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Update keras-nlp to keras-hub #584

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c977d78
Created using Colab
sachinprasadhs May 6, 2025
3eaee3e
change from keras_nlp to keras_hub
sachinprasadhs May 12, 2025
46ba6a8
format notebook
sachinprasadhs May 12, 2025
66a7ab5
remove keras-nlp
sachinprasadhs May 29, 2025
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127 changes: 90 additions & 37 deletions site/en/gemma/docs/codegemma/keras_quickstart.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@
},
{
"cell_type": "code",
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"execution_count": 1,
"metadata": {
"cellView": "form",
"id": "tuOe1ymfHZPu"
Expand Down Expand Up @@ -88,7 +88,7 @@
"* A 7B instruction-tuned code model\n",
"* A 2B model, trained specifically for code infilling and open-ended generation.\n",
"\n",
"This guide walks you through using the CodeGemma 2B model with KerasNLP for a code completion task.\n"
"This guide walks you through using the CodeGemma 2B model with KerasHub for a code completion task.\n"
]
},
{
Expand Down Expand Up @@ -161,7 +161,7 @@
},
{
"cell_type": "code",
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"execution_count": 2,
"metadata": {
"id": "DrBoa_Urw9Vx"
},
Expand All @@ -185,13 +185,24 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {
"id": "KWOQ2sJocj-w"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m792.1/792.1 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m53.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install -q -U keras-nlp"
"!pip install -q -U keras-hub\n",
"!pip install -q -U keras"
]
},
{
Expand All @@ -216,7 +227,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {
"id": "ww83zI9ToPso"
},
Expand All @@ -233,18 +244,18 @@
"source": [
"### Import packages\n",
"\n",
"Import Keras and KerasNLP."
"Import Keras and KerasHub."
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {
"id": "oQkqsyE1a2YD"
},
"outputs": [],
"source": [
"import keras_nlp\n",
"import keras_hub\n",
"import keras\n",
"\n",
"# Run at half precision.\n",
Expand All @@ -259,30 +270,72 @@
"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",
"KerasHub 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",
"\n",
"Create the model using the `from_preset` method:"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {
"id": "yygIK9DEIldp"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/2/download/config.json...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 785/785 [00:00<00:00, 1.64MB/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/2/download/model.weights.h5...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4.67G/4.67G [00:50<00:00, 99.2MB/s]\n"
]
},
{
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"output_type": "stream",
"text": [
"Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/2/download/tokenizer.json...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 591/591 [00:00<00:00, 946kB/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/2/download/assets/tokenizer/vocabulary.spm...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/config.json...\n",
"100%|██████████| 554/554 [00:00<00:00, 1.41MB/s]\n",
"Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/model.weights.h5...\n",
"100%|██████████| 4.67G/4.67G [05:06<00:00, 16.4MB/s]\n",
"Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/tokenizer.json...\n",
"100%|██████████| 401/401 [00:00<00:00, 382kB/s]\n",
"Downloading from https://www.kaggle.com/api/v1/models/keras/codegemma/keras/code_gemma_2b_en/1/download/assets/tokenizer/vocabulary.spm...\n",
"100%|██████████| 4.04M/4.04M [00:01<00:00, 2.41MB/s]\n"
"100%|██████████| 4.04M/4.04M [00:00<00:00, 43.1MB/s]\n"
]
},
{
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Tokenizer (type) </span>┃<span style=\"font-weight: bold\"> Vocab # </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
"└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Config </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ gemma_tokenizer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GemmaTokenizer</span>) │ Vocab size: <span style=\"color: #00af00; text-decoration-color: #00af00\">256,000</span> │\n",
"└───────────────────────────────────────────────────────────────┴──────────────────────────────────────────┘\n",
"</pre>\n"
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mTokenizer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Vocab #\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m) │ \u001b[38;5;34m256,000\u001b[0m │\n",
"└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n"
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Config\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ gemma_tokenizer (\u001b[38;5;33mGemmaTokenizer\u001b[0m) │ Vocab size: \u001b[38;5;34m256,000\u001b[0m │\n",
"└───────────────────────────────────────────────────────────────┴──────────────────────────────────────────┘\n"
]
},
"metadata": {},
Expand Down Expand Up @@ -410,7 +463,7 @@
}
],
"source": [
"gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"code_gemma_2b_en\")\n",
"gemma_lm = keras_hub.models.GemmaCausalLM.from_preset(\"code_gemma_2b_en\")\n",
"gemma_lm.summary()"
]
},
Expand Down Expand Up @@ -448,7 +501,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {
"id": "tGby-fi8n-Hv"
},
Expand All @@ -471,7 +524,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {
"id": "k1ousdBnr2j8"
},
Expand Down Expand Up @@ -507,7 +560,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {
"id": "N7UlgjSt5QnF"
},
Expand Down Expand Up @@ -542,7 +595,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {
"id": "aae5GHrdpj2_"
},
Expand All @@ -556,7 +609,7 @@
"'<|fim_prefix|>import <|fim_suffix|>if __name__ == \"__main__\":\\n sys.exit(0)<|fim_middle|>sys\\n<|file_separator|>'"
]
},
"execution_count": 12,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
Expand All @@ -582,7 +635,7 @@
"source": [
"## Summary\n",
"\n",
"This tutorial walked you through using CodeGemma to infill code based on the surrounding context. Next, check out the [AI Assisted Programming with CodeGemma and KerasNLP notebook](https://ai.google.dev/gemma/docs/codegemma/code_assist_keras) for more examples on how you can use CodeGemma.\n",
"This tutorial walked you through using CodeGemma to infill code based on the surrounding context. Next, check out the [AI Assisted Programming with CodeGemma and KerasHub notebook](https://ai.google.dev/gemma/docs/codegemma/code_assist_keras) for more examples on how you can use CodeGemma.\n",
"\n",
"Also refer to The [CodeGemma model card](https://ai.google.dev/gemma/docs/codegemma/model_card) for the technical specs of the CodeGemma models.\n"
]
Expand Down
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