BytePairTokenizer must not split sequences of \n (keras-team#1910)
* fix for loading of special tokens in Llama tokenizer
* fix for Llama tokenizer which can have multiple end tokens
* bug fix
* adding some missing tokens to Llama3 tokenizer
* fixed tests and Llama3Tokenizer init.
* now loading correct eos_token config from Hugging Face checkpoint. Using hack for Keras checkpoint because it does not have this info
* fix for BytePairTokenizer to make Lllama3-instruct work in chat: \n\n sequences are significant in the chat template and must be preserved by the tokenizer
---------
Co-authored-by: Martin Görner <[email protected]>
fix for generation that never stops in Llama3-Instruct variants (keras-team#1904)
* fix for loading of special tokens in Llama tokenizer
* fix for Llama tokenizer which can have multiple end tokens
* bug fix
* adding some missing tokens to Llama3 tokenizer
* fixed tests and Llama3Tokenizer init.
* now loading correct eos_token config from Hugging Face checkpoint. Using hack for Keras checkpoint because it does not have this info
---------
Co-authored-by: Martin Görner <[email protected]>
fix failing JAX GPU test (keras-team#1911)
* fix tests
* fix test
Refactor `MMDiT`, add `ImageToImage` and `Inpaint` for SD3 (keras-team#1909)
* Refactor `MMDiT` and add `ImageToImage`
* Update model version
* Fix minor bugs.
* Add `Inpaint` for SD3.
* Fix warnings of MMDiT.
* Addcomment to Inpaint
* Simplify `MMDiT` implementation and info of `summary()`.
* Refactor `generate()` API of `TextToImage`, `ImageToImage` and `Inpaint`.
Minor bug fix (keras-team#1915)
Change to image_converter.image_size since it is a tuple and it's not a callable function.
[Mix Transformer] Add Presets for MiTB0...MiTB5 (keras-team#1893)
* add presets for mit
* add standin paths
* register presets in __init__.py
* fix op in overlapping patching and embedding, start adding conversion utils
* style
* add padding to MiT patchingandembedding
* update to support other presets
* update conversin script
* fix link for b5
* add cityscapes weights
* update presets
* update presets
* update conversion script to make directories
* use save_preset
* change name of output dir
* add preprocessor flow
* api gen and add preprocessor to mits
* conform to new image classifier style
* format
* resizing image converter -> ImageConverter
* address comments
refactoring
remove default resizing for vision backbones (keras-team#1916)
* remove defailt resizing
* fix GPU test
Update VGG model to be compatible with HF and add conversion scripts (keras-team#1914)
Deeplab presets (keras-team#1918)
* add preset configurations for deeplabv3
* fix uri
* Add training details
update presets to point to the main Keras Kaggle page (keras-team#1921)
* update presets to point to the main keras page
* update mit path
Added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates (keras-team#1912)
* added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates
* un commented the test lines that were commented by mistake
* fixed linter errors
Task models fix (keras-team#1922)
* added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates
* fix for wrongly configured task models LLama, PaliGemma, Mistral and Phi3 + test
* comments
* un commented the test lines that were commented by mistake
* fixed linter errors
adding option strip_prompt to generate() (keras-team#1913)
* added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates
* un commented the test lines that were commented by mistake
* fixed linter errors
* added options strip_prompt to generate()
* fix for tensorflow: the compiled version of generate(strip_prompt=True) now works + code refactoring to make it more understandable
* added test for generate(strip_prompt=True)
* minor edits
Layout map for Llama (keras-team#1923)
* added test for the way BytePairTokenizer handles the \n\n sequence, which is important in Lama chat templates
* un commented the test lines that were commented by mistake
* fixed linter errors
* added default layout map for Llama
* minor fixes in tests
Update deeplab_v3_presets.py (keras-team#1924)
Add paths to get SAM weights from (keras-team#1925)
Two fixes for image resizing in preprocessing (keras-team#1927)
1. Properly display when are not resizing the input image in
`model.summary()`
2. Allow setting the `image_size` directly on a preprocessing layer.
2. is just to allow a more consistent way to set the input shape
across tasks. We now have:
```python
text_classifier = keras_hub.models.TextClassifer.from_preset(
"bert_base_en",
)
text_classifier.preprocessor.sequence_length = 256
image_classifier = keras_hub.models.TextClassifer.from_preset(
"bert_base_en",
)
image_classifier.preprocessor.image_size = (256, 256)
multi_modal_lm = keras_hub.models.CausalLM.from_preset(
"some_preset",
)
multi_modal_lm.preprocessor.sequence_length = 256
multi_modal_lm.preprocessor.image_size = (256, 256)
```
add back default image resizing (keras-team#1926)
Update deeplab_v3_presets.py (keras-team#1928)
* Update deeplab_v3_presets.py
* Update deeplab_v3_presets.py
Update PaliGemma to remove `include_rescaling` arg (keras-team#1917)
* update PaliGemma
* update conversion script
* fix GPU tests
fix path (keras-team#1929)
* fix path
* nit
Fix paligemma checkpoint conversion script (keras-team#1931)
* add back default image resizing
* fix bug in image converter
* fix paligemma checkpoint conversion file
* fix preset name
* remove debug code
* revert unintended changes
update preset path to point to latest version of models (keras-team#1932)
Update sdv3 path (keras-team#1934)
update sam docstring to show correct backbone in docstring (keras-team#1936)
Convert input dict to tensors during train_on_batch (keras-team#1919)
Register VGG presets. (keras-team#1935)
* register vgg preset
* nit
* nit
* nit
Add ResNetVD presets (keras-team#1897)
* Add ResNetVD presets
* Updated Kaggle handles
* Add weight conversion script for ResNet_vd
* Add usage
rebase conflict resolved
conflict resolve
Update sam_presets.py (keras-team#1940)
Update vit_det_backbone.py (keras-team#1941)
fix gpu test (keras-team#1939)
* fix gpu test
* cast input
* update dtype
* change to resnet preset
* remove arg
Added Support for Returning Attention Scores in TransformerEncoder call (keras-team#1879)
* Added: Return attention scores argument to transformer encoder
* Added: docstring for return_attention_scores and added a test to chek the working of the argument
* Fixed: Test case by removing print stmts and using self.assertAllEqual
* Fixed: Linting
Mark preset tests as large (keras-team#1942)
* fix tests
* fix test
* Update preset_utils_test.py
version bump to 0.17.0.dev0 (keras-team#1944)
Update stable_diffusion_3_presets.py (keras-team#1946)
[Semantic Segmentation] - Add SegFormer Architecture, Weight Conversion Script and Presets (keras-team#1883)
* initial commit - tf-based, kcv
* porting to keras_hub structure - removing aliases, presets, etc.
* enable instantiation of segformer backbone with custom MiT backbone
* remove num_classes from backbone
* fix input
* add imports to __init__
* update preset
* update docstrings
* add basic tests
* remove redundant imports
* update docstrings
* remove unused import
* running api_gen.py
* undo refactor of mit
* update docstrings
* add presets for mit
* add standin paths
* add presets for segformer backbone
* register presets in __init__.py
* addressing comments
* addressing comments
* addressing comments
* update most tests
* add remaining tests
* remove copyright
* fix test
* override from_config
* fix op in overlapping patching and embedding, start adding conversion utils
* style
* add padding to MiT patchingandembedding
* update to support other presets
* update conversin script
* fix link for b5
* add cityscapes weights
* update presets
* update presets
* update conversion script to make directories
* use save_preset
* change name of output dir
* add preprocessor flow
* api gen and add preprocessor to mits
* conform to new image classifier style
* format
* resizing image converter -> ImageConverter
* merge mit branch into segformer branch
* add preprocessor and converter
* address comments
* clarify backbone usage
* add conversion script
* numerical equivalence changes
* fix numerical inaccuracies
* update conversion script
* update conversion script
* remove transpose
* add preprocessor to segformer class
* fix preset path
* update test shape
* update presets
* update test shape
* expand docstrings
* add rescaling and normalization to preprocessor
* remove backbone presets, remove copyrights, remove backbone cls from segmenter
* remove copyright and unused import
* apply same transformation to masks as input images
* fix import
* fix shape in tests
Update readme (keras-team#1949)
* Update README.md
* Update README.md
Update llama_backbone.py docstring (keras-team#1950)
Update path (keras-team#1953)
Update preset path for keras.io.
There is no LLaMA2 in keras.io https://keras.io/api/keras_hub/models/llama2
This is the actual link:
https://keras.io/api/keras_hub/models/llama2
For Vicuna it does not have it's own model direcotry, since it is also the part of Llama,, updated the path.
Update SD3 init parameters (replacing `height`, `width` with `image_shape`) (keras-team#1951)
* Replace SD3 `height` and `width` with `image_shape`
* Update URI
* Revert comment
* Update SD3 handle
* Replace `height` and `width` with `image_shape`
* Update docstrings
* Fix CI
Update docstring (keras-team#1954)
AudioConverter is registered as "keras_hub.layers.WhisperAudioConverter" and not as part of models.
updated Mobilenet backbone to match it with torch implementation
timm script added
checkpoint conversion added
Refactoring