v0.18.1

Summary

• Minor bug fix point release.
  • Remove einops code from flux model.
  • Fix specifying dtype during task from_preset.

What's Changed

• Adding PaliGemma2 to KerasHub by @divyashreepathihalli in #1998
• Update pali_gemma_presets.py by @divyashreepathihalli in #2003
• Remove einops dep by @mattdangerw in #2006
• Fix dtype when creating a task with a task.json by @mattdangerw in #2007
• Version bump dev release by @mattdangerw in #2009
• Version bump release by @mattdangerw in #2010

Full Changelog: v0.18.0...v0.18.1

v0.18.0

Summary

• New Models.
  • PaliGemma 2: Better performing PaliGemma release based on Gemma 2.
  • SegFormer: Introduced the SegFormer architecture for SemanticSegmentation.
  • CLIP.
  • EfficientNet: Added EfficientNet presets, including the Edge and lite0 variants.
  • RetinaNet: Added an object detection task model.
  • Stable Diffusion: Added SD3.5 large and large turbo presets and flash attention support.
• HuggingFace integration.
  • All Keras team presets are now on both Kaggle and Huggingface hubs.

Breaking Changes.

• Updated initialization parameters for SD3, replacing height and width with image_shape.

What's Changed

• version bump to 0.17.0.dev0 by @divyashreepathihalli in #1944
• Update stable_diffusion_3_presets.py path by @divyashreepathihalli in #1946
• [Semantic Segmentation] - Add SegFormer Architecture, Weight Conversion Script and Presets by @DavidLandup0 in #1883
• Update readme by @divyashreepathihalli in #1949
• Update llama_backbone.py docstring by @divyashreepathihalli in #1950
• Update path for Llama by @sachinprasadhs in #1953
• Update SD3 init parameters (replacing height, width with image_shape) by @james77777778 in #1951
• Update docstring by @sachinprasadhs in #1954
• Add CLIP model by @james77777778 in #1955
• Add EfficientNet Presets by @pkgoogle in #1933
• Add SD3.5 large and large turbo presets by @james77777778 in #1960
• Mirror all weights on HF from Kaggle by @divyashreepathihalli in #1959
• [T5 1.1] Enable v1.1 Presets by @DavidLandup0 in #1948
• Update preset path for SD 3.5 and T5 1.1 by @divyashreepathihalli in #1961
• minor fix to HF mirror script by @divyashreepathihalli in #1962
• Add presets for CLIP and fix some minor bugs by @james77777778 in #1964
• sync models and update mirror script to sync model cards on HF and Kaggle by @divyashreepathihalli in #1971
• Bump the python group with 5 updates by @dependabot in #1969
• [MiT and SegFormer] Refactor Backbone Arg Names by @DavidLandup0 in #1958
• Correct model card links for Gemma variants by @RyanMullins in #1972
• [RetinaNet] Image Converter and ObjectDetector by @sineeli in #1906
• Improve future compatibility of CLIPMultiHeadAttention by @james77777778 in #1975
• Fix return_attention_scores bug by @abheesht17 in #1977
• Correct the kaggle handle by @sineeli in #1982
• Add Efficientnet Edge presets by @pkgoogle in #1976
• update docstring examples by @sachinprasadhs in #1970
• [Flux] Port Flux Core Model by @DavidLandup0 in #1864
• Add closest EfficientNet variants by @pkgoogle in #1967
• Sync HF <> Kaggle by @divyashreepathihalli in #1986
• EfficientNet: Add lite0 variant by @pkgoogle in #1968
• Update README.md by @mattdangerw in #1990
• Reduce the metadata we track per preset by @mattdangerw in #1991
• Temp fix for keras-hub testing by @mattdangerw in #1996
• Version bump to 0.18.0.dev0 by @divyashreepathihalli in #2001
• Skip failing JAX test by @divyashreepathihalli in #2000
• Version bump to 0.18.0 and cherry pick by @divyashreepathihalli in #2002

Full Changelog: v0.17.0...v0.18.0

v0.17.0

Summary

• 📢 KerasNLP and KerasCV are now becoming KerasHub 📢. KerasCV and KerasNLP have been consolidated into KerasHub package
• Models available now in KerasHub are albert, bart, bert, bloom, clip, csp_darknet, deberta_v3, deeplab_v3, densenet, distil_bert, efficientnet, electra, f_net, falcon, gemma, gpt2, gpt_neo_x, llama, llama3, mistral, mit, mobilenet, opt, pali_gemma, phi3, resnet, retinanet, roberta, sam, stable_diffusion_3, t5, vae, vgg, vit_det, whisper, xlm_roberta and xlnet.
• A new preprocessor flow has been added for vision and audio models

What's Changed

• Update python version in readme to 3.8 by @haifeng-jin in #618
• Modify our pip install line so we upgrade tf by @mattdangerw in #616
• Use Adam optimizer for quick start by @mattdangerw in #620
• Clean up class name and self in calls to super() by @mbrukman in #628
• Update word_piece_tokenizer.py by @ADITYADAS1999 in #617
• Add DeBERTaV3 Conversion Script by @abheesht17 in #633
• Add AlbertTokenizer and AlbertPreprocessor by @abheesht17 in #627
• Create Backbone base class by @jbischof in #621
• Add TPU testing by @chenmoneygithub in #591
• Add Base Preprocessor Class by @abheesht17 in #638
• Add keras_nlp.samplers by @chenmoneygithub in #563
• Add ALBERT Backbone by @abheesht17 in #622
• Add a small script to count parameters in our presets by @mattdangerw in #610
• Clean up examples/ directory by @ADITYADAS1999 in #637
• Fix Small BERT Typo by @abheesht17 in #651
• Rename examples/bert -> examples/bert_pretraining by @mattdangerw in #647
• Add FNet Preprocessor by @abheesht17 in #646
• Add FNet Backbone by @abheesht17 in #643
• Small DeBERTa Docstring Fixes by @abheesht17 in #666
• Add Fenced Docstring Testing by @abheesht17 in #640
• Corrected the epsilon value by @soma2000-lang in #665
• Consolidate docstring formatting weirdness in Backbone and Preprocessor base classes by @mattdangerw in #654
• Fix value_dim in TransformerDecoder's cross-attn layer by @abheesht17 in #667
• Add ALBERT Presets by @abheesht17 in #655
• Add Base Task Class by @abheesht17 in #671
• Implement TopP, TopK and Beam samplers by @chenmoneygithub in #652
• Add FNet Presets by @abheesht17 in #659
• Bump the year to 2023 by @mattdangerw in #679
• Add BART Backbone by @abheesht17 in #661
• Handle trainable and name in the backbone base class by @mattdangerw in #680
• Ignore Task Docstring for Testing by @abheesht17 in #683
• Light-weight benchmarking script by @NusretOzates in #664
• Conditionally import tf_text everywhere by @mattdangerw in #684
• Expose token_embedding as a Backbone Property by @abheesht17 in #676
• Move from_preset to base tokenizer classes by @shivance in #673
• add f_net_classifier and f_net_classifier_test by @ADITYADAS1999 in #670
• import rouge_scorer directly from rouge_score package by @sampathweb in #691
• Fix typo in requirements file juypter -> jupyter by @mattdangerw in #693
• Temporary fix to get nightly green again by @mattdangerw in #696
• GPT2 Text Generation APIs by @chenmoneygithub in #592
• Run keras saving tests on nightly and fix RobertaClassifier test by @mattdangerw in #692
• Speed up pip install keras-nlp; simplify deps by @mattdangerw in #697
• Add AlbertClassifier by @shivance in #668
• Make tokenizer, backbone, preprocessor properties settable on base class by @mattdangerw in #700
• Update to latest black by @mattdangerw in #708
• RobertaMaskedLM task and preprocessor by @mattdangerw in #653
• Default compilation for BERT/RoBERTa classifiers by @jbischof in #695
• Add start/end token padding to GPT2Preprocessor by @chenmoneygithub in #704
• Don't install tf stable when building our nightly image by @mattdangerw in #711
• Add OPT Backbone and Tokenizer by @mattdangerw in #699
• Small OPT Doc-string Edits by @abheesht17 in #716
• Default compilation other classifiers by @Plutone11011 in #714
• Add BartTokenizer and BART Presets by @abheesht17 in #685
• Add an add_prefix_space Arg in BytePairTokenizer by @shivance in #715
• Opt presets by @mattdangerw in #707
• fix import of tensorflow_text in tf_utils by @sampathweb in #723
• Check for masked token in roberta tokenizer by @mattdangerw in #742
• Improve test coverage for special tokens in model tokenizers by @mattdangerw in #743
• Fix the sampler truncation strategy by @chenmoneygithub in #713
• Add ALBERT Conversion Script by @abheesht17 in #736
• Add FNet Conversion Script by @abheesht17 in #737
• Add BART Conversion Script by @abheesht17 in #739
• Pass Correct LayerNorm Epsilon value to TransformerEncoder in Backbones by @TheAthleticCoder in #731
• Improving the layer Description. by @Neeshamraghav012 in #734
• Adding ragged support to SinePositionEncoding by @apupneja in #751
• Fix trailing space by @mattdangerw in #755
• Adding an AlbertMaskedLM task + Fix Projection layer dimension in MaskedLMHead by @shivance in #725
• New docstring example for TokenAndPosition Embedding layer. by @Neeshamraghav012 in #760
• Add a note for TPU issues for deberta_v3 by @mattdangerw in #758
• Add missing exports to models API by @mattdangerw in #763
• Autogenerate preset table by @Cyber-Machine in #690
• Version bump to 0.5.0 by @mattdangerw in #767
• Adding a FNetMaskedLM task model and preprocessor by @apupneja in #740
• Add a DistilBertMaskedLM task model by @ADITYADAS1999 in #724
• Add cache support to decoding journey by @chenmoneygithub in #745
• Handle [MASK] token in DebertaV3Tokenizer by @abheesht17 in #759
• Update README for 2.4.1 release by @mattdangerw in #757
• Fix typo in test docstring by @jbischof in #791
• Fixed Incorrect Links for FNet and DeBERTaV3 models by @Cyber-Machine in #793
• Patch 1 - doc-string spell fix by @atharvapurdue in #781
• Don't rely on core keras initializer config details by @mattdangerw in #802
• Simplify the cache decoding graph by @mattdangerw in #780
• Fix Fenced Doc-String #782 by @atharvapurdue in #785
• Solve #721 Deberta masklm model by @Plutone11011 in #732
• Add from_config to sampler by @mattdangerw in #803
• BertMaskedLM Task Model and Preprocessor by @Cyber-Machine in #774
• Stop generation once end_t...

v0.16.0.dev0

Summary

• 📢 KerasNLP and KerasCV are now becoming KerasHub 📢. KerasCV and KerasNLP have been consolidated into KerasHub package
• Models available now in KerasHub are albert, bart, bert, bloom, clip, csp_darknet, deberta_v3, deeplab_v3, densenet, distil_bert, efficientnet, electra, f_net, falcon, gemma, gpt2, gpt_neo_x, llama, llama3, mistral, mit, mobilenet, opt, pali_gemma, phi3, resnet, retinanet, roberta, sam, stable_diffusion_3, t5, vae, vgg, vit_det, whisper, xlm_roberta and xlnet.
• A new preprocessor flow has been added for vision and audio models

What's Changed

• Update python version in readme to 3.8 by @haifeng-jin in #618
• Modify our pip install line so we upgrade tf by @mattdangerw in #616
• Use Adam optimizer for quick start by @mattdangerw in #620
• Clean up class name and self in calls to super() by @mbrukman in #628
• Update word_piece_tokenizer.py by @ADITYADAS1999 in #617
• Add DeBERTaV3 Conversion Script by @abheesht17 in #633
• Add AlbertTokenizer and AlbertPreprocessor by @abheesht17 in #627
• Create Backbone base class by @jbischof in #621
• Add TPU testing by @chenmoneygithub in #591
• Add Base Preprocessor Class by @abheesht17 in #638
• Add keras_nlp.samplers by @chenmoneygithub in #563
• Add ALBERT Backbone by @abheesht17 in #622
• Add a small script to count parameters in our presets by @mattdangerw in #610
• Clean up examples/ directory by @ADITYADAS1999 in #637
• Fix Small BERT Typo by @abheesht17 in #651
• Rename examples/bert -> examples/bert_pretraining by @mattdangerw in #647
• Add FNet Preprocessor by @abheesht17 in #646
• Add FNet Backbone by @abheesht17 in #643
• Small DeBERTa Docstring Fixes by @abheesht17 in #666
• Add Fenced Docstring Testing by @abheesht17 in #640
• Corrected the epsilon value by @soma2000-lang in #665
• Consolidate docstring formatting weirdness in Backbone and Preprocessor base classes by @mattdangerw in #654
• Fix value_dim in TransformerDecoder's cross-attn layer by @abheesht17 in #667
• Add ALBERT Presets by @abheesht17 in #655
• Add Base Task Class by @abheesht17 in #671
• Implement TopP, TopK and Beam samplers by @chenmoneygithub in #652
• Add FNet Presets by @abheesht17 in #659
• Bump the year to 2023 by @mattdangerw in #679
• Add BART Backbone by @abheesht17 in #661
• Handle trainable and name in the backbone base class by @mattdangerw in #680
• Ignore Task Docstring for Testing by @abheesht17 in #683
• Light-weight benchmarking script by @NusretOzates in #664
• Conditionally import tf_text everywhere by @mattdangerw in #684
• Expose token_embedding as a Backbone Property by @abheesht17 in #676
• Move from_preset to base tokenizer classes by @shivance in #673
• add f_net_classifier and f_net_classifier_test by @ADITYADAS1999 in #670
• import rouge_scorer directly from rouge_score package by @sampathweb in #691
• Fix typo in requirements file juypter -> jupyter by @mattdangerw in #693
• Temporary fix to get nightly green again by @mattdangerw in #696
• GPT2 Text Generation APIs by @chenmoneygithub in #592
• Run keras saving tests on nightly and fix RobertaClassifier test by @mattdangerw in #692
• Speed up pip install keras-nlp; simplify deps by @mattdangerw in #697
• Add AlbertClassifier by @shivance in #668
• Make tokenizer, backbone, preprocessor properties settable on base class by @mattdangerw in #700
• Update to latest black by @mattdangerw in #708
• RobertaMaskedLM task and preprocessor by @mattdangerw in #653
• Default compilation for BERT/RoBERTa classifiers by @jbischof in #695
• Add start/end token padding to GPT2Preprocessor by @chenmoneygithub in #704
• Don't install tf stable when building our nightly image by @mattdangerw in #711
• Add OPT Backbone and Tokenizer by @mattdangerw in #699
• Small OPT Doc-string Edits by @abheesht17 in #716
• Default compilation other classifiers by @Plutone11011 in #714
• Add BartTokenizer and BART Presets by @abheesht17 in #685
• Add an add_prefix_space Arg in BytePairTokenizer by @shivance in #715
• Opt presets by @mattdangerw in #707
• fix import of tensorflow_text in tf_utils by @sampathweb in #723
• Check for masked token in roberta tokenizer by @mattdangerw in #742
• Improve test coverage for special tokens in model tokenizers by @mattdangerw in #743
• Fix the sampler truncation strategy by @chenmoneygithub in #713
• Add ALBERT Conversion Script by @abheesht17 in #736
• Add FNet Conversion Script by @abheesht17 in #737
• Add BART Conversion Script by @abheesht17 in #739
• Pass Correct LayerNorm Epsilon value to TransformerEncoder in Backbones by @TheAthleticCoder in #731
• Improving the layer Description. by @Neeshamraghav012 in #734
• Adding ragged support to SinePositionEncoding by @apupneja in #751
• Fix trailing space by @mattdangerw in #755
• Adding an AlbertMaskedLM task + Fix Projection layer dimension in MaskedLMHead by @shivance in #725
• New docstring example for TokenAndPosition Embedding layer. by @Neeshamraghav012 in #760
• Add a note for TPU issues for deberta_v3 by @mattdangerw in #758
• Add missing exports to models API by @mattdangerw in #763
• Autogenerate preset table by @Cyber-Machine in #690
• Version bump to 0.5.0 by @mattdangerw in #767
• Adding a FNetMaskedLM task model and preprocessor by @apupneja in #740
• Add a DistilBertMaskedLM task model by @ADITYADAS1999 in #724
• Add cache support to decoding journey by @chenmoneygithub in #745
• Handle [MASK] token in DebertaV3Tokenizer by @abheesht17 in #759
• Update README for 2.4.1 release by @mattdangerw in #757
• Fix typo in test docstring by @jbischof in #791
• Fixed Incorrect Links for FNet and DeBERTaV3 models by @Cyber-Machine in #793
• Patch 1 - doc-string spell fix by @atharvapurdue in #781
• Don't rely on core keras initializer config details by @mattdangerw in #802
• Simplify the cache decoding graph by @mattdangerw in #780
• Fix Fenced Doc-String #782 by @atharvapurdue in #785
• Solve #721 Deberta masklm model by @Plutone11011 in #732
• Add from_config to sampler by @mattdangerw in #803
• BertMaskedLM Task Model and Preprocessor by @Cyber-Machine in #774
• Stop generation once en...

v0.15.1

Summary

Bug fix patch release.

• Always run tf preprocessing on CPU.
• Fix running preprocessing outside the main python thread.
• Fix loading classifiers with the "old name" of XXClasssifier as XXTextClassifier.
• Restore support for bytestring to tokenizers and other preprocessing layers as strings.

What's Changed

• Version bump for pre-release by @mattdangerw in #1842
• V0.15.1.dev1 by @mattdangerw in #1844
• Version bump for 0.15.1 release by @mattdangerw in #1845

Full Changelog: v0.15.0...v0.15.1

v0.15.0

Summary

📢 KerasNLP is becoming KerasHub 📢, read more about it here.

This release contains a number of feature improvements:

• Added int8 quantization support.
  • Use the quantize() method to quantize any model.
  • Llama 2 and Llama 3 pre-quantized presets are available.
• PaliGemmaCausalLM will automatically resize input images during preprocessing.
• Added more converters for hugginface/transformers checkpoints.
  • Gemma 2, PaliGemma, GPT2, Bert, Albert, DistilBert, Bart.
• Class detection for huggingface/transformers checkpoints.
  • Call from_preset() on a base class, and we will find the correct subclass to create.
• Added Vicuna presets.
• Alias Classifier as TextClassifier, BertClassifier as BertTextClassifier.
• Added tokenizer.special_tokens and tokenizer.special_token_ids as convenient properties to view all special tokens on a pretrained tokenizer.

# Quantize an unquantized model.
lm = keras_nlp.models.CausalLM.from_preset(
    "gemma2_instruct_2b_en",
    dtype="bfloat16",
)
lm.quantize("int8")
# Load a pre-quantized model.
lm = keras_nlp.models.CausalLM.from_preset(
    "llama3_instruct_8b_en_int8",
    dtype="bfloat16",
)
# Convert a bert model in the huggingface/transformers format.
classifier = keras_nlp.models.TextClassifier.from_preset(
    "hf://google-bert/bert-base-uncased",
    num_classes=2,
)
# View all special tokens.
print(classifier.preprocessor.tokenizer.special_tokens)
print(classifier.preprocessor.tokenizer.special_token_ids)

Breaking changes

• On all backends, all strings and ragged output will be returned as python strings or python lists respectively.
  • This include preprocessing methods like tokenize() and detokenize().
  • This may break code that depended on tf.Tensor output on the tensorflow backend, but will lead to consistent output on all backends, which we believe will be an overall improvement.
  • Preprocessing layers can still always be included in a tf.data preprocessing pipeline, on any backend.

What's Changed

• Version bump to 0.14.0.dev0 by @grasskin in #1675
• Revert "Version bump to 0.14.0.dev0" by @grasskin in #1676
• Remove Keras pin, fix tests by @mattdangerw in #1681
• Add quantization support for Gemma, Gemma2 and PaliGemma by @james77777778 in #1670
• add vicuna preset by @sineeli in #1672
• Porting Gemma 2 transformers checkpoint by @ariG23498 in #1678
• Improve CI speed and resolve issues of run_quantization_check by @james77777778 in #1682
• Remove build_from_signature from MHA layers by @mattdangerw in #1687
• Refactoring: in CachedMultiHeadAttention call MHA methods instead of recoding the attention calculation by @apehex in #1684
• Porting PaliGemma transformers checkpoint by @ariG23498 in #1686
• Allow importing keras_nlp without tensorflow by @mattdangerw in #1660
• Add flag to gemma conversion script to specify local orbax by @mattdangerw in #1688
• Fix compatibility for earlier versions of Keras by @james77777778 in #1690
• Add a test against keras-nightly by @mattdangerw in #1693
• Fix dtype bugs in ReversibleEmbedding and LayerNorm by @james77777778 in #1692
• Partially revert #1687 by @mattdangerw in #1695
• Fix quantization test for XLNet by @james77777778 in #1699
• Add a HF BERT converter, improve safetensor loading by @mattdangerw in #1694
• Add a subtle fix for gemma 2 conversions by @mattdangerw in #1701
• One more small Gemma conversion fix by @mattdangerw in #1702
• Slightly more defensive handling of type for backbone by @mattdangerw in #1703
• Add support for converting Gemma 2 checkpoints by @mattdangerw in #1700
• Make it clearer what is running in the github action UI by @mattdangerw in #1707
• Try upgrading tensorflow pin by @mattdangerw in #1706
• Bump version to fix query norm in Gemma 2 9b by @mattdangerw in #1709
• Gemma: Add logit soft-capping to score function. by @RyanMullins in #1712
• Version bump HEAD to 0.15 by @mattdangerw in #1713
• Port gpt2 transformers checkpoint by @cosmo3769 in #1704
• Add soft capping to reversible embedding layer by @mattdangerw in #1718
• Add presets for gemma 2 2b by @mattdangerw in #1721
• Utilize to_numpy=True in quantize if available by @james77777778 in #1725
• Dynamic int8 quantization for Llama2 and Llama3 by @james77777778 in #1720
• Bump the python group with 2 updates by @dependabot in #1726
• Shield gemma shortnames by @mattdangerw in #1731
• Sliding window fixes by @mattdangerw in #1738
• Add int8 models to Llama2 and Llama3 by @james77777778 in #1734
• Port distilbert transformer checkpoint by @cosmo3769 in #1736
• Add support of kwargs to Backbone.from_preset and fix the dtype forwarding in Task.from_preset by @james77777778 in #1742
• Remove src init file contents by @mattdangerw in #1743
• Remove ROADMAP.md by @mattdangerw in #1773
• Fix nested list in args on keras.io by @mattdangerw in #1772
• Remove stale tf only examples by @mattdangerw in #1771
• Limit the default sequence length to 1024 for all models by @mattdangerw in #1770
• Consistent preprocessing output on all backends by @mattdangerw in #1777
• Port albert transformer checkpoint by @cosmo3769 in #1767
• Lower the default learning rate for albert by @mattdangerw in #1786
• Port bart transformer checkpoint by @cosmo3769 in #1783
• Add an option to disable default compilation by @mattdangerw in #1787
• Port mistral transformer checkpoint by @cosmo3769 in #1768
• [Bart]Fix missing weight port by @cosmo3769 in #1789
• Remove python 3.8 version in setup.py by @mattdangerw in #1792
• Class detection works for huggingface checkpoints by @mattdangerw in #1800
• Rename KerasNLP symbols for a multi-modal future by @mattdangerw in #1803
• Move preprocessing to base classes by @mattdangerw in #1807
• Add add_bos=False, add_eos=False to SentencePieceTokenizer.init() by @briango28 in #1811
• Only load a full task config when load_task_extras is passed by @mattdangerw in #1812
• Add image and audio converter classes by @mattdangerw in #1813
• Simplify registering "built-in" presets by @mattdangerw in #1818
• Support image and audio information in task summaries by @mattdangerw in #1819
• Take two of #1812, simpler classifier head loading by @mattdangerw in #1823
• Remove preprocessing layers we no longer use by @mattdangerw in #1824
• Version bump for dev release by @mattdangerw in #1825
• Version bump for dev release by @mattdangerw in #1830
• Version bump for 0.15.0 release by @mattdangerw in #1832

New Contributors

• @apehex made their first contribution in #1684
• @cosmo3769 made their first contribution in #1704

Full Changelog: v0.14.4...v0.15.0

v0.14.4

Summary

• Fix issues with Gemma 2 sliding window.
• Fix TensorFlow backend Gemma 2 generation.

What's Changed

• Sliding window fixes by @mattdangerw in #1738
• version bump by @mattdangerw in #1740
• version bump by @mattdangerw in #1741

Full Changelog: v0.14.3...v0.14.4

v0.14.3

Summary

• Short names for shield gemma checkpoints.

keras_nlp.models.GemmaCausalLM.from_preset("shieldgemma_2b_en")

What's Changed

• Version bump dev release by @mattdangerw in #1732
• Version bump for release by @mattdangerw in #1733

Full Changelog: v0.14.2...v0.14.3

v0.14.2

Summary

• Add Gemma 2 2b.
• Fixes for logit softcapping.

What's Changed

• Version bump 0.14.2.dev0 by @mattdangerw in #1719
• Bump pypi action version by @mattdangerw in #1722
• version bump by @mattdangerw in #1723
• Version bump 0.14.2 by @mattdangerw in #1724

Full Changelog: v0.14.1...v0.14.2

v0.14.1

Summary

• Update Gemma 2 9b to fix minor config error.

What's Changed

• Bump version to fix query norm in Gemma 2 9b by @mattdangerw in #1709
• Version bump 0.14.1.dev0 by @mattdangerw in #1714

Full Changelog: v0.14.0...v0.14.1