Gemma_2_2B_colab.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/Vaibhavs10/gpu-poor-llm-notebooks/blob/main/Gemma_2_2B_colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run Gemma-2-2B with < 5GB VRAM!\n",
        "\n",
        "Powered by Transformers 🤗\n",
        "\n",
        "[Model Checkpoint](https://huggingface.co/google/gemma-2-2b-it)\n",
        "\n",
        "*Make sure to accept the license by visiting the model checkpoint above.*"
      ],
      "metadata": {
        "id": "ycrm7hWWxYoX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup Environment\n",
        "\n",
        "Gemma-2-2B should work out of the box with Transformers, make sure to be on the latest transformers release!"
      ],
      "metadata": {
        "id": "PCzvqFRoyGcM"
      }
    },
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      "cell_type": "code",
      "execution_count": null,
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        "id": "u5hdTjYoYHqn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9339ce98-509b-4751-f34a-6d73396d8bf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.4/9.4 MB\u001b[0m \u001b[31m38.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m315.1/315.1 kB\u001b[0m \u001b[31m13.7 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[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q --upgrade transformers accelerate"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Tokenizer and Model checkpoint"
      ],
      "metadata": {
        "id": "fO093ZGCynnm"
      }
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        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "model_id = \"google/gemma-2-2b-it\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "  model_id,\n",
        "  torch_dtype=torch.bfloat16,\n",
        "  low_cpu_mem_usage=True,\n",
        ").to(\"cuda\")"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define Prompt & Tokenize"
      ],
      "metadata": {
        "id": "sck3FWKpy11A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": \"Who are you? Please, answer in pirate-speak.\"},\n",
        "]\n",
        "\n",
        "inputs = tokenizer.apply_chat_template(\n",
        "  messages,\n",
        "  tokenize=True,\n",
        "  add_generation_prompt=True,\n",
        "  return_tensors=\"pt\",\n",
        "  return_dict=True,\n",
        ").to(\"cuda\")"
      ],
      "metadata": {
        "id": "HDS8InY0y0WC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generate"
      ],
      "metadata": {
        "id": "0veYFyBOzAmG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model.generate(**inputs, do_sample=True, max_new_tokens=25)\n",
        "print(tokenizer.batch_decode(outputs, skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QslxgBSgy7w_",
        "outputId": "383c69a1-d08e-472a-b257-2d9247ed6962"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['user\\nWho are you? Please, answer in pirate-speak.\\nmodel\\nAhoy there, matey!  I be Gemma, a salty sea dog of the digital docks.  I be here']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Voila! You now have a smart and capable assistant! 🦙"
      ],
      "metadata": {
        "id": "o__5S-lCzDo3"
      }
    }
  ]
}