Example_FirstStep_zephyr_7b_alpha.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/textspur/prompting_open_llm/blob/main/Example_FirstStep_zephyr_7b_alpha.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Steps covered:\n",
        "\n",
        "*   Installation: Instructions to install necessary libraries (transformers and accelerate) from Hugging Face.\n",
        "*   Load Dependencies: Importing torch and the pipeline from transformers.\n",
        "*   Model Loading: Initializing the text generation pipeline with the Zephyr-7b-alpha model.\n",
        "*   First Prompt Setup: Demonstrates how to set up a prompt for text generation, including defining roles (system, user, assistant) for the interaction.\n",
        "* Response Generation: Shows how to generate a response from the model based on the provided prompt.\n",
        "* Functions for Text Classification: Includes functions for zero-shot and one-shot text classification, demonstrating the versatility of the model.\n",
        "* Example Runs: Provides example runs for both zero-shot and few-shot text classification using the model.\n",
        "\n"
      ],
      "metadata": {
        "id": "C27GLOgkYyyV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install"
      ],
      "metadata": {
        "id": "76_gzskPNN4L"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HwtjsFjgM6EP",
        "outputId": "b1b4529e-6ee3-4113-c037-5119e58b12ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git\n",
            "  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-hnr85yvv\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-hnr85yvv\n",
            "  Resolved https://github.com/huggingface/transformers.git to commit acd653164b6874e395ac9d46850f67599d8cdb58\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.36.0.dev0) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0.dev0) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.36.0.dev0) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.36.0.dev0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.36.0.dev0) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.36.0.dev0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.36.0.dev0) (2023.11.17)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.25.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu118)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.19.4)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "%pip install git+https://github.com/huggingface/transformers.git\n",
        "%pip install accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load dependencies"
      ],
      "metadata": {
        "id": "keBBxvnwNVt5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import pipeline"
      ],
      "metadata": {
        "id": "xDCNI9-9NU3g"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load model"
      ],
      "metadata": {
        "id": "TILS47OfNscW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe = pipeline(\"text-generation\", model=\"HuggingFaceH4/zephyr-7b-alpha\", torch_dtype=torch.bfloat16, device_map=\"auto\")"
      ],
      "metadata": {
        "colab": {
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              "added_tokens.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e93c40db75840718ca58a696d66aee4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/168 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b996a74cb0094ce49508c30c74b237fd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## First Prompt"
      ],
      "metadata": {
        "id": "qNResiiaNv0A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Each message can have 1 of 3 roles: \"system\" (to provide initial instructions), \"user\", or \"assistant\". For inference, make sure \"user\" is the role in the final message.\n",
        "messages = [\n",
        "    {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": \"You are a friendly chatbot who always responds in the style of a pirate.\",\n",
        "    },\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": \"How many helicopters can a human eat in one sitting?\"},\n",
        "]\n",
        "\n",
        "# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating\n",
        "prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uiec-ZZcNiJV",
        "outputId": "7d5e174d-4726-4bc2-e2d4-70ee20243daf"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "You are a friendly chatbot who always responds in the style of a pirate.</s>\n",
            "<|user|>\n",
            "How many helicopters can a human eat in one sitting?</s>\n",
            "<|assistant|>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate a response (took 47 seconds):"
      ],
      "metadata": {
        "id": "VIWPDJdYP5NG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)\n",
        "print(outputs[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZAgfbv_wPjR0",
        "outputId": "e6f4eed1-b717-4070-937a-96d98e470589"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1517: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "You are a friendly chatbot who always responds in the style of a pirate.</s>\n",
            "<|user|>\n",
            "How many helicopters can a human eat in one sitting?</s>\n",
            "<|assistant|>\n",
            "Matey, I'm afraid no human can eat a helicopter in one sitting. Helicopters are not edible, and they're too large to fit in the human digestive system. Ye be hearin' me, me hearties!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Return only generated assistant message, since we are not using a seed a different answer is given:"
      ],
      "metadata": {
        "id": "mhueIKuxPxuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, return_full_text=False)\n",
        "print(outputs[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKJnEtczPnT5",
        "outputId": "178505cb-5669-4b27-fa0a-3ff4d5f0a267"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ahoy there, matey! As for your question, I'm afraid none, for a human can't eat a helicopter, and even if they could, they wouldn't be able to consume one in a single sitting! Yarr!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Functions"
      ],
      "metadata": {
        "id": "LdSzyB-NOBHT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "{\"role\": \"system\", \"content\":\"Text of System Prompt\"},\n",
        "{\"role\": \"user\", \"content\": \"Question and text to classify\"},\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "DvaQlDWVOOSJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Zero-shot prompt messages"
      ],
      "metadata": {
        "id": "YjQDrHxYRx6_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def zero_shot_prompt_messages(system_prompt, comment_text, question):\n",
        "    messages = [\n",
        "        {\"role\": \"system\", \"content\": system_prompt},\n",
        "        {\"role\": \"user\", \"content\": 'Text to classify: ' + str(comment_text) + ' \\n '   +  str(question)},\n",
        "    ]\n",
        "    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
        "    return prompt\n"
      ],
      "metadata": {
        "id": "zT1yyyOuN3Xu"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"You are a reliabel chat assistant for classifying tweets for their sentiment.\"\n",
        "question = \"Is the sentiment for this tweet positive, negative or neutral (Respond only with:  ['positive', 'neutral', 'negative'])?\"\n",
        "\n",
        "textToClassify = \"The weather is very good! I love it\"\n",
        "\n",
        "first_zero_shot_prompt = zero_shot_prompt_messages(system_prompt, textToClassify, question)\n",
        "print(first_zero_shot_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HRF8-qcOTcR",
        "outputId": "48359497-fc17-4fbe-8b39-e47f494be2ec"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "You are a reliabel chat assistant for classifying tweets for their sentiment.</s>\n",
            "<|user|>\n",
            "Text to classify: The weather is very good! I love it \n",
            " Is the sentiment for this tweet positive, negative or neutral (Respond only with:  ['positive', 'neutral', 'negative'])?</s>\n",
            "<|assistant|>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run text classification example"
      ],
      "metadata": {
        "id": "nPVYLWISRska"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = pipe(first_zero_shot_prompt, max_new_tokens=10, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, return_full_text=False)\n",
        "print(outputs[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nekY75iQPYYb",
        "outputId": "b4a7c78f-9bd7-42c7-dc5d-34d92e8dc73d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['positive']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One or few shot prompt messages"
      ],
      "metadata": {
        "id": "yjftHSvmSMWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def few_shot_prompt_messages(system_prompt, comment_text, question, few_shot_examples):\n",
        "    messages = []\n",
        "    messages.append({\"role\": \"system\", \"content\": system_prompt})\n",
        "\n",
        "    for user_prompt, assistent_prompt in few_shot_examples:\n",
        "        messages.append({\"role\": \"user\", \"content\": user_prompt + \" \" + question})\n",
        "        messages.append({\"role\": \"assistant\", \"content\": assistent_prompt})\n",
        "\n",
        "    messages.append({\"role\": \"user\", \"content\":  str(comment_text) + str(question) })\n",
        "\n",
        "    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
        "    return prompt"
      ],
      "metadata": {
        "id": "kekTYAuKSL9P"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "An example using one-shot:"
      ],
      "metadata": {
        "id": "WaKDFQHISSRM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example_p = [(\"Missing  Foo Fighters concert on the 12th at the gorge though so that's a major bummer\",  \"negative\")]\n",
        "system_prompt = \"Please help me to classify tweets for their sentiment.  \"\n",
        "question = \"--- Is the sentiment for this tweet positive, negative or neutral (only responde with ['positive', 'neutral', 'negative'])?  \"\n",
        "\n",
        "textToClassify = \"The weather is very good! I love it\"\n",
        "\n",
        "\n",
        "first_one_shot_prompt = few_shot_prompt_messages(system_prompt, textToClassify, question, example_p)\n",
        "print(first_one_shot_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDvOvROaSbYn",
        "outputId": "9691f46b-cb57-49ea-aa38-f77cc6cf0a0f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|system|>\n",
            "Please help me to classify tweets for their sentiment.  </s>\n",
            "<|user|>\n",
            "Missing  Foo Fighters concert on the 12th at the gorge though so that's a major bummer --- Is the sentiment for this tweet positive, negative or neutral (only responde with ['positive', 'neutral', 'negative'])?  </s>\n",
            "<|assistant|>\n",
            "negative</s>\n",
            "<|user|>\n",
            "The weather is very good! I love it--- Is the sentiment for this tweet positive, negative or neutral (only responde with ['positive', 'neutral', 'negative'])?  </s>\n",
            "<|assistant|>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run text classification example"
      ],
      "metadata": {
        "id": "fZJexfN9TJmq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = pipe(first_one_shot_prompt, max_new_tokens=10, do_sample=True, temperature=0.7, top_k=50, top_p=0.95, return_full_text=False)\n",
        "print(outputs[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xRrojAAyTBxh",
        "outputId": "a3d4aa89-bca3-4910-a8a8-21fdc410bc20"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "positive\n"
          ]
        }
      ]
    }
  ]
}