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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.makedirs(\"tinygpt_base\", exist_ok=True)\n",
        "print(\"Folder recreated!\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yL26uQjMEWhe",
        "outputId": "7c261948-9901-42e8-b6f6-5b7f3d51e7ab"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Folder recreated!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import snapshot_download\n",
        "\n",
        "snapshot_download(\n",
        "    repo_id=\"Abdurrahmanesc/tinygpt-base-model\",\n",
        "    local_dir=\"./tinygpt_base\",\n",
        "    local_dir_use_symlinks=False\n",
        ")\n",
        "\n",
        "print(\"Model pulled from HF!\")\n"
      ],
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        },
        "id": "3Gzdp_FAEYHB",
        "outputId": "805f744a-1acb-4709-81b9-fd95d712b280"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/file_download.py:979: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n",
            "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n",
            "  warnings.warn(\n"
          ]
        },
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              "Fetching 13 files:   0%|          | 0/13 [00:00<?, ?it/s]"
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              "model_id": "09cded50c71e4faba349a125e93170a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model pulled from HF!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9db5vU-i8od7",
        "outputId": "51d4c97e-9081-4057-dfb5-f3790b05e176"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "configuration_tinygpt.py created!\n"
          ]
        }
      ],
      "source": [
        "tinygpt_config_text = \"\"\"\n",
        "from transformers.configuration_utils import PretrainedConfig\n",
        "\n",
        "class TinyGPTConfig(PretrainedConfig):\n",
        "    model_type = \"tinygpt\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        vocab_size=30522,\n",
        "        n_layers=4,\n",
        "        n_heads=4,\n",
        "        d_model=256,\n",
        "        d_ff=1024,\n",
        "        max_seq_len=512,\n",
        "        **kwargs\n",
        "    ):\n",
        "        super().__init__(**kwargs)\n",
        "        self.vocab_size = vocab_size\n",
        "        self.n_layers = n_layers\n",
        "        self.n_heads = n_heads\n",
        "        self.d_model = d_model\n",
        "        self.d_ff = d_ff\n",
        "        self.max_seq_len = max_seq_len\n",
        "\"\"\"\n",
        "\n",
        "with open(\"./tinygpt_base/configuration_tinygpt.py\", \"w\") as f:\n",
        "    f.write(tinygpt_config_text)\n",
        "\n",
        "print(\"configuration_tinygpt.py created!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tinygpt_model_text = \"\"\"\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.nn import CrossEntropyLoss\n",
        "from transformers.modeling_outputs import CausalLMOutput\n",
        "from transformers.modeling_utils import PreTrainedModel\n",
        "from configuration_tinygpt import TinyGPTConfig # Changed from relative to absolute import\n",
        "\n",
        "class TinyGPTForCausalLM(PreTrainedModel):\n",
        "    config_class = TinyGPTConfig\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super().__init__(config)\n",
        "\n",
        "        self.embed = nn.Embedding(config.vocab_size, config.d_model)\n",
        "        self.pos_embed = nn.Embedding(config.max_seq_len, config.d_model)\n",
        "\n",
        "        self.blocks = nn.ModuleList([\n",
        "            nn.TransformerEncoderLayer(\n",
        "                d_model=config.d_model,\n",
        "                nhead=config.n_heads,\n",
        "                dim_feedforward=config.d_ff,\n",
        "                batch_first=True\n",
        "            )\n",
        "            for _ in range(config.n_layers)\n",
        "        ])\n",
        "\n",
        "        self.norm = nn.LayerNorm(config.d_model)\n",
        "        self.lm_head = nn.Linear(config.d_model, config.vocab_size)\n",
        "\n",
        "        self.post_init()\n",
        "\n",
        "    def forward(self, input_ids, labels=None):\n",
        "        B, T = input_ids.shape\n",
        "        positions = torch.arange(T, device=input_ids.device).unsqueeze(0)\n",
        "\n",
        "        x = self.embed(input_ids) + self.pos_embed(positions)\n",
        "\n",
        "        for blk in self.blocks:\n",
        "            x = blk(x)\n",
        "\n",
        "        x = self.norm(x)\n",
        "        logits = self.lm_head(x)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            shift_logits = logits[:, :-1, :].contiguous()\n",
        "            shift_labels = labels[:, 1:].contiguous()\n",
        "            loss_fct = CrossEntropyLoss()\n",
        "            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),\n",
        "                            shift_labels.view(-1))\n",
        "\n",
        "        return CausalLMOutput(\n",
        "            loss=loss,\n",
        "            logits=logits\n",
        "        )\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def generate(self, input_ids, max_new_tokens=50):\n",
        "        for _ in range(max_new_tokens):\n",
        "            logits = self.forward(input_ids).logits\n",
        "            next_token = torch.argmax(logits[:, -1, :], dim=-1)\n",
        "            input_ids = torch.cat([input_ids, next_token[:, None]], dim=1)\n",
        "        return input_ids\n",
        "\"\"\"\n",
        "\n",
        "with open(\"./tinygpt_base/modeling_tinygpt.py\", \"w\") as f:\n",
        "    f.write(tinygpt_model_text)\n",
        "\n",
        "print(\"modeling_tinygpt.py created!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LQoJsIFEjWp",
        "outputId": "74c372f4-424a-47dc-b341-d0b03af20bcb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "modeling_tinygpt.py created!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNkns5H5Hrot",
        "outputId": "3f6ad90a-93c4-4f6a-bf5c-30a22017cc36"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.12/dist-packages (4.57.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers) (3.20.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.34.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.36.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2.0.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (25.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers) (6.0.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.12/dist-packages (from transformers) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from transformers) (2.32.4)\n",
            "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.22.1)\n",
            "Requirement already satisfied: safetensors>=0.4.3 in /usr/local/lib/python3.12/dist-packages (from transformers) (0.6.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers) (4.67.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (2025.3.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (4.15.0)\n",
            "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<1.0,>=0.34.0->transformers) (1.2.0)\n",
            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (3.4.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (3.11)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (2.5.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->transformers) (2025.10.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "import os\n",
        "\n",
        "# Add the directory containing the custom model files to sys.path\n",
        "model_dir = \"./tinygpt_base\"\n",
        "if model_dir not in sys.path:\n",
        "    sys.path.insert(0, model_dir)\n",
        "\n",
        "# Explicitly import the custom configuration and model classes\n",
        "from configuration_tinygpt import TinyGPTConfig\n",
        "from modeling_tinygpt import TinyGPTForCausalLM\n",
        "\n",
        "# Register the custom model with AutoConfig and AutoModelForCausalLM\n",
        "from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "AutoConfig.register(\"tinygpt\", TinyGPTConfig)\n",
        "AutoModelForCausalLM.register(TinyGPTConfig, TinyGPTForCausalLM)\n",
        "\n",
        "# Now, load the model from the local directory (trust_remote_code=True is no longer needed after explicit registration)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_dir\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"Abdurrahmanesc/tinygpt-base-model\")\n",
        "\n",
        "\n",
        "inputs = tokenizer(\"Hello TinyGPT!\", return_tensors=\"pt\")\n",
        "outputs = model.generate(inputs[\"input_ids\"], max_new_tokens=30)\n",
        "\n",
        "print(tokenizer.decode(outputs[0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
          "referenced_widgets": [
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        },
        "id": "U86dcczSEpAX",
        "outputId": "42494a47-6fcf-4110-dca9-cdeb98de37ac"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of TinyGPTForCausalLM were not initialized from the model checkpoint at ./tinygpt_base and are newly initialized: ['blocks.0.linear1.bias', 'blocks.0.linear1.weight', 'blocks.0.linear2.bias', 'blocks.0.linear2.weight', 'blocks.0.norm1.bias', 'blocks.0.norm1.weight', 'blocks.0.norm2.bias', 'blocks.0.norm2.weight', 'blocks.0.self_attn.in_proj_bias', 'blocks.0.self_attn.in_proj_weight', 'blocks.0.self_attn.out_proj.bias', 'blocks.0.self_attn.out_proj.weight', 'blocks.1.linear1.bias', 'blocks.1.linear1.weight', 'blocks.1.linear2.bias', 'blocks.1.linear2.weight', 'blocks.1.norm1.bias', 'blocks.1.norm1.weight', 'blocks.1.norm2.bias', 'blocks.1.norm2.weight', 'blocks.1.self_attn.in_proj_bias', 'blocks.1.self_attn.in_proj_weight', 'blocks.1.self_attn.out_proj.bias', 'blocks.1.self_attn.out_proj.weight', 'blocks.2.linear1.bias', 'blocks.2.linear1.weight', 'blocks.2.linear2.bias', 'blocks.2.linear2.weight', 'blocks.2.norm1.bias', 'blocks.2.norm1.weight', 'blocks.2.norm2.bias', 'blocks.2.norm2.weight', 'blocks.2.self_attn.in_proj_bias', 'blocks.2.self_attn.in_proj_weight', 'blocks.2.self_attn.out_proj.bias', 'blocks.2.self_attn.out_proj.weight', 'blocks.3.linear1.bias', 'blocks.3.linear1.weight', 'blocks.3.linear2.bias', 'blocks.3.linear2.weight', 'blocks.3.norm1.bias', 'blocks.3.norm1.weight', 'blocks.3.norm2.bias', 'blocks.3.norm2.weight', 'blocks.3.self_attn.in_proj_bias', 'blocks.3.self_attn.in_proj_weight', 'blocks.3.self_attn.out_proj.bias', 'blocks.3.self_attn.out_proj.weight', 'embed.weight', 'lm_head.bias', 'lm_head.weight', 'norm.bias', 'norm.weight', 'pos_embed.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/674 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ac487f36414466cb439076e0f65331c"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2fcd2ef454564699b0925102d93e18ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/23.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4171e2a8c45d47c7b2eff6c24ea74174"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21de4cad19ca4162ae8e9e437fdb21d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello TinyGPT! Admir?).vaspectHer vain breakdown beaten Washington offending taste unacceptable healthier startling�CHATovtable chicken�essel Sask Grayag outright339 managerserveDi1988\n"
          ]
        }
      ]
    }
  ]
}