{
  "cells": [
    {
      "metadata": {
        "id": "My2AZpV_RuTs"
      },
      "cell_type": "markdown",
      "source": [
        "# Sampling\n",
        "\n",
        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/gemma/blob/main/colabs/sampling.ipynb)\n",
        "\n",
        "Example on how to load a Gemma model and run inference on it.\n",
        "\n",
        "The Gemma library has 3 ways to prompt a model:\n",
        "\n",
        "* `gm.text.ChatSampler`: Easiest to use, simply talk to the model and get answer. Support multi-turns conversations out-of-the-box.\n",
        "* `gm.text.Sampler`: Lower level, but give more control. The chat state has to be manually handeled for multi-turn.\n",
        "* `model.apply`: Directly call the model, only predict a single token."
      ]
    },
    {
      "metadata": {
        "id": "94CVV9ZxKVDO"
      },
      "cell_type": "code",
      "source": [
        "!pip install -q gemma"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {
        "executionInfo": {
          "elapsed": 2610,
          "status": "ok",
          "timestamp": 1741517119876,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "PXBc1hRKRuTt"
      },
      "cell_type": "code",
      "source": [
        "# Common imports\n",
        "import os\n",
        "import jax\n",
        "import jax.numpy as jnp\n",
        "\n",
        "# Gemma imports\n",
        "from gemma import gm"
      ],
      "outputs": [],
      "execution_count": 1
    },
    {
      "metadata": {
        "id": "i_DEVehe3v5Y"
      },
      "cell_type": "markdown",
      "source": [
        "By default, Jax do not utilize the full GPU memory, but this can be overwritten. See [GPU memory allocation](https://docs.jax.dev/en/latest/gpu_memory_allocation.html):"
      ]
    },
    {
      "metadata": {
        "id": "AaK17GWo3v5Y"
      },
      "cell_type": "code",
      "source": [
        "os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"]=\"1.00\""
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "XzEj3PYvX1Sm"
      },
      "cell_type": "markdown",
      "source": [
        "Load the model and the params. Here we load the instruction-tuned version of the model."
      ]
    },
    {
      "metadata": {
        "id": "ox1CAuffKJtj"
      },
      "cell_type": "code",
      "source": [
        "model = gm.nn.Gemma3_4B()\n",
        "\n",
        "params = gm.ckpts.load_params(gm.ckpts.CheckpointPath.GEMMA3_4B_IT)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "iRjnbNREdugN"
      },
      "cell_type": "markdown",
      "source": [
        "## Multi-turns conversations\n",
        "\n",
        "The easiest way to chat with Gemma is to use the `gm.text.ChatSampler`. It hides the boilerplate of the conversation cache, as well as the `<start_of_turn>` / `<end_of_turn>` tokens used to format the conversation.\n",
        "\n",
        "Here, we set `multi_turn=True` when creating `gm.text.ChatSampler` (by default, the `ChatSampler` start a new conversation every time).\n",
        "\n",
        "In multi-turn mode, you can erase the previous conversation state, by passing `chatbot.chat(..., multi_turn=False)`."
      ]
    },
    {
      "metadata": {
        "executionInfo": {
          "elapsed": 18237,
          "status": "ok",
          "timestamp": 1741517159587,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "bGSE6aTYdxqt",
        "outputId": "5d428ab7-f9bf-4898-a921-cd57353ff364"
      },
      "cell_type": "code",
      "source": [
        "sampler = gm.text.ChatSampler(\n",
        "    model=model,\n",
        "    params=params,\n",
        "    multi_turn=True,\n",
        "    print_stream=True,  # Print output as it is generated.\n",
        ")\n",
        "\n",
        "turn0 = sampler.chat('Share one methapore linking \"shadow\" and \"laughter\".')"
      ],
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Okay, here's a metaphor linking \"shadow\" and \"laughter,\" aiming for a slightly evocative and layered feel:\n",
            "\n",
            "**\"Laughter is the fleeting shadow of joy, dancing across a face that’s often hidden in the long shadow of sorrow.\"**\n",
            "\n",
            "---\n",
            "\n",
            "**Here's a breakdown of why this works:**\n",
            "\n",
            "*   **\"Shadow\"** represents sadness, pain, or a past experience that lingers. It’s not necessarily a dark shadow, but a persistent presence.\n",
            "*   **\"Laughter\"** is presented as a brief, bright appearance – a momentary flash of happiness.\n",
            "*   **\"Dancing across a face that’s often hidden\"** emphasizes that the joy isn't constant, and the underlying sadness is still there, obscuring it.\n",
            "\n",
            "---\n",
            "\n",
            "Would you like me to:\n",
            "\n",
            "*   Try a different type of metaphor?\n",
            "*   Expand on this one with a short story snippet?\n"
          ]
        }
      ],
      "execution_count": 3
    },
    {
      "metadata": {
        "executionInfo": {
          "elapsed": 7822,
          "status": "ok",
          "timestamp": 1741517170597,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "MIZqgSSRfmRS",
        "outputId": "575490ff-3d07-48f6-cb39-5fac3553e24a"
      },
      "cell_type": "code",
      "source": [
        "turn1 = sampler.chat('Expand it in a haiku.')"
      ],
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Okay, here’s a haiku based on the metaphor:\n",
            "\n",
            "Shadow stretches long,\n",
            "Laughter’s brief, bright, dancing grace,\n",
            "Joy hides in the dark. \n",
            "\n",
            "---\n",
            "\n",
            "Would you like me to try another haiku, or perhaps a different poetic form?\n"
          ]
        }
      ],
      "execution_count": 4
    },
    {
      "metadata": {
        "id": "uOQHd6eFd67c"
      },
      "cell_type": "markdown",
      "source": [
        "Note: By default (`multi_turn=False`), the conversation state is reset everytime, but you can still continue the previous conversation by passing `sampler.chat(..., multi_turn=True)`\n",
        "\n",
        "By default, greedy decoding is used. You can pass a custom `sampling=` method as kwargs:\n",
        "\n",
        "* `gm.text.Greedy()`: (default) Greedy decoding\n",
        "* `gm.text.RandomSampling()`: Simple random sampling with temperature, for more variety"
      ]
    },
    {
      "metadata": {
        "id": "AH0eWFWJaiNk"
      },
      "cell_type": "markdown",
      "source": [
        "## Sample a prompt\n",
        "\n",
        "For more control, we also provide a `gm.text.Sampler` which still perform efficient sampling (with kv-caching, early stopping,...).\n",
        "\n",
        "Prompting the sampler require to correctly add format the prompt with the `<start_of_turn>` / `<end_of_turn>` tokens (see the custom token section doc on [tokenizer](https://gemma-llm.readthedocs.io/en/latest/tokenizer.html))."
      ]
    },
    {
      "metadata": {
        "executionInfo": {
          "elapsed": 14339,
          "status": "ok",
          "timestamp": 1741277003042,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "6R5J42EiZtkC",
        "outputId": "e309909f-b627-4057-94c0-524e66dd3ea2"
      },
      "cell_type": "code",
      "source": [
        "sampler = gm.text.Sampler(\n",
        "    model=model,\n",
        "    params=params,\n",
        ")\n",
        "\n",
        "prompt = \"\"\"<start_of_turn>user\n",
        "Give me a list of inspirational quotes.<end_of_turn>\n",
        "<start_of_turn>model\n",
        "\"\"\"\n",
        "\n",
        "out = sampler.sample(prompt, max_new_tokens=1000)\n",
        "print(out)"
      ],
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Okay, here's a list of inspirational quotes, categorized a little to give you a variety:\n",
            "\n",
            "**On Perseverance & Resilience:**\n",
            "\n",
            "*   “The only way to do great work is to love what you do.” – Steve Jobs\n",
            "*   “Fall seven times, stand up eight.” – Japanese Proverb\n",
            "*   “The difference between ordinary and extraordinary is that little extra.” – Jimmy Johnson\n",
            "*   “Success is not final, failure is not fatal: It is the courage to continue that counts.” – Winston Churchill\n",
            "*   “Don’t watch the clock; do what it does. Keep going.” – Sam Levenson\n",
            "*   “When the going gets tough, the tough get going.” – Theodore Roosevelt\n",
            "\n",
            "\n",
            "**On Self-Love & Confidence:**\n",
            "\n",
            "*   “You are enough.” – Brené Brown\n",
            "*   “Believe you can and you’re halfway there.” – Theodore Roosevelt\n",
            "*   “You must be the change you wish to see in the world.” – Mahatma Gandhi\n",
            "*   “The best is yet to come.” – Frank Sinatra\n",
            "*   “Be the energy you want to attract.” – Tony Gaskins\n",
            "*   “Don’t be defined by your past. Define your future.” – Unknown\n",
            "\n",
            "\n",
            "**On Dreams & Goals:**\n",
            "\n",
            "*   “If you can dream it, you can do it.” – Walt Disney\n",
            "*   “The future belongs to those who believe in the beauty of their dreams.” – Eleanor Roosevelt\n",
            "*   “Shoot for the moon. Even if you miss, you’ll land among the stars.” – Les Brown\n",
            "*   “Start where you are. Use what you have. Do what you can.” – Arthur Ashe\n",
            "*   “Life begins at the end of your comfort zone.” – Unknown\n",
            "\n",
            "\n",
            "**On Happiness & Perspective:**\n",
            "\n",
            "*   “Happiness is not something readymade. It comes from your own actions.” – Dalai Lama\n",
            "*   “It’s not the triumph that matters, it’s the effort.” – Winston Churchill\n",
            "*   “Don’t wait for the perfect moment, take the moment and make it perfect.” – Oscar Wilde\n",
            "*   “Be present. Be grateful. Be you.” – Unknown\n",
            "*   “The only way out is through.” – Robert Frost\n",
            "\n",
            "\n",
            "\n",
            "**Short & Powerful:**\n",
            "\n",
            "*   “Be the change.” – Mahatma Gandhi\n",
            "*   “Just breathe.”\n",
            "*   “Keep going.”\n",
            "*   “You got this.”\n",
            "*   “Dream big.”\n",
            "\n",
            "---\n",
            "\n",
            "**Resources for More Quotes:**\n",
            "\n",
            "*   **BrainyQuote:** [https://www.brainyquote.com/](https://www.brainyquote.com/)\n",
            "*   **Goodreads:** [https://www.goodreads.com/quotes](https://www.goodreads.com/quotes)\n",
            "*   **Quote Garden:** [https://quotegarden.com/](https://quotegarden.com/)\n",
            "\n",
            "To help me give you even more relevant quotes, could you tell me:\n",
            "\n",
            "*   **What kind of inspiration are you looking for?** (e.g., motivation for work, overcoming challenges, self-love, etc.)\n",
            "*   **Is there a particular theme or topic you'd like quotes about?**<end_of_turn>\n"
          ]
        }
      ],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "TPf01271RuTs"
      },
      "cell_type": "markdown",
      "source": [
        "## Use the model directly\n",
        "\n",
        "Here's an example of predicting a single token, directly calling the model.\n",
        "\n",
        "The model input expectes encoded tokens. For this, we first need to encode the prompt with our tokenizer. See our [tokenizer](https://gemma-llm.readthedocs.io/en/latest/tokenizer.html) documentation for more information on using the tokenizer."
      ]
    },
    {
      "metadata": {
        "id": "mvCAQCDXZ0D3"
      },
      "cell_type": "code",
      "source": [
        "tokenizer = gm.text.Gemma3Tokenizer()"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "kH534DHohG67"
      },
      "cell_type": "markdown",
      "source": [
        "Note: When encoding the prompt, don't forget to add the beginning-of-string token with `add_bos=True`. All prompts feed to the model should start by this token."
      ]
    },
    {
      "metadata": {
        "id": "T1GC0OPRhHGc"
      },
      "cell_type": "code",
      "source": [
        "prompt = tokenizer.encode('One word to describe Paris: \\n\\n', add_bos=True)\n",
        "prompt = jnp.asarray(prompt)"
      ],
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "hHqyFnskZ5SC"
      },
      "cell_type": "markdown",
      "source": [
        "We then can call the model, and get the predicted logits."
      ]
    },
    {
      "metadata": {
        "colab": {
          "height": 35
        },
        "executionInfo": {
          "elapsed": 7194,
          "status": "ok",
          "timestamp": 1741277771722,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "G3Kbo9hgRuTt",
        "outputId": "3b0e468e-c55c-466a-ece9-ade04926612d"
      },
      "cell_type": "code",
      "source": [
        "# Run the model\n",
        "out = model.apply(\n",
        "    {'params': params},\n",
        "    tokens=prompt,\n",
        "    return_last_only=True,  # Only predict the last token\n",
        ")\n",
        "\n",
        "\n",
        "# Sample a token from the predicted logits\n",
        "next_token = jax.random.categorical(\n",
        "    jax.random.key(1),\n",
        "    out.logits\n",
        ")\n",
        "tokenizer.decode(next_token)"
      ],
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Romantic'"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "TNb88k3EF5gu"
      },
      "cell_type": "markdown",
      "source": [
        "You can also display the next token probability."
      ]
    },
    {
      "metadata": {
        "colab": {
          "height": 542
        },
        "executionInfo": {
          "elapsed": 104,
          "status": "ok",
          "timestamp": 1741277773577,
          "user": {
            "displayName": "",
            "userId": ""
          },
          "user_tz": -60
        },
        "id": "mIkOdE9dF45s",
        "outputId": "ab610392-44f5-44cc-9370-bd881bc9136b"
      },
      "cell_type": "code",
      "source": [
        "tokenizer.plot_logits(out.logits)"
      ],
      "outputs": [
        {
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.34.0.min.js\"></script>                <div id=\"404ff5b8-2355-423c-8591-efb917a0d684\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"404ff5b8-2355-423c-8591-efb917a0d684\")) {                    Plotly.newPlot(                        \"404ff5b8-2355-423c-8591-efb917a0d684\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"x=%{x}\\u003cbr\\u003ey=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"x\":[\"'**'\",\"'Romantic'\",\"'Ch'\",\"'Elegant'\",\"'*'\",\"'Icon'\",\"'Rom'\",\"'E'\",\"'Mag'\",\"'Charm'\",\"'Magn'\",\"'Beautiful'\",\"'\\u003cb\\u003e'\",\"'Dream'\",\"'Tim'\",\"'***'\",\"'\\u2728'\",\"'romantic'\",\"'Capt'\",\"'\\\\\\\\'\",\"'Ephemeral'\",\"'Paris'\",\"'Classic'\",\"'En'\",\"'icon'\",\"'A'\",\"'Lux'\",\"'Magic'\",\"'('\",\"'Maj'\"],\"xaxis\":\"x\",\"y\":[0.6171875,0.19921875,0.05712890625,0.039306640625,0.02392578125,0.0185546875,0.016357421875,0.003662109375,0.003662109375,0.0022125244140625,0.0019683837890625,0.0017242431640625,0.0013427734375,0.001190185546875,0.0007171630859375,0.0007171630859375,0.00063323974609375,0.00063323974609375,0.00063323974609375,0.000560760498046875,0.000560760498046875,0.000560760498046875,0.00049591064453125,0.0004367828369140625,0.0004367828369140625,0.000385284423828125,0.000385284423828125,0.000385284423828125,0.000339508056640625,0.000301361083984375],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Tokens\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Probability\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"barmode\":\"relative\",\"title\":{\"text\":\"Probability Distribution of Tokens\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('404ff5b8-2355-423c-8591-efb917a0d684');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "3yeUUP8Rh5mU"
      },
      "cell_type": "markdown",
      "source": [
        "## Next steps\n",
        "\n",
        "* See our [multimodal](https://gemma-llm.readthedocs.io/en/latest/multimodal.html) example to query the model with images.\n",
        "* See our [finetuning](https://gemma-llm.readthedocs.io/en/latest/finetuning.html) example to train Gemma on your custom task.\n",
        "* See our [tool use](https://gemma-llm.readthedocs.io/en/latest/tool_use.html) tutorial to extend Gemma with external tools.\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "last_runtime": {},
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
