Fix a couple more glitches.
* Add RepVGG model.
* Add RepVGG README
* Extract var to top level
* Replace hashmap with a match
* Add a variant for the model kind + avoid some unnecessary config cloning.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
* Add the Mixtral model.
* Add more of the mixtral layers.
* Add the final layers for mixtral.
* Sketch the expert selection.
* Add some expert routing logic.
* Hopefully finish the routing logic for mixtral.
* Add the mixtral example.
* Fix the weight filenames.
* Bugfix.
* Another fix.
* Yet another fix + remove the unused pragma.
* Shape fix.
* Add a readme.
* 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
* distilbet files
* Apply various cleanups.
* More cleanups.
* More polish.
---------
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
* 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.
---------
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.
* 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.
* Only optimize float tensors.
* Use full tensors for zeros and ones.
* Add a benchmark for the matmul slowness.
* Add the convmixer model.
* Proper adaptive pooling.
* Quantized version of mistral.
* Integrate the quantized mistral variant.
* Use the quantized weight files.
* Tweak the quantization command.
* Fix the dtype when computing the rotary embeddings.
* Update the readme with the quantized version.
* Fix the decoding of the remaining tokens.
