hebrew_gpt_neo_with_Javascript_interfaceipynb.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Norod/hebrew-gpt_neo/blob/main/hebrew_gpt_neo_with_Javascript_interfaceipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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        },
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      },
      "source": [
        "%cd /content/\n",
        "\n",
        "######################################################################\n",
        "#                                                                    #\n",
        "model_name = \"Norod78/hebrew-gpt_neo-xl\"\n",
        "#model_name =\"Norod78/hebrew-gpt_neo-xl-poetry\"\n",
        "#                                                                    #\n",
        "#model_name = \"Norod78/hebrew-gpt_neo-small\"\n",
        "#model_name = \"Norod78/hebrew_poetry-gpt_neo-small\"\n",
        "#model_name = \"Norod78/hebrew-project_ben_yehuda-gpt_neo-small\"\n",
        "#model_name = \"Norod78/hebrew_stories-gpt_neo-small\"\n",
        "#                                                                    #\n",
        "#model_name = \"Norod78/hebrew-gpt_neo-tiny\"\n",
        "#model_name = \"Norod78/hebrew_poetry-gpt_neo-tiny\"\n",
        "#model_name = \"Norod78/hebrew-bad_wiki-gpt_neo-tiny\"\n",
        "#                                                                    #\n",
        "######################################################################\n",
        "\n",
        "seed = None\n",
        "stop_token = \"<|endoftext|>\"\n",
        "new_lines = \"\\n\\n\\n\"\n",
        "\n",
        "tokenizer_name = model_name"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "7ngeUPwu8BT2",
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      },
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        "\n",
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        "\n",
        "import argparse\n",
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        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "  \n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForCausalLM.from_pretrained(tokenizer_name, pad_token_id=tokenizer.eos_token_id)\n",
        "\n",
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        "n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count()\n",
        "\n",
        "torch.manual_seed(random_seed)\n",
        "if n_gpu > 0:\n",
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        "\n",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPTNeoForCausalLM(\n",
              "  (transformer): GPTNeoModel(\n",
              "    (wte): Embedding(50257, 2048)\n",
              "    (wpe): Embedding(2048, 2048)\n",
              "    (drop): Dropout(p=0, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (1): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (2): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (3): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (4): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (5): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (6): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (7): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (8): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (9): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (10): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (11): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (12): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (13): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (14): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (15): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (16): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (17): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (18): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (19): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (20): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (21): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (22): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "      (23): GPTNeoBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTNeoAttention(\n",
              "          (attention): GPTNeoLocalSelfAttention(\n",
              "            (attn_dropout): Dropout(p=0, inplace=False)\n",
              "            (resid_dropout): Dropout(p=0, inplace=False)\n",
              "            (k_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (v_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (q_proj): Linear(in_features=2048, out_features=2048, bias=False)\n",
              "            (out_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          )\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTNeoMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (dropout): Dropout(p=0, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=2048, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ESIl1MJ-9v1q",
        "outputId": "a3ff04f7-8d87-4c16-fb21-9ac866362df6"
      },
      "source": [
        "!git clone https://github.com/gpt2ent/gpt2colab-js.git\n",
        "%cd gpt2colab-js"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'gpt2colab-js' already exists and is not an empty directory.\n",
            "/content/gpt2colab-js\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGUX9yKxnaRe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b926d74-7085-4a5d-c459-999a2bf7ce15"
      },
      "source": [
        "generate_count = 0\n",
        "spinner_speed = \"1200ms\"\n",
        "\n",
        "import google.colab.output\n",
        "\n",
        "def overlap(a, b):\n",
        "    return max(i for i in range(len(b)+1) if a.endswith(b[:i]))\n",
        "\n",
        "\n",
        "def ai_generate(input_text, top_p, top_k, max_size):\n",
        "    global generate_count\n",
        "\n",
        "    top_p = float(top_p)\n",
        "    top_k = int(top_k)\n",
        "    length = int(max_size)\n",
        "\n",
        "    if len(input_text) == 0:\n",
        "        input_text = \" \"\n",
        "\n",
        "    encoded_prompt = tokenizer.encode(\n",
        "    input_text, add_special_tokens=False, return_tensors=\"pt\")\n",
        "\n",
        "    encoded_prompt = encoded_prompt.to(device)\n",
        "\n",
        "    if encoded_prompt.size()[-1] == 0:\n",
        "        input_ids = None\n",
        "    else:\n",
        "        input_ids = encoded_prompt\n",
        "    \n",
        "    output_sequences = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    max_length=length + len(encoded_prompt[0]),\n",
        "    top_k=top_k, \n",
        "    top_p=top_p, \n",
        "    do_sample=True,\n",
        "    num_return_sequences=1)\n",
        "\n",
        "    # Remove the batch dimension when returning multiple sequences\n",
        "    if len(output_sequences.shape) > 2:\n",
        "        output_sequences.squeeze_()\n",
        "\n",
        "    generated_sequences = []\n",
        "\n",
        "    for generated_sequence_idx, generated_sequence in enumerate(output_sequences):        \n",
        "        generated_sequence = generated_sequence.tolist()\n",
        "\n",
        "        # Decode text\n",
        "        text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)\n",
        "\n",
        "        # Remove all text after the stop token\n",
        "        text = text[: text.find(stop_token) if stop_token else None]\n",
        "\n",
        "        # Remove all text after 3 newlines\n",
        "        text = text[: text.find(new_lines) if new_lines else None]\n",
        "\n",
        "        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing\n",
        "        total_sequence = (\n",
        "            input_text + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]\n",
        "        )\n",
        "\n",
        "        generated_sequences.append(total_sequence)\n",
        "    \n",
        "    parsed_text = total_sequence.replace(\"<|startoftext|>\", \"\").replace(\"\\r\",\"\").replace(\"\\n\\n\", \"\\n\")\n",
        "    if len(parsed_text) == 0:\n",
        "        parsed_text = \"שגיאה\"\n",
        "    else:\n",
        "      j = overlap(input_text, parsed_text)\n",
        "      if j > 0 :      \n",
        "        parsed_text = parsed_text[j:]\n",
        "\n",
        "    generate_count += 1\n",
        "    return parsed_text\n",
        "\n",
        "#register callback for Javascript\n",
        "google.colab.output.register_callback('ai_generate', ai_generate)\n",
        "\n",
        "print('Done')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WqDfpKfLil8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6e107c3b-abd5-4985-d6f2-89432c0c45dc"
      },
      "source": [
        "%cd /content/gpt2colab-js/\n",
        "ai_generate(\"ברוכים הבאים\", 0.92, 50, 10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gpt2colab-js\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' לעולם המיחזור. אם אתם מתעניינים בנו'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdyRipC0o8vR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "e10550aa-ffe2-400a-e8ad-846448cbffd8"
      },
      "source": [
        "from IPython.display import HTML\n",
        "\n",
        "#spinner from https://codepen.io/vovchisko/pen/vROoYQ\n",
        "spinner_css = \"\"\"\n",
        "<style>\n",
        "@keyframes c-inline-spinner-kf {\n",
        "  0% {\n",
        "    transform: rotate(0deg);\n",
        "  }\n",
        "  100% {\n",
        "    transform: rotate(360deg);\n",
        "  }\n",
        "}\n",
        "\n",
        ".c-inline-spinner,\n",
        ".c-inline-spinner:before {\n",
        "  display: inline-block;\n",
        "  width: 11px;\n",
        "  height: 11px;\n",
        "  transform-origin: 50%;\n",
        "  border: 2px solid transparent;\n",
        "  border-color: #74a8d0 #74a8d0 transparent transparent;\n",
        "  border-radius: 50%;\n",
        "  content: \"\";\n",
        "  animation: linear c-inline-spinner-kf \"\"\"+spinner_speed+\"\"\" infinite;\n",
        "  position: relative;\n",
        "  vertical-align: inherit;\n",
        "  line-height: inherit;\n",
        "}\n",
        ".c-inline-spinner {\n",
        "  top: 3px;\n",
        "  margin: 0 3px;\n",
        "}\n",
        ".c-inline-spinner:before {\n",
        "  border-color: #74a8d0 #74a8d0 transparent transparent;\n",
        "  position: absolute;\n",
        "  left: -2px;\n",
        "  top: -2px;\n",
        "  border-style: solid;\n",
        "}\n",
        "</style>\n",
        "\"\"\"\n",
        "\n",
        "input_form = \"\"\"\n",
        "<link rel=\"stylesheet\" href=\"https://unpkg.com/purecss@1.0.1/build/pure-min.css\" integrity=\"sha384-oAOxQR6DkCoMliIh8yFnu25d7Eq/PHS21PClpwjOTeU2jRSq11vu66rf90/cZr47\" crossorigin=\"anonymous\">\n",
        "\n",
        "<div style=\"background-color:white; border:solid #ccc; width:800px; padding:20px; color: black;\">\n",
        "<p>You have currently loaded </p>\n",
        "<p> %s model</p>\n",
        "<textarea dir=\"rtl\" id=\"main_textarea\" cols=\"40\" rows=\"10\" style=\"font-family: 'Liberation Serif', 'DejaVu Serif', Georgia, 'Times New Roman', Times, serif; font-size: 13pt; padding:10px;\"></textarea><br>\n",
        "<div class=\"pure-form pure-form-aligned\">\n",
        "    <div class=\"pure-control-group\">\n",
        "      <label for=\"top_p\">top_p:</label>\n",
        "      <input type=\"number\" min=\"0.00\" max=\"1.00\" step=\"0.5\" id=\"top_p\" value=\"0.95\" style=\"background-color: white;\">\n",
        "    </div>\n",
        "    <div class=\"pure-control-group\">\n",
        "        <label for=\"top_k\">top_k:</label>\n",
        "        <input type=\"number\" min=\"0\" max=\"9999\" id=\"top_k\" value=\"50\" style=\"background-color: white;\">\n",
        "    </div>\n",
        "    <div class=\"pure-control-group\">\n",
        "        <label for=\"length\">Generate how much:</label>\n",
        "        <input type=\"number\" id=\"length\" min=\"1\" max=\"2043\" value=\"48\" style=\"background-color: white;\">\n",
        "    </div>\n",
        "    <div style=\"width: 300px; display: block; margin-left: auto !important; margin-right: auto !important;\">\n",
        "        <p><button class=\"pure-button pure-button-primary\" style=\"font-size: 125%%;\" onclick=\"generate()\">המשך</button>\n",
        "        <span class=\"c-inline-spinner\" style=\"visibility: hidden;\" id=\"spinner\"></span></p>\n",
        "    </div>\n",
        "</div>\n",
        "</div>\n",
        "\"\"\" % model_name\n",
        "\n",
        "javascript = \"\"\"\n",
        "<script type=\"text/Javascript\">\n",
        "    function desanitize(text) {\n",
        "        return text.slice(1,-1).replace(/\\\\\\\\n/g, \"\\\\n\").replace(/\\\\\\\\'/g, \"'\");\n",
        "    };\n",
        "\n",
        "    function add_text(text) {\n",
        "        var deftext = document.getElementById('main_textarea').value;\n",
        "        document.getElementById('main_textarea').value = deftext + text;\n",
        "    };\n",
        "\n",
        "    function generate(){\n",
        "        var prefix = document.getElementById('main_textarea').value;\n",
        "        var top_p = document.getElementById('top_p').value;\n",
        "        var top_k = document.getElementById('top_k').value;\n",
        "        var length = document.getElementById('length').value;\n",
        "        \n",
        "        var kernel = google.colab.kernel;\n",
        "        var resultPromise = kernel.invokeFunction(\"ai_generate\", [prefix,top_p,top_k,length]); // developer, look here\n",
        "        resultPromise.then(\n",
        "            function(value) {\n",
        "              add_text(desanitize(value.data[\"text/plain\"]));\n",
        "              document.getElementById('spinner').style = \"visibility: hidden;\";\n",
        "        });\n",
        "        document.getElementById('spinner').style = \"visibility: visible;\";\n",
        "    };\n",
        "</script>\n",
        "\"\"\"\n",
        "\n",
        "HTML(spinner_css + input_form + javascript)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "@keyframes c-inline-spinner-kf {\n",
              "  0% {\n",
              "    transform: rotate(0deg);\n",
              "  }\n",
              "  100% {\n",
              "    transform: rotate(360deg);\n",
              "  }\n",
              "}\n",
              "\n",
              ".c-inline-spinner,\n",
              ".c-inline-spinner:before {\n",
              "  display: inline-block;\n",
              "  width: 11px;\n",
              "  height: 11px;\n",
              "  transform-origin: 50%;\n",
              "  border: 2px solid transparent;\n",
              "  border-color: #74a8d0 #74a8d0 transparent transparent;\n",
              "  border-radius: 50%;\n",
              "  content: \"\";\n",
              "  animation: linear c-inline-spinner-kf 1200ms infinite;\n",
              "  position: relative;\n",
              "  vertical-align: inherit;\n",
              "  line-height: inherit;\n",
              "}\n",
              ".c-inline-spinner {\n",
              "  top: 3px;\n",
              "  margin: 0 3px;\n",
              "}\n",
              ".c-inline-spinner:before {\n",
              "  border-color: #74a8d0 #74a8d0 transparent transparent;\n",
              "  position: absolute;\n",
              "  left: -2px;\n",
              "  top: -2px;\n",
              "  border-style: solid;\n",
              "}\n",
              "</style>\n",
              "\n",
              "<link rel=\"stylesheet\" href=\"https://unpkg.com/purecss@1.0.1/build/pure-min.css\" integrity=\"sha384-oAOxQR6DkCoMliIh8yFnu25d7Eq/PHS21PClpwjOTeU2jRSq11vu66rf90/cZr47\" crossorigin=\"anonymous\">\n",
              "\n",
              "<div style=\"background-color:white; border:solid #ccc; width:800px; padding:20px; color: black;\">\n",
              "<p>You have currently loaded </p>\n",
              "<p> Norod78/hebrew-gpt_neo-xl model</p>\n",
              "<textarea dir=\"rtl\" id=\"main_textarea\" cols=\"40\" rows=\"10\" style=\"font-family: 'Liberation Serif', 'DejaVu Serif', Georgia, 'Times New Roman', Times, serif; font-size: 13pt; padding:10px;\"></textarea><br>\n",
              "<div class=\"pure-form pure-form-aligned\">\n",
              "    <div class=\"pure-control-group\">\n",
              "      <label for=\"top_p\">top_p:</label>\n",
              "      <input type=\"number\" min=\"0.00\" max=\"1.00\" step=\"0.5\" id=\"top_p\" value=\"0.95\" style=\"background-color: white;\">\n",
              "    </div>\n",
              "    <div class=\"pure-control-group\">\n",
              "        <label for=\"top_k\">top_k:</label>\n",
              "        <input type=\"number\" min=\"0\" max=\"9999\" id=\"top_k\" value=\"50\" style=\"background-color: white;\">\n",
              "    </div>\n",
              "    <div class=\"pure-control-group\">\n",
              "        <label for=\"length\">Generate how much:</label>\n",
              "        <input type=\"number\" id=\"length\" min=\"1\" max=\"2043\" value=\"48\" style=\"background-color: white;\">\n",
              "    </div>\n",
              "    <div style=\"width: 300px; display: block; margin-left: auto !important; margin-right: auto !important;\">\n",
              "        <p><button class=\"pure-button pure-button-primary\" style=\"font-size: 125%;\" onclick=\"generate()\">המשך</button>\n",
              "        <span class=\"c-inline-spinner\" style=\"visibility: hidden;\" id=\"spinner\"></span></p>\n",
              "    </div>\n",
              "</div>\n",
              "</div>\n",
              "\n",
              "<script type=\"text/Javascript\">\n",
              "    function desanitize(text) {\n",
              "        return text.slice(1,-1).replace(/\\\\n/g, \"\\n\").replace(/\\\\'/g, \"'\");\n",
              "    };\n",
              "\n",
              "    function add_text(text) {\n",
              "        var deftext = document.getElementById('main_textarea').value;\n",
              "        document.getElementById('main_textarea').value = deftext + text;\n",
              "    };\n",
              "\n",
              "    function generate(){\n",
              "        var prefix = document.getElementById('main_textarea').value;\n",
              "        var top_p = document.getElementById('top_p').value;\n",
              "        var top_k = document.getElementById('top_k').value;\n",
              "        var length = document.getElementById('length').value;\n",
              "        \n",
              "        var kernel = google.colab.kernel;\n",
              "        var resultPromise = kernel.invokeFunction(\"ai_generate\", [prefix,top_p,top_k,length]); // developer, look here\n",
              "        resultPromise.then(\n",
              "            function(value) {\n",
              "              add_text(desanitize(value.data[\"text/plain\"]));\n",
              "              document.getElementById('spinner').style = \"visibility: hidden;\";\n",
              "        });\n",
              "        document.getElementById('spinner').style = \"visibility: visible;\";\n",
              "    };\n",
              "</script>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    }
  ]
}