finetuneLLaMa-2#1.ipynb

{
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   "id": "af2ee7fb-e888-4e38-a349-c7c40dfd2963",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "source": [
    "# Fine-tune LLaMA 2 models on SageMaker JumpStart #1: Deploying & Invoking LLaMa-2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0679163a-387a-4ba6-8ce0-d5571614c0dc",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "source": [
    "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
    "\n",
    "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-west-2/introduction_to_amazon_algorithms|jumpstart-foundation-models|llama-2-text-completion.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251624f9-1eb6-4051-a774-0a4ba83cabf5",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "source": [
    "---\n",
    "In this demo notebook, we demonstrate how to use the SageMaker Python SDK to deploy pre-trained Llama 2 model as well as fine-tune it for your dataset in domain adaptation or instruction tuning format.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d9b99d-639b-40f3-91e3-1fe00ee032a4",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "source": [
    "### Model License information\n",
    "---\n",
    "To perform inference on these models, you need to pass custom_attributes='accept_eula=true' as part of header. This means you have read and accept the end-user-license-agreement (EULA) of the model. EULA can be found in model card description or from https://ai.meta.com/resources/models-and-libraries/llama-downloads/. By default, this notebook sets custom_attributes='accept_eula=false', so all inference requests will fail until you explicitly change this custom attribute.\n",
    "\n",
    "Note: Custom_attributes used to pass EULA are key/value pairs. The key and value are separated by '=' and pairs are separated by ';'. If the user passes the same key more than once, the last value is kept and passed to the script handler (i.e., in this case, used for conditional logic). For example, if 'accept_eula=false; accept_eula=true' is passed to the server, then 'accept_eula=true' is kept and passed to the script handler.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019c4fcd-d6c5-4381-8425-1d224c0ac197",
   "metadata": {
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "source": [
    "### Set up\n",
    "\n",
    "---\n",
    "We begin by installing and upgrading necessary packages. Restart the kernel after executing the cell below for the first time.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85addd9d-ec89-44a7-9fb5-9bc24fe9993b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[33mDEPRECATION: pyodbc 4.0.0-unsupported has a non-standard version number. pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pyodbc or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade sagemaker datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13274b9b-87bd-4090-a6aa-294570c31e0e",
   "metadata": {},
   "source": [
    "## Deploy Pre-trained Model\n",
    "\n",
    "---\n",
    "\n",
    "First we will deploy the Llama-2 model as a SageMaker endpoint. To train/deploy 13B and 70B models, please change model_id to \"meta-textgeneration-llama-2-7b\" and \"meta-textgeneration-llama-2-70b\" respectively.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e756bd54",
   "metadata": {
    "jumpStartAlterations": [
     "modelIdVersion"
    ],
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_id, model_version = \"meta-textgeneration-llama-2-7b\", \"*\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "319f5108-9cc5-4bb1-a2f9-1b39fd89fa92",
   "metadata": {},
   "source": [
    "If you are deploying the model for the first time, make sure to follow the code below to deploy the model endpoint and then use it to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1722b230-b7bc-487f-b4ee-98ca42848423",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sagemaker.jumpstart.model import JumpStartModel\n",
    "\n",
    "# pretrained_model = JumpStartModel(model_id=model_id)\n",
    "# pretrained_predictor = pretrained_model.deploy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5034e450-254d-431e-8e32-4def47e20fef",
   "metadata": {},
   "source": [
    "If you have already deployed the model and do not wish to deploy again, use the code below to utilize the existing endpoint to make predictions based on the user query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e01bcc7-0ae3-4e67-9b06-aaf7ab0a081e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /root/.config/sagemaker/config.yaml\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The endpoint attribute has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
     ]
    }
   ],
   "source": [
    "from sagemaker.predictor import Predictor\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "from sagemaker.deserializers import JSONDeserializer\n",
    "\n",
    "# Use the existing endpoint name\n",
    "endpoint_name = \"meta-textgeneration-llama-2-7b-2023-09-24-19-57-25-271\"  # Replace with your endpoint name\n",
    "\n",
    "# Create a SageMaker predictor object\n",
    "pretrained_predictor = Predictor(\n",
    "    endpoint_name=endpoint_name,\n",
    "    serializer=JSONSerializer(),\n",
    "    deserializer=JSONDeserializer(),\n",
    ")\n",
    "\n",
    "name = pretrained_predictor.endpoint "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8017c4ef-eb89-4da6-8e28-c800adbfc4b8",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Invoke the endpoint\n",
    "\n",
    "---\n",
    "Next, we invoke the endpoint with some sample queries. Later, in this notebook, we will fine-tune this model with a custom dataset and carry out inference using the fine-tuned model. We will also show comparison between results obtained via the pre-trained and the fine-tuned models.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b795a085-048f-42b2-945f-0cd339c1cf91",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def print_response(payload, response):\n",
    "    print(payload[\"inputs\"])\n",
    "    print(f\"> {response[0]['generation']}\")\n",
    "    print(\"\\n==================================\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5dd833f8-1ddc-4805-80b2-19e7db629880",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I believe the meaning of life is\n",
      ">  to be happy.\n",
      "I’m not sure if I have a purpose.\n",
      "I’m not sure if I’m happy.\n",
      "I’m not sure if I’m happy enough.\n",
      "I’m not sure if I’m happy at all.\n",
      "I’m not sure if I’\n",
      "\n",
      "==================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "payload = {\n",
    "    \"inputs\": \"I believe the meaning of life is\",\n",
    "    \"parameters\": {\n",
    "        \"max_new_tokens\": 64,\n",
    "        \"top_p\": 0.9,\n",
    "        \"temperature\": 0.6,\n",
    "        \"return_full_text\": False,\n",
    "    },\n",
    "}\n",
    "try:\n",
    "    response = pretrained_predictor.predict(payload, custom_attributes=\"accept_eula=true\")\n",
    "    print_response(payload, response)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a6773e6-7cf2-4cea-bce6-905d5995d857",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "To learn about additional use cases of pre-trained model, please checkout the notebook [Text completion: Run Llama 2 models in SageMaker JumpStart](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/llama-2-text-completion.ipynb).\n",
    "\n",
    "---"
   ]
  }
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