Update to kerasHub package

Author: sachinprasadhs

site/en/gemma/docs/core/lora_tuning.ipynb

@@ -83,7 +83,7 @@
         "\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). This dataset contains 15,000 high-quality human-generated prompt / response pairs specifically designed for fine-tuning LLMs."
+        "This tutorial walks you through using KerasHub 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."
       ]
     },
     {
@@ -180,7 +180,7 @@
       "source": [
         "### Install dependencies\n",
         "\n",
-        "Install Keras, KerasNLP, and other dependencies."
+        "Install Keras, KerasHub, and other dependencies."
       ]
     },
     {
@@ -192,8 +192,8 @@
       "outputs": [],
       "source": [
         "# Install Keras 3 last. See https://keras.io/getting_started/ for more details.\n",
-        "!pip install -q -U keras-nlp\n",
-        "!pip install -q -U \"keras>=3\""
+        "!pip install -q -U keras-hub\n",
+        "!pip install -q -U keras"
       ]
     },
     {
@@ -230,7 +230,7 @@
       "source": [
         "### Import packages\n",
         "\n",
-        "Import Keras and KerasNLP."
+        "Import Keras and KerasHub."
       ]
     },
     {
@@ -242,7 +242,7 @@
       "outputs": [],
       "source": [
         "import keras\n",
-        "import keras_nlp"
+        "import keras_hub"
       ]
     },
     {
@@ -329,7 +329,7 @@
       "source": [
         "## Load Model\n",
         "\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",
+        "KerasHub provides implementations of many popular [model architectures](https://keras.io/api/keras_hub/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:"
       ]
@@ -466,7 +466,7 @@
         }
       ],
       "source": [
-        "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma2_2b_en\")\n",
+        "gemma_lm = keras_hub.models.GemmaCausalLM.from_preset(\"gemma2_2b_en\")\n",
         "gemma_lm.summary()"
       ]
     },
@@ -557,7 +557,7 @@
         "    instruction=\"What should I do on a trip to Europe?\",\n",
         "    response=\"\",\n",
         ")\n",
-        "sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)\n",
+        "sampler = keras_hub.samplers.TopKSampler(k=5, seed=2)\n",
         "gemma_lm.compile(sampler=sampler)\n",
         "print(gemma_lm.generate(prompt, max_length=256))"
       ]
@@ -912,7 +912,7 @@
         "    instruction=\"What should I do on a trip to Europe?\",\n",
         "    response=\"\",\n",
         ")\n",
-        "sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)\n",
+        "sampler = keras_hub.samplers.TopKSampler(k=5, seed=2)\n",
         "gemma_lm.compile(sampler=sampler)\n",
         "print(gemma_lm.generate(prompt, max_length=256))"
       ]
@@ -993,12 +993,12 @@
       "source": [
         "## Summary and next steps\n",
         "\n",
-        "This tutorial covered LoRA fine-tuning on a Gemma model using KerasNLP. Check out the following docs next:\n",
+        "This tutorial covered LoRA fine-tuning on a Gemma model using KerasHub. Check out the following docs next:\n",
         "\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/core/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).\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)."
+        "* Learn how to [fine-tune Gemma using KerasHub 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)."
       ]
     }
   ],