Now you can publish your fine-tuned models directly from the Keras API to either Kaggle or Hugging Face
Overview
This article discusses how to publish Keras models on Kaggle and Hugging Face, highlighting the ease of sharing fine-tuned models with the community. It provides step-by-step instructions for uploading models, the advantages of using Keras, and showcases popular models and competitions on Kaggle.
What You'll Learn
1
How to upload fine-tuned Keras models to Kaggle
2
How to load models from Kaggle using KerasNLP
3
Why Keras is the preferred model format for Kaggle
4
How to publish Keras models on Hugging Face
Key Questions Answered
How can I upload my Keras model to Kaggle?
To upload your Keras model to Kaggle, you need to save your fine-tuned model as a KerasNLP preset and use the upload function with the Kaggle URI format. This allows others to load your model using the same .from_preset() API call.
What are the benefits of using Keras for model uploads?
Keras provides a consistent user experience with its .from_preset() API for loading models. It also ensures that models have a familiar API for fine-tuning and that the source code is always in kerasCV or kerasNLP, making it easy to use and understand.
How do I upload my Keras model to Hugging Face?
To upload your Keras model to Hugging Face, save your model as a KerasNLP preset and use the upload function with the 'hf://' URI format. This process is similar to uploading to Kaggle, with the main difference being the URI prefix.
What is the usability rating feature on Kaggle?
The usability rating feature on Kaggle helps users identify which model details are missing, allowing them to improve their model's visibility and appreciation within the community. This encourages better documentation and model sharing.
Key Statistics & Figures
Number of pre-trained models on Kaggle Models
~4,000
This indicates the growing repository of models available for users to explore and utilize.
Gemma downloads on Kaggle
more than 2x more downloads in Keras than in all other formats combined
This showcases the popularity and user preference for Keras models over other formats.
KerasNLP starter notebooks copied
more than 950 times
This reflects the usefulness of the KerasNLP resources provided for Kaggle competitions.
Technologies & Tools
Some links below are affiliate links. We may earn a commission if you make a purchase.
Machine Learning Framework
Keras
Used for building and training models, as well as for uploading and sharing them on Kaggle and Hugging Face.
Machine Learning Library
Kerasnlp
Facilitates the loading, fine-tuning, and uploading of NLP models.
Key Actionable Insights
1Utilize the KerasNLP library to streamline your model uploads to Kaggle and Hugging Face.By leveraging the built-in functions for saving and uploading models, you can save time and ensure a consistent experience for users accessing your models.
2Make sure to provide comprehensive descriptions and details about your dataset when uploading models.This enhances the discoverability of your models on Kaggle and Hugging Face, allowing other users to understand the context and potential applications of your work.
3Engage with the Kaggle community by sharing your fine-tuned models and participating in competitions.This not only helps you gain visibility but also allows you to receive feedback and improve your skills through collaboration.
Common Pitfalls
1
Failing to provide adequate model descriptions and details can lead to poor visibility.
When users upload models without sufficient context, it becomes challenging for others to understand their purpose and applications, reducing the likelihood of engagement.
Related Concepts
Model Fine-tuning
Kaggle Competitions
Hugging Face Model Sharing
Kerasnlp Functionalities