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  "cells": [
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      "cell_type": "markdown",
      "source": [
        "# RecurrentGemma - 2B & 2B-it\n",
        "\n",
        "RecurrentGemma is a family of open language models built on a novel recurrent architecture developed at Google. Both pre-trained (2B) and instruction-tuned (2B-it) versions are available in English.\n",
        "\n",
        "Like Gemma, [RecurrentGemma](https://huggingface.co/google/recurrentgemma-2b-it) models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Because of its novel architecture, RecurrentGemma requires less memory than Gemma and achieves faster inference when generating long sequences."
      ],
      "metadata": {
        "id": "MVkIfH6Cg7Fx"
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        "id": "ahVaTC6rEIVI",
        "outputId": "2036392c-b381-4ca0-80ba-16ba8c87cde3"
      },
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          "name": "stdout",
          "text": [
            "Collecting transformers==4.40.0.dev0\n",
            "  Downloading https://huggingface.co/datasets/reach-vb/random-wheels/resolve/main/transformers-4.40.0.dev0-py3-none-any.whl (8.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.8/8.8 MB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (3.13.3)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (0.20.3)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (0.15.2)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (0.4.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.0.dev0) (4.66.2)\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==4.40.0.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==4.40.0.dev0) (4.10.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.40.0.dev0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.40.0.dev0) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.40.0.dev0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.40.0.dev0) (2024.2.2)\n",
            "Installing collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.38.2\n",
            "    Uninstalling transformers-4.38.2:\n",
            "      Successfully uninstalled transformers-4.38.2\n",
            "Successfully installed transformers-4.40.0.dev0\n"
          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the model checkpoints\n",
        "\n",
        "Make sure to accept the terms and conditions for the model before running the code further here: https://huggingface.co/google/recurrentgemma-2b-it.\n"
      ],
      "metadata": {
        "id": "FZK4T_zHhL9Q"
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      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/recurrentgemma-2b-it\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"google/recurrentgemma-2b-it\", torch_dtype=torch.float16).to(\"cuda:0\")"
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        },
        "id": "XItA_HZ-EPIR",
        "outputId": "22b1edbc-c6d7-4ad0-b992-0f59682a30ce"
      },
      "execution_count": null,
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        {
          "output_type": "display_data",
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            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/40.5k [00:00<?, ?B/s]"
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    {
      "cell_type": "markdown",
      "source": [
        "## Prepare our input text with chat template.\n",
        "\n",
        "The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.\n",
        "\n",
        "Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:"
      ],
      "metadata": {
        "id": "u3hoYG18hmHS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chat = [\n",
        "    { \"role\": \"user\", \"content\": \"Write a hello world program\" },\n",
        "]\n",
        "prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)"
      ],
      "metadata": {
        "id": "IrVmrmVpkgN3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenize the inputs"
      ],
      "metadata": {
        "id": "fDAnnNAYsghB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors=\"pt\").to(model.device)"
      ],
      "metadata": {
        "id": "dlokCV7isgEI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pass the input through the model and generate."
      ],
      "metadata": {
        "id": "pBXeqctLhuGy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)\n",
        "print(tokenizer.batch_decode(outputs, skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odmrX7pQeRo6",
        "outputId": "3d2f4189-0f7e-4536-eee1-e5265915c657"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['<start_of_turn>user\\nWrite a hello world program<end_of_turn>\\n<start_of_turn>model\\n```python\\nprint(\"Hello, world!\")\\n```\\n\\nThis program will print the message \"Hello, world!\" to the console.\\n\\n**Explanation:**\\n\\n* `print()` is a built-in Python function that prints the given argument to the console.\\n* `\"Hello, world!\"` is the string that will be printed.\\n\\n**Output:**\\n\\n```\\nHello, world!\\n```']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Enjoy! There's much more you can do to maximise the output of your generation. Check out this guide: https://huggingface.co/docs/transformers/generation_strategies"
      ],
      "metadata": {
        "id": "rkpXJ5sHwmMH"
      }
    }
  ]
}