Announcing New Hugging Face and KerasHub integration

The Hugging Face Hub is a vast repository, currently hosting 750K+ public models, offering a diverse range of pre-trained models for various machine learning frameworks. Among these, 346,268 (as of the time of writing) models are built using the popular Transformers library. The KerasHub library recently added an integration with the Hub compatible with a first batch of 33 models.

In this first version, users of KerasHub were limited to only the KerasHub-based models available on the Hugging Face Hub.

from keras_hub.models import GemmaCausalLM

gemma_lm = GemmaCausalLM.from_preset(
    "hf://google/gemma-2b-keras"
)

They were able to train/fine-tune the model and upload it back to the Hub (notice that the model is still a Keras model).

model.save_to_preset("./gemma-2b-finetune")
keras_hub.upload_preset(
    "hf://username/gemma-2b-finetune",
    "./gemma-2b-finetune"
)

They were missing out on the extensive collection of over 300K models created with the transformers library. Figure 1 shows 4k Gemma models in the Hub.

Figure 1: Gemma Models in the Hugging Face Hub (Source:https://huggingface.co/models?other=gemma)

However, what if we told you that you can now access and use these 300K+ models with KerasHub, significantly expanding your model selection and capabilities?

from keras_hub.models import GemmaCausalLM

gemma_lm = GemmaCausalLM.from_preset(
    "hf://google/gemma-2b" # this is not a keras model!
)

We're thrilled to announce a significant step forward for the Hub community: Transformers and KerasHub now have a shared model save format. This means that models of the transformers library on the Hugging Face Hub can now also be loaded directly into KerasHub - immediately making a huge range of fine-tuned models available to KerasHub users. Initially, this integration focuses on enabling the use of Gemma (1 and 2), Llama 3, and PaliGemma models, with plans to expand compatibility to a wider range of architectures in the near future.