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      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "ipsiG2Y-lhFs",
        "outputId": "77e88b37-2fdd-42f6-b1cb-0ed638561c33"
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          "text": [
            "Sat Aug 26 16:04:24 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   52C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git accelerate"
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        "id": "4nT2EbTEluVA",
        "outputId": "a9e548fa-82ea-4ed2-cd28-f78fce47166c"
      },
      "execution_count": null,
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          "text": [
            "Collecting git+https://github.com/huggingface/transformers.git\n",
            "  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-xcfnghoq\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-xcfnghoq\n",
            "  Resolved https://github.com/huggingface/transformers.git to commit 960807f62e53676723ab8281019219864ef3db4d\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",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.22.0-py3-none-any.whl (251 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.2/251.2 kB\u001b[0m \u001b[31m5.4 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==4.33.0.dev0) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.15.1 (from transformers==4.33.0.dev0)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.0.dev0) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.0.dev0) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.0.dev0) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.0.dev0) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.0.dev0) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.33.0.dev0)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m22.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers==4.33.0.dev0)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m49.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.33.0.dev0) (4.66.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers==4.33.0.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,>=0.15.1->transformers==4.33.0.dev0) (4.7.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
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            "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: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.2)\n",
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            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.0.dev0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.0.dev0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.0.dev0) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.33.0.dev0) (2023.7.22)\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: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.33.0.dev0-py3-none-any.whl size=7586983 sha256=c2845a1c29e35f1b25011ac4d650fa2723e8a83588ef97182468a91171d3d274\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-96kvi1cf/wheels/e7/9c/5b/e1a9c8007c343041e61cc484433d512ea9274272e3fcbe7c16\n",
            "Successfully built transformers\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers, accelerate\n",
            "Successfully installed accelerate-0.22.0 huggingface-hub-0.16.4 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.0.dev0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "import transformers\n",
        "import torch"
      ],
      "metadata": {
        "id": "Py5FxVJ8lzNO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = \"codellama/CodeLlama-7b-Instruct-hf\"\n",
        "#model = \"codellama/CodeLlama-7b-hf\""
      ],
      "metadata": {
        "id": "f4ScdlCml-3q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model)\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
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        "id": "XN0b8GBlmB2d",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/745 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
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          "output_type": "display_data",
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          "metadata": {}
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          "metadata": {}
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            ],
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            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system = \"Provide answers in Python\"\n",
        "user = \"Implement a function that takes a list of integers as input and returns a new list containing only the prime numbers from the input list.\"\n",
        "\n",
        "prompt = f\"<s><<SYS>>\\n{system}\\n<</SYS>>\\n\\n{user}\"\n"
      ],
      "metadata": {
        "id": "hH9PbwU2mIS8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = pipeline(\n",
        "    prompt,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    temperature=0.1,\n",
        "    top_p=0.95,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    max_length=400,\n",
        "    add_special_tokens=False\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vc76mKKznaid",
        "outputId": "ddd29360-8ae8-4cb2-b1fc-a35d347f86ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for seq in sequences:\n",
        "    print(f\"Result: {seq['generated_text']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovc-AJUUniEZ",
        "outputId": "7001fded-2cd6-42cd-ea27-e4de965edd65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result: <s><<SYS>>\n",
            "Provide answers in Python\n",
            "<</SYS>>\n",
            "\n",
            "Implement a function that takes a list of integers as input and returns a new list containing only the prime numbers from the input list.\n",
            "\n",
            "<details>\n",
            "<summary>Answer</summary>\n",
            "\n",
            "```python\n",
            "def get_prime_numbers(my_list):\n",
            "    prime_numbers = []\n",
            "    for num in my_list:\n",
            "        if is_prime(num):\n",
            "            prime_numbers.append(num)\n",
            "    return prime_numbers\n",
            "\n",
            "def is_prime(num):\n",
            "    if num < 2:\n",
            "        return False\n",
            "    for i in range(2, num):\n",
            "        if num % i == 0:\n",
            "            return False\n",
            "    return True\n",
            "```\n",
            "</details>\n",
            "\n",
            "<details>\n",
            "<summary>Test cases</summary>\n",
            "\n",
            "```python\n",
            "assert get_prime_numbers([]) == []\n",
            "assert get_prime_numbers([1]) == []\n",
            "assert get_prime_numbers([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) == [2, 3, 5, 7]\n",
            "```\n",
            "</details>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system = \"Provide answers in Python\"\n",
        "user = \"Create a Python class that represents a library catalog. The class should allow users to add new books, remove books by title, display the list of all books, and count the total number of books in the catalog\"\n",
        "\n",
        "prompt = f\"<s><<SYS>>\\n{system}\\n<</SYS>>\\n\\n{user}\"\n"
      ],
      "metadata": {
        "id": "D-kbFqYooJZv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sequences = pipeline(\n",
        "    prompt,\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    temperature=0.1,\n",
        "    top_p=0.95,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    max_length=1000,\n",
        "    add_special_tokens=False\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajJUAz-coVrI",
        "outputId": "bf1ba71b-85a0-4291-dc1f-7971064be16c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for seq in sequences:\n",
        "    print(f\"Result: {seq['generated_text']}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xk_aZs8eoZ34",
        "outputId": "8b2a462b-eed4-4dfa-ebbe-4fb9a0ae7e26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result: <s><<SYS>>\n",
            "Provide answers in Python\n",
            "<</SYS>>\n",
            "\n",
            "Create a Python class that represents a library catalog. The class should allow users to add new books, remove books by title, display the list of all books, and count the total number of books in the catalog.\n",
            "\n",
            "<details>\n",
            "  <summary>Answer</summary>\n",
            "\n",
            "```python\n",
            "class LibraryCatalog:\n",
            "    def __init__(self):\n",
            "        self.books = []\n",
            "\n",
            "    def add_book(self, title, author):\n",
            "        self.books.append({\"title\": title, \"author\": author})\n",
            "\n",
            "    def remove_book(self, title):\n",
            "        self.books = [book for book in self.books if book[\"title\"]!= title]\n",
            "\n",
            "    def display_books(self):\n",
            "        for book in self.books:\n",
            "            print(book[\"title\"], book[\"author\"])\n",
            "\n",
            "    def count_books(self):\n",
            "        return len(self.books)\n",
            "```\n",
            "\n",
            "</details>\n",
            "\n",
            "<details>\n",
            "  <summary>Test</summary>\n",
            "\n",
            "```python\n",
            "catalog = LibraryCatalog()\n",
            "catalog.add_book(\"The Great Gatsby\", \"F. Scott Fitzgerald\")\n",
            "catalog.add_book(\"To Kill a Mockingbird\", \"Harper Lee\")\n",
            "catalog.add_book(\"The Catcher in the Rye\", \"J.D. Salinger\")\n",
            "catalog.add_book(\"The Adventures of Huckleberry Finn\", \"Mark Twain\")\n",
            "\n",
            "print(catalog.count_books())  # 4\n",
            "\n",
            "catalog.remove_book(\"The Catcher in the Rye\")\n",
            "\n",
            "print(catalog.count_books())  # 3\n",
            "\n",
            "catalog.display_books()\n",
            "# The Great Gatsby F. Scott Fitzgerald\n",
            "# To Kill a Mockingbird Harper Lee\n",
            "# The Adventures of Huckleberry Finn Mark Twain\n",
            "```\n",
            "\n",
            "</details>\n",
            "\n"
          ]
        }
      ]
    }
  ]
}