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README.md

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   * **CodeGemma** is a collection of lightweight open code models built on top of Gemma  
   * **RecurrentGemma** is a family of open language models built on a novel recurrent architecture developed at Google  
   * **ShieldGemma** is a series of safety content moderation models built upon Gemma 2 that target four harm categories  
-* **[BERT](https://huggingface.co/collections/google/bert-release-64ff5e7a4be99045d1896dbc), [T5](https://huggingface.co/collections/google/t5-release-65005e7c520f8d7b4d037918), and [TimesFM](https://github.com/google-research/timesfm) Model Families**
-* **Author ML models with [MaxText](https://github.com/google/maxtext), [JAX](https://github.com/google/jax), [Keras](https://github.com/keras-team/keras), [Tensorflow](https://github.com/tensorflow/tensorflow), and [PyTorch/XLA](https://github.com/pytorch/xla)**
+* **[**BERT**](https://huggingface.co/collections/google/bert-release-64ff5e7a4be99045d1896dbc), [**T5**](https://huggingface.co/collections/google/t5-release-65005e7c520f8d7b4d037918), and [**TimesFM**](https://github.com/google-research/timesfm) Model Families**
+* **Author ML models with [**MaxText**](https://github.com/google/maxtext), [**JAX**](https://github.com/google/jax), [**Keras**](https://github.com/keras-team/keras), [**Tensorflow**](https://github.com/tensorflow/tensorflow), and [**PyTorch/XLA**](https://github.com/pytorch/xla)**
 
 ## Open Research and Community Resources
 
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 ## Partnership Highlights and Resources
 
-* Select Google Cloud CPU, GPU, or TPU options when setting up your **Hugging Face [Inference Endpoints](https://huggingface.co/blog/tpu-inference-endpoints-spaces) and Spaces**  
-* **Train and Deploy Hugging Face models** on Google Kubernetes Engine (GKE) and Vertex AI **directly from Hugging Face model landing pages or from Google Cloud Model Garden.**  
-* **Integrate [Colab](https://colab.research.google.com/) notebooks with Hugging Face Hub** via the [HF\_TOKEN secret manager integration](https://huggingface.co/docs/huggingface_hub/v0.23.3/en/quick-start#environment-variable) and transformers/huggingface\_hub pre-installs  
-* Leverage [**Hugging Face Deep Learning Containers (DLCs)**](https://cloud.google.com/deep-learning-containers/docs/choosing-container#hugging-face) for easy training and deployment of Hugging Face models on Google Cloud infrastructure.
+* Select Google Cloud CPU, GPU, or TPU options when setting up your **Hugging Face [**Inference Endpoints**](https://huggingface.co/blog/tpu-inference-endpoints-spaces) and Spaces**  
+* **Train and Deploy Hugging Face models** on Google Kubernetes Engine (GKE) and Vertex AI **directly from Hugging Face model landing pages or from Google Cloud Model Garden**  
+* **Integrate [**Colab**](https://colab.research.google.com/) notebooks with Hugging Face Hub** via the [HF\_TOKEN secret manager integration](https://huggingface.co/docs/huggingface_hub/v0.23.3/en/quick-start#environment-variable) and transformers/huggingface\_hub pre-installs  
+* Leverage [**Hugging Face Deep Learning Containers (DLCs)**](https://cloud.google.com/deep-learning-containers/docs/choosing-container#hugging-face) for easy training and deployment of Hugging Face models on Google Cloud infrastructure
 
 Read about our principles for responsible AI at [https://ai.google/responsibility/principles](https://ai.google/responsibility/principles/)