[Feature Request]: Modernize JAX/TPU Parallelism with Gemma 3 (Kaggle v5e-8 Support)

[Awesome075]

Description of the feature request:

Hi, I’ve noted that the [Gemma_1]data_parallel_inference_in_jax_tpu.ipynb is outdated, it rely on legacy Flax/Hugging Face classes that are being deprecated.

Furthermore, current environment constraints (Colab focusing on v5e-1/v6e-1 single-chip slices) mean the existing "multi-core" examples no longer run as intended in that environment.

I propose adding a new, modernized guide that:

Uses Gemma 3 with the Keras 3 JAX backend to leverage the 32k context window.

Provides a Kaggle-specific notebook to utilize the TPU v5e-8 (8-core) mesh for true data parallelism.

Demonstrates the Keras Distribution API, which is the current best practice for sharding JAX models across multiple TPU cores.

This update will ensure that users can still learn distributed JAX processing on available multi-core hardware (Kaggle) while using a future-proof technical stack.

What problem are you trying to solve with this feature?

Fix for outdated Notebook

Any other information you'd like to share?

No response

[Awesome075]

I have a working prototype for the Gemma 3 270M implementation on TPU v5e using the Keras 3 JAX backend. If this proposal aligns with the project goals, I would be happy to prepare and submit a Pull Request for review. Please let me know if you have specific preferences for the notebook's structure or any additional features you'd like to see included.

[CoderOggy78]

Great Suggestion Sir