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  "cells": [
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      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub transformers transformers[sentencepiece]"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "_rbUD_747eUJ",
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.19.3)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.0)\n",
            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.1.99)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers) (3.20.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
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        "outputId": "ba2da87e-8a04-48b1-97c9-1da10a834653"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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        "outputId": "0233d826-96a1-406f-b927-98c215e086f2"
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              "MT5ForConditionalGeneration(\n",
              "  (shared): Embedding(250112, 1024)\n",
              "  (encoder): MT5Stack(\n",
              "    (embed_tokens): Embedding(250112, 1024)\n",
              "    (block): ModuleList(\n",
              "      (0): MT5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): MT5LayerSelfAttention(\n",
              "            (SelfAttention): MT5Attention(\n",
              "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (relative_attention_bias): Embedding(32, 16)\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): MT5LayerFF(\n",
              "            (DenseReluDense): MT5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
              "              (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
              "              (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1-23): 23 x MT5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): MT5LayerSelfAttention(\n",
              "            (SelfAttention): MT5Attention(\n",
              "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): MT5LayerFF(\n",
              "            (DenseReluDense): MT5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
              "              (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
              "              (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (final_layer_norm): MT5LayerNorm()\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (decoder): MT5Stack(\n",
              "    (embed_tokens): Embedding(250112, 1024)\n",
              "    (block): ModuleList(\n",
              "      (0): MT5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): MT5LayerSelfAttention(\n",
              "            (SelfAttention): MT5Attention(\n",
              "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (relative_attention_bias): Embedding(32, 16)\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): MT5LayerCrossAttention(\n",
              "            (EncDecAttention): MT5Attention(\n",
              "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): MT5LayerFF(\n",
              "            (DenseReluDense): MT5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
              "              (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
              "              (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "      (1-23): 23 x MT5Block(\n",
              "        (layer): ModuleList(\n",
              "          (0): MT5LayerSelfAttention(\n",
              "            (SelfAttention): MT5Attention(\n",
              "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (1): MT5LayerCrossAttention(\n",
              "            (EncDecAttention): MT5Attention(\n",
              "              (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "              (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "          (2): MT5LayerFF(\n",
              "            (DenseReluDense): MT5DenseGatedActDense(\n",
              "              (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
              "              (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
              "              (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "              (act): NewGELUActivation()\n",
              "            )\n",
              "            (layer_norm): MT5LayerNorm()\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (final_layer_norm): MT5LayerNorm()\n",
              "    (dropout): Dropout(p=0.1, inplace=False)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=1024, out_features=250112, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, MT5ForConditionalGeneration\n",
        "\n",
        "model_name = 'learningmachineaz/cirtdan-azerbaijani-chatbot'\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = MT5ForConditionalGeneration.from_pretrained(model_name).to(device)\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"<SC6>Sən bir oğlansan, müxtəlif məsələlər üzrə məsləhətçi. Çox ağıllısan. Həmsöhbətinə kömək etmək istəyirəm. Dialoqu davam et:\\nHəmsöhbət: salam! necəsən?\\nSən: <extra_id_0>\"\n",
        "\n",
        "input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)\n",
        "\n",
        "out_ids = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    do_sample=True,\n",
        "    temperature=0.9,\n",
        "    max_new_tokens=128,\n",
        "    top_p=0.85,\n",
        "    top_k=2,\n",
        "    repetition_penalty=1.2\n",
        ")\n",
        "\n",
        "output = tokenizer.decode(out_ids[0][1:])\n",
        "\n",
        "if '</s>' in output:\n",
        "    output = output[:output.find('</s>')].strip()\n",
        "\n",
        "output = output.replace('<extra_id_0>', '').strip()\n",
        "output = output.split('Həmsöhbət')[0].strip()\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IVwZ1iRS1TSc",
        "outputId": "52878039-1719-4284-ec3b-f79df04629ea"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Salam.\n"
          ]
        }
      ]
    }
  ]
}