apply_chat_template_new.ipynb

apply_chat_template_new.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO4sIUHyOWCVf9zieKhWDiD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/1d130bbba88e6f7d109685d9e065125b/apply_chat_template_new.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "0KNAocOmS6nc"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade --quiet transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)"
      ],
      "metadata": {
        "id": "oF2KvGu6W-I3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_id = \"NousResearch/Hermes-2-Pro-Llama-3-8B\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)"
      ],
      "metadata": {
        "id": "k_iiO4-vTdBo"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_current_temperature(location: str):\n",
        "  \"\"\"Get the temperature at a given location.\n",
        "  Args:\n",
        "    location: the location to get the temperature for\n",
        "  \"\"\"\n",
        "  return 22"
      ],
      "metadata": {
        "id": "IxdylcocWa8J"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": \"What's the temperature in Paris?\"}\n",
        "]"
      ],
      "metadata": {
        "id": "SEG8bJEET3kZ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tools=[get_current_temperature],\n",
        "    tokenize=False\n",
        ")\n",
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZCv-oajT8IX",
        "outputId": "3516200c-249f-4364-c546-25dc6ed1d179"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|begin_of_text|>You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {\"type\": \"function\", \"function\": {\"name\": get_current_temperature\", \"description\": \"get_current_temperature(location: str) - Get the temperature at a given location.\n",
            "\n",
            "    Args:\n",
            "        location(str): the location to get the temperature for\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\", \"description\": \"the location to get the temperature for\"}}, \"required\": [\"location\"]}} </tools>Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}, \"name\": {\"title\": \"Name\", \"type\": \"string\"}}, \"required\": [\"arguments\", \"name\"], \"title\": \"FunctionCall\", \"type\": \"object\"}\n",
            "For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:\n",
            "<tool_call>\n",
            "{\"arguments\": <args-dict>, \"name\": <function-name>}\n",
            "</tool_call><|im_end|><|im_start|>user\n",
            "What's the temperature in Paris?<|im_end|>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IVjmdCH_UAdZ"
      },
      "execution_count": 6,
      "outputs": []
    }
  ]
}