Removing "external" link tags (#510)

- prospective fix for translation serving failure

Author: joefernandez

site/en/gemma/docs/lora_tuning.ipynb

@@ -85,9 +85,9 @@
  "\n",
  "LLMs are extremely large in size (parameters in the order of billions). Full fine-tuning (which updates all the parameters in the model) is not required for most applications because typical fine-tuning datasets are relatively much smaller than the pre-training datasets.\n",
  "\n",
- "[Low Rank Adaptation (LoRA)](https://arxiv.org/abs/2106.09685){:.external} is a fine-tuning technique which greatly reduces the number of trainable parameters for downstream tasks by freezing the weights of the model and inserting a smaller number of new weights into the model. This makes training with LoRA much faster and more memory-efficient, and produces smaller model weights (a few hundred MBs), all while maintaining the quality of the model outputs.\n",
+ "[Low Rank Adaptation (LoRA)](https://arxiv.org/abs/2106.09685) is a fine-tuning technique which greatly reduces the number of trainable parameters for downstream tasks by freezing the weights of the model and inserting a smaller number of new weights into the model. This makes training with LoRA much faster and more memory-efficient, and produces smaller model weights (a few hundred MBs), all while maintaining the quality of the model outputs.\n",
  "\n",
- "This tutorial walks you through using KerasNLP to perform LoRA fine-tuning on a Gemma 2B model using the [Databricks Dolly 15k dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k){:.external}. This dataset contains 15,000 high-quality human-generated prompt / response pairs specifically designed for fine-tuning LLMs."
+ "This tutorial walks you through using KerasNLP to perform LoRA fine-tuning on a Gemma 2B model using the [Databricks Dolly 15k dataset](https://huggingface.co/datasets/databricks/databricks-dolly-15k). This dataset contains 15,000 high-quality human-generated prompt / response pairs specifically designed for fine-tuning LLMs."
  ]
  },
  {
@@ -109,7 +109,7 @@
  "\n",
  "To complete this tutorial, you will first need to complete the setup instructions at [Gemma setup](https://ai.google.dev/gemma/docs/setup). The Gemma setup instructions show you how to do the following:\n",
  "\n",
- "* Get access to Gemma on [kaggle.com](https://kaggle.com){:.external}.\n",
+ "* Get access to Gemma on [kaggle.com](https://kaggle.com).\n",
  "* Select a Colab runtime with sufficient resources to run\n",
  " the Gemma 2B model.\n",
  "* Generate and configure a Kaggle username and API key.\n",
@@ -333,7 +333,7 @@
  "source": [
  "## Load Model\n",
  "\n",
- "KerasNLP provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/){:.external}. In this tutorial, you'll create a model using `GemmaCausalLM`, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.\n",
+ "KerasNLP provides implementations of many popular [model architectures](https://keras.io/api/keras_nlp/models/). In this tutorial, you'll create a model using `GemmaCausalLM`, an end-to-end Gemma model for causal language modeling. A causal language model predicts the next token based on previous tokens.\n",
  "\n",
  "Create the model using the `from_preset` method:"
  ]
@@ -1001,8 +1001,8 @@
  "\n",
  "* Learn how to [generate text with a Gemma model](https://ai.google.dev/gemma/docs/get_started).\n",
  "* Learn how to perform [distributed fine-tuning and inference on a Gemma model](https://ai.google.dev/gemma/docs/distributed_tuning).\n",
- "* Learn how to [use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma){:.external}.\n",
- "* Learn how to [fine-tune Gemma using KerasNLP and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb){:.external}."
+ "* Learn how to [use Gemma open models with Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/open-models/use-gemma).\n",
+ "* Learn how to [fine-tune Gemma using KerasNLP and deploy to Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_kerasnlp_to_vertexai.ipynb)."
  ]
  }
  ],