diff --git a/site/en/gemini-api/docs/model-tuning/python.ipynb b/site/en/gemini-api/docs/model-tuning/python.ipynb index 7549c123d..0aae0196c 100644 --- a/site/en/gemini-api/docs/model-tuning/python.ipynb +++ b/site/en/gemini-api/docs/model-tuning/python.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu" @@ -77,62 +77,6 @@ "## Setup" ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "SWxKvwd-MSIV" - }, - "source": [ - "### Authenticate" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JjS8Zy1ojIgc" - }, - "source": [ - "The Gemini API lets you tune models on your own data. Since it's your data and\n", - "your tuned models this needs stricter access controls than API-Keys can provide.\n", - "\n", - "Before you can run this tutorial, you'll need to\n", - "[setup OAuth for your project](https://ai.google.dev/gemini-api/docs/oauth).\n", - "\n", - "\n", - "In Colab the easiest wat to get setup is to copy the contents of your `client_secret.json` file into Colab's \"Secrets manager\" (under the key icon in the left panel) with the secret name `CLIENT_SECRET`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I6zTC-3mJ0-2" - }, - "source": [ - "This gcloud command turns the `client_secret.json` file into credentials that can be used to authenticate with the service.\n", - "\n", - "> Important: If you're running this in Colab, **don't just click the link it prints**. That will fail. Follow the instructions and copy the `gcloud` command it prints to your local machine and run it there, then paste the output from your local machine back here.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "9FUwyB_MJ0-2" - }, - "outputs": [], - "source": [ - "import os\n", - "if 'COLAB_RELEASE_TAG' in os.environ:\n", - " from google.colab import userdata\n", - " import pathlib\n", - " pathlib.Path('client_secret.json').write_text(userdata.get('CLIENT_SECRET'))\n", - "\n", - " # Use `--no-browser` in colab\n", - " !gcloud auth application-default login --no-browser --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'\n", - "else:\n", - " !gcloud auth application-default login --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'" - ] - }, { "cell_type": "markdown", "metadata": { @@ -144,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "id": "cbcf72bcb56d" }, @@ -164,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "8enrppafJPCX" }, @@ -173,6 +117,18 @@ "import google.generativeai as genai" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "lhqVUjH7ZKUi" + }, + "outputs": [], + "source": [ + "from google.colab import userdata\n", + "genai.configure(api_key=userdata.get('GOOGLE_API_KEY'))" + ] + }, { "cell_type": "markdown", "metadata": { @@ -184,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "id": "XyWzoYFxU4r6" }, @@ -193,11 +149,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "tunedModels/my-model-8527\n", - "tunedModels/my-model-7092\n", - "tunedModels/my-model-2778\n", - "tunedModels/my-model-1298\n", - "tunedModels/my-model-3883\n" + "tunedModels/number-generator-model-y330kq4bvrcv\n", + "tunedModels/number-generator-model-d16t2al75c46\n", + "tunedModels/number-generator-model-hte4uvo67uda\n", + "tunedModels/number-generator-model-7ale2efhsgif\n", + "tunedModels/number-generator-model-eoykb5s4p15s\n" ] } ], @@ -228,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "w-EBSe9wTbLB" }, @@ -236,21 +192,21 @@ { "data": { "text/plain": [ - "Model(name='models/gemini-1.0-pro-001',\n", + "Model(name='models/gemini-1.5-flash-001-tuning',\n", " base_model_id='',\n", " version='001',\n", - " display_name='Gemini 1.0 Pro',\n", - " description=('The best model for scaling across a wide range of tasks. This is a stable '\n", - " 'model that supports tuning.'),\n", - " input_token_limit=30720,\n", - " output_token_limit=2048,\n", + " display_name='Gemini 1.5 Flash 001 Tuning',\n", + " description='Fast and versatile multimodal model for scaling across diverse tasks',\n", + " input_token_limit=16384,\n", + " output_token_limit=8192,\n", " supported_generation_methods=['generateContent', 'countTokens', 'createTunedModel'],\n", - " temperature=0.9,\n", - " top_p=1.0,\n", - " top_k=1)" + " temperature=1.0,\n", + " max_temperature=2.0,\n", + " top_p=0.95,\n", + " top_k=64)" ] }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -258,13 +214,14 @@ "source": [ "base_model = [\n", " m for m in genai.list_models()\n", - " if \"createTunedModel\" in m.supported_generation_methods][0]\n", + " if \"createTunedModel\" in m.supported_generation_methods and\n", + " \"flash\" in m.name][0]\n", "base_model" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "baHjHh1oTTTC" }, @@ -342,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "id": "su64KgY4Uztj" }, @@ -350,26 +307,27 @@ { "data": { "text/plain": [ - "TunedModel(name='tunedModels/generate-num-2946',\n", - " source_model='models/gemini-1.0-pro-001',\n", - " base_model='models/gemini-1.0-pro-001',\n", + "TunedModel(name='tunedModels/generate-num-8122',\n", + " source_model='models/gemini-1.5-flash-001-tuning',\n", + " base_model='models/gemini-1.5-flash-001-tuning',\n", " display_name='',\n", " description='',\n", - " temperature=0.9,\n", - " top_p=1.0,\n", - " top_k=1,\n", + " temperature=1.0,\n", + " top_p=0.95,\n", + " top_k=64,\n", " state=,\n", - " create_time=datetime.datetime(2024, 2, 21, 20, 4, 16, 448050, tzinfo=datetime.timezone.utc),\n", - " update_time=datetime.datetime(2024, 2, 21, 20, 4, 16, 448050, tzinfo=datetime.timezone.utc),\n", - " tuning_task=TuningTask(start_time=datetime.datetime(2024, 2, 21, 20, 4, 16, 890698, tzinfo=datetime.timezone.utc),\n", + " create_time=datetime.datetime(2024, 10, 21, 21, 42, 55, 748537, tzinfo=datetime.timezone.utc),\n", + " update_time=datetime.datetime(2024, 10, 21, 21, 42, 55, 748537, tzinfo=datetime.timezone.utc),\n", + " tuning_task=TuningTask(start_time=None,\n", " complete_time=None,\n", " snapshots=[],\n", " hyperparameters=Hyperparameters(epoch_count=100,\n", " batch_size=4,\n", - " learning_rate=0.001)))" + " learning_rate=0.001)),\n", + " reader_project_numbers=None)" ] }, - "execution_count": 24, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -382,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "id": "EUodUwZkKPi-" }, @@ -393,7 +351,7 @@ "" ] }, - "execution_count": 25, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -422,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "id": "g08vqtxYLMxT" }, @@ -431,10 +389,10 @@ "data": { "text/plain": [ "total_steps: 375\n", - "tuned_model: \"tunedModels/generate-num-2946\"" + "tuned_model: \"tunedModels/generate-num-8122\"" ] }, - "execution_count": 26, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -454,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "id": "SOUowIv1HgSE" }, @@ -462,7 +420,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2aa2ed6548e24841a4a28ca9482b431b", + "model_id": "32da6e78695a43fe8dae58e9e6f55f76", "version_major": 2, "version_minor": 0 }, @@ -492,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "id": "oQuJ70_hqJi9" }, @@ -512,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "id": "bIiG57xWLhP7" }, @@ -523,13 +481,13 @@ "" ] }, - "execution_count": 29, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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\n", "text/plain": [ "
" ] @@ -562,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "id": "zO0YcuSyxydZ" }, @@ -573,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "id": "UwGrrj6hS_x2" }, @@ -587,7 +545,7 @@ "'56'" ] }, - "execution_count": 40, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -599,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "id": "YSNB2zjTx5SZ" }, @@ -613,7 +571,7 @@ "'123456'" ] }, - "execution_count": 41, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -625,7 +583,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "id": "Y2YVO-m0Ut9H" }, @@ -639,7 +597,7 @@ "'five'" ] }, - "execution_count": 46, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -651,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "id": "h2MkTR0uTb6U" }, @@ -665,7 +623,7 @@ "'cinq'" ] }, - "execution_count": 45, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -677,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "id": "OruCW1zETsZw" }, @@ -691,7 +649,7 @@ "'IV'" ] }, - "execution_count": 47, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -703,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "id": "thDdSuUDUJOx" }, @@ -717,7 +675,7 @@ "'八'" ] }, - "execution_count": 48, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -749,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "id": "9gAVuXT_wG3x" }, @@ -760,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "id": "d-c3YerBxVYs" }, @@ -774,7 +732,7 @@ "'This is my model.'" ] }, - "execution_count": 52, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -798,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "id": "cepfaUCvVGCo" }, @@ -818,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "id": "kN_bkut_4ayL" }, @@ -827,7 +785,14 @@ "name": "stdout", "output_type": "stream", "text": [ - ": 404 Tuned model tunedModels/generate-num-2946 does not exist.\n" + ": 404 GET https://generativelanguage.googleapis.com/v1beta/tunedModels/generate-num-8122?%24alt=json%3Benum-encoding%3Dint: Tuned model tunedModels/generate-num-8122 does not exist.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tornado.access:404 GET /v1beta/tunedModels/generate-num-8122?%24alt=json%3Benum-encoding%3Dint (127.0.0.1) 838.85ms\n" ] } ],