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certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n", + "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n", + "Installing collected packages: tokenizers, safetensors, bitsandbytes, huggingface-hub, transformers, accelerate\n", + "Successfully installed accelerate-0.21.0 bitsandbytes-0.41.1 huggingface-hub-0.16.4 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0\n" + ] + } + ], + "source": [ + "!pip install torch transformers bitsandbytes accelerate" + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline" + ], + "metadata": { + "id": "BzllQomZfQnj" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "torch.cuda.is_available()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hztT0MXkfRs1", + "outputId": "93300c3e-4119-4cab-ab97-7b1f1c86ffef" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# use an A100 on Colab Pro (or any system with >30GB VRAM on your own machine) to load this in bf16\n", + "# if not available, use a GPU with minimum 20GB VRAM to load this in 8bit, or with minimum 12GB of VRAM to load in 4bit\n", + "# on Colab, works with a V100 but crashes on a T4\n", + "\n", + "# downloading the model and then loading it to memory step takes around 10 minutes the first time. Be patient :)" + ], + "metadata": { + "id": "Acq5rPZjfWAi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model_name = \"defog/sqlcoder\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + " trust_remote_code=True,\n", + " # torch_dtype=torch.bfloat16,\n", + " # load_in_8bit=True,\n", + " load_in_4bit=True,\n", + " device_map=\"auto\",\n", + " use_cache=True,\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 465, + "referenced_widgets": [ + "26d596dcafa94b49a49ad570d9b23a7d", + "30f0a973e4234439aba16655fd032e5e", + "ecaed6123d9c4986b3cc77e2e78824c4", + "e1fbd3768a144d6aa4bb09854d8f0d49", + "8edfb45c4f954acc91d905f2a5cac8a4", + "5fc70bcdf65e42ca855da3939e6831ff", + "fed19e1071ab4d3a997cd133d1043ee1", + "3b43c9034959444bb1fbc72a0d606e96", + "c849a4f6077341558c4e6150b99957d6", + 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sum(sales.quantity) AS sales_2022\n", + " FROM sales\n", + " WHERE sales.sale_date >= '2022-01-01'\n", + " AND sales.sale_date <= '2022-12-31'\n", + " GROUP BY sales.product_id\n", + ")\n", + "SELECT products.name,\n", + " sales_2021.sales_2021,\n", + " sales_2022.sales_2022,\n", + " sales_2022.sales_2022 - sales_2021.sales_2021 AS difference\n", + "FROM products\n", + " LEFT JOIN sales_2021 ON products.product_id = sales_2021.product_id\n", + " LEFT JOIN sales_2022 ON products.product_id = sales_2022.product_id\n", + "ORDER BY difference DESC NULLS LAST;\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "wDnRg0zRlQs2" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file