mathstral_7b_colab.ipynb

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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Vaibhavs10/notebooks/blob/main/mathstral_7b_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mathstral 7B ∑\n",
        "\n",
        "🤗 [Hugging Face Model](https://huggingface.co/mistralai/mathstral-7B-v0.1)\n",
        "\n",
        "From the [Mathstral blogpost](https://mistral.ai/news/mathstral/),\n",
        "\"We’re contributing Mathstral to the science community to bolster efforts in advanced mathematical problems requiring complex, multi-step logical reasoning. The Mathstral release is part of our broader effort to support academic projects—it was produced in the context of our collaboration with Project Numina.\n",
        "\n",
        "Akin to Isaac Newton in his time, Mathstral stands on the shoulders of Mistral 7B and specializes in STEM subjects. It achieves state-of-the-art reasoning capacities in its size category across various industry-standard benchmarks. In particular, it achieves 56.6% on MATH and 63.47% on MMLU, with the following MMLU performance difference by subject between Mathstral 7B and Mistral 7B.\""
      ],
      "metadata": {
        "id": "JfpfjqVpwuyy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup Inference Environment\n",
        "\n"
      ],
      "metadata": {
        "id": "VMtBo2a7xGMu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avrloNBGZxR7",
        "outputId": "6a1e51bd-26d9-4f21-faa1-a7fce31dd5a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m52.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/transformers.git accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initialise the Text Generation pipeline\n",
        "\n",
        "P.S. Make sure to accept the terms and conditions from the model page [here](https://huggingface.co/mistralai/mathstral-7B-v0.1)"
      ],
      "metadata": {
        "id": "Fhds129ewt3e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "import torch\n",
        "\n",
        "model_id = \"mistralai/mathstral-7B-v0.1\"\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model_id,\n",
        "    model_kwargs={\"torch_dtype\": torch.bfloat16},\n",
        "    device_map=\"auto\",\n",
        ")"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:accelerate.big_modeling:Some parameters are on the meta device device because they were offloaded to the cpu.\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define the prompt similar to messages API\n",
        "\n",
        "You can pretty much define any query, question, discussion that you want the LLM to process below."
      ],
      "metadata": {
        "id": "UoqdBFmyxgAF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": \"Albert likes to surf every week. Each surfing session lasts for 4 hours and costs $20 per hour. How much would Albert spend in 5 weeks?\"},\n",
        "]"
      ],
      "metadata": {
        "id": "ZvcAcz-9xfAU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pass the outputs to the pipeline"
      ],
      "metadata": {
        "id": "vzmqL5IQxy9_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = pipe(\n",
        "    messages,\n",
        "    max_new_tokens=256,\n",
        "    do_sample=False,\n",
        ")"
      ],
      "metadata": {
        "id": "yJV662pJxxmC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df706cd9-5207-4056-b989-0d5b99a0c143"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No chat template is set for this tokenizer, falling back to a default class-level template. This is very error-prone, because models are often trained with templates different from the class default! Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which point any code depending on them will stop working. We recommend setting a valid chat template before then to ensure that this model continues working without issues.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generate response"
      ],
      "metadata": {
        "id": "ITfaA8YYx4lQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "assistant_response = outputs[0][\"generated_text\"][-1][\"content\"]\n",
        "print(assistant_response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndJWFgIfx20C",
        "outputId": "0adb4bcc-7e40-4b29-9091-f6e0161d2a4d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " To calculate the total cost for Albert's surfing sessions over 5 weeks, we follow these steps:\n",
            "\n",
            "1. First, we calculate the cost per session. Since each session lasts for 4 hours and costs $20 per hour, the cost per session is calculated as:\n",
            "\\[4 \\text{ hours} \\times \\$20/\\text{hour} = \\$80\\]\n",
            "\n",
            "2. Next, to find out how much Albert would spend in 5 weeks, we multiply the cost per session by the number of weeks:\n",
            "\\[\\$80/\\text{session} \\times 5 \\text{ sessions} = \\$400\\]\n",
            "\n",
            "Therefore, the total amount Albert would spend in 5 weeks is \\boxed{\\$400}.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Voila! You have your own personal (& powerful)\n",
        "math assistant now!"
      ],
      "metadata": {
        "id": "NkfqH9gKzEnO"
      }
    }
  ]
}