fine_tune_phi.ipynb

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      "execution_count": null,
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        {
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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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      "source": [
        "# **در این قسمت کتابخانه های مورد نیاز را وارد میکنیم**"
      ],
      "metadata": {
        "id": "iGFp5C88req4"
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        "from datasets import load_dataset, Dataset\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling, BitsAndBytesConfig\n",
        "from peft import LoraConfig, get_peft_model\n",
        "import os\n",
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        "# **در این قسمت توکنایزر مدلی که میخوایم فاین توین کنیم را از هاگینگ فیس دانلود میکنیم و آن را کانفیگ میکنیم. **"
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      },
      "execution_count": null,
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        {
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              "tokenizer_config.json:   0%|          | 0.00/237 [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"microsoft/phi-1_5\",\n",
        "    device_map={\"\":0},\n",
        "    trust_remote_code=True,\n",
        "    quantization_config=bnb_config\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336,
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/864 [00:00<?, ?B/s]"
            ],
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          "data": {
            "text/plain": [
              "configuration_phi.py:   0%|          | 0.00/9.26k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-1_5:\n",
            "- configuration_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_phi.py:   0%|          | 0.00/62.7k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-1_5:\n",
            "- modeling_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/2.84G [00:00<?, ?B/s]"
            ],
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              "model_id": "819126764c6d4d5c92cb0fde8d95a7f2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
            "  return self.fget.__get__(instance, owner)()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/74.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POPRm1m99I-d",
        "outputId": "1db04c74-fadc-4a4a-c499-48b8ea87d8cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PhiForCausalLM(\n",
              "  (model): PhiModel(\n",
              "    (embed_tokens): Embedding(51200, 2048)\n",
              "    (embed_dropout): Dropout(p=0.0, inplace=False)\n",
              "    (layers): ModuleList(\n",
              "      (0-23): 24 x PhiDecoderLayer(\n",
              "        (self_attn): PhiAttention(\n",
              "          (q_proj): Linear4bit(in_features=2048, out_features=2048, bias=True)\n",
              "          (k_proj): Linear4bit(in_features=2048, out_features=2048, bias=True)\n",
              "          (v_proj): Linear4bit(in_features=2048, out_features=2048, bias=True)\n",
              "          (dense): Linear4bit(in_features=2048, out_features=2048, bias=True)\n",
              "          (rotary_emb): PhiRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): PhiMLP(\n",
              "          (activation_fn): NewGELUActivation()\n",
              "          (fc1): Linear4bit(in_features=2048, out_features=8192, bias=True)\n",
              "          (fc2): Linear4bit(in_features=8192, out_features=2048, bias=True)\n",
              "        )\n",
              "        (input_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (final_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=2048, out_features=51200, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=16,\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"dense\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "model.print_trainable_parameters()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29s6NU4k95VC",
        "outputId": "dd6070de-c73d-488c-8a40-200cdf896bca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 6,291,456 || all params: 1,424,562,176 || trainable%: 0.4416413762764399\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "id": "32M87ley_fPo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(sample):\n",
        "    model_inps =  tokenizer(sample[\"query\"], padding=True, truncation=True, max_length=512)\n",
        "    return model_inps"
      ],
      "metadata": {
        "id": "V4m25e_1_l-0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **دیتاست مورد نظر را از کتابخانه سایکت لیرن دانلود میکنیم و آن را به دو قسمت تست و ترین تقسیم میکنیم**"
      ],
      "metadata": {
        "id": "-FsLFXR5AIqd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_20newsgroups\n",
        "newsgroups_train = fetch_20newsgroups(subset='train')"
      ],
      "metadata": {
        "id": "yGv_DHNe_l4-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(newsgroups_train[\"data\"], newsgroups_train[\"target\"], test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "3ap-f8GrAR5i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert training data to a DataFrame\n",
        "train_df = pd.DataFrame({\"text\": X_train, \"target\": y_train})\n",
        "\n",
        "# Convert testing data to a DataFrame\n",
        "test_df = pd.DataFrame({\"text\": X_test, \"target\": y_test})"
      ],
      "metadata": {
        "id": "LgZBwFdGBpsz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "target_map = {i: newsgroups_train.target_names[i] for i in range(len(newsgroups_train.target_names))}\n",
        "\n",
        "# Map target values to class names in the training and testing DataFrames\n",
        "train_df['target'] = train_df['target'].map(target_map)\n",
        "test_df['target'] = test_df['target'].map(target_map)"
      ],
      "metadata": {
        "id": "OqgdTrrnCiGt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vfivC3PIB5q2",
        "outputId": "b0cb881a-b044-4996-baeb-9f33ff036874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text                    target\n",
              "0  From: Rupin.Dang@dartmouth.edu (Rupin Dang)\\nS...              misc.forsale\n",
              "1  From: simon@giaeb.cc.monash.edu.au\\nSubject: S...    soc.religion.christian\n",
              "2  From: sun075!Gerry.Palo@uunet.uu.net (Gerry Pa...    soc.religion.christian\n",
              "3  From: downs@helios.nevada.edu (Lamont Downs)\\n...   comp.os.ms-windows.misc\n",
              "4  From: U52885@uicvm.uic.edu\\nSubject: Re: Gatew...  comp.sys.ibm.pc.hardware"
            ],
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              "      <td>From: Rupin.Dang@dartmouth.edu (Rupin Dang)\\nS...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>From: simon@giaeb.cc.monash.edu.au\\nSubject: S...</td>\n",
              "      <td>soc.religion.christian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>From: sun075!Gerry.Palo@uunet.uu.net (Gerry Pa...</td>\n",
              "      <td>soc.religion.christian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>From: downs@helios.nevada.edu (Lamont Downs)\\n...</td>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>From: U52885@uicvm.uic.edu\\nSubject: Re: Gatew...</td>\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3e5b0fd7-d30e-4139-999d-43e90895dc64 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3e5b0fd7-d30e-4139-999d-43e90895dc64');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-46272b43-96c5-4903-8fcf-6663e568d9a7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-46272b43-96c5-4903-8fcf-6663e568d9a7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-46272b43-96c5-4903-8fcf-6663e568d9a7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **دیتا را با توجه به نیاز های مدل اماده میکنیم**"
      ],
      "metadata": {
        "id": "sKvqzBBCAcuD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df[\"query\"] = train_df[[\"text\", \"target\"]].apply(lambda x: \"question: \" + x[\"text\"] + \" answer: \" + x[\"target\"], axis=1)\n",
        "data = Dataset.from_pandas(train_df)\n",
        "tokenized_data = data.map(tokenize, batched=True, desc=\"Tokenizing data\", remove_columns=data.column_names)\n",
        "tokenized_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "71d7da6b3bee407798a101807931bf17",
            "bfaabe13716f4dd4aaabb3e749653c4d",
            "a8ef5c7adaa44da08c9d3a36e77f72d8",
            "d51581e4053240e1a97b338b0a5f4a2a",
            "ad7e66e4b9964ad49e114ccee5dc89d3",
            "7d968ab7bf10421c9fa5ac188903fd0c",
            "2997b1e0d6454b98b0eb8843ce44999a",
            "a98cef96d7ec47ff8a07140f426e6930",
            "15e1123d52db43beb96a554cbaf63875",
            "6d9f4ae8d7034ad881f571a713c30115",
            "58f4491ad21544b4b845d7b7d109ab44"
          ]
        },
        "id": "dMZUhX-8B08o",
        "outputId": "270f89dc-344f-4711-fd24-55d5715aeb30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing data:   0%|          | 0/9051 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71d7da6b3bee407798a101807931bf17"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'attention_mask'],\n",
              "    num_rows: 9051\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **پارامتر های اموزش را ست میکنیم و مدل را آموزش میدهیم**"
      ],
      "metadata": {
        "id": "T5Ibqvm1Aj62"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "training_arguments = TrainingArguments(\n",
        "        output_dir=\"phi-1_5-finetuned\",\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=1,\n",
        "        learning_rate=2e-4,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        logging_steps=100,\n",
        "        max_steps=1000,\n",
        "        num_train_epochs=1,\n",
        "        push_to_hub=True,\n",
        "        optim=\"paged_adamw_32bit\",\n",
        "    )"
      ],
      "metadata": {
        "id": "i0Si3o0DDFYv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_data,\n",
        "    args=training_arguments,\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "trainer.train()\n",
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "YumriopaFkI8",
        "outputId": "fb3c0821-a21f-4afe-c4cd-fb06597db8c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1000/1000 20:34, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>3.203100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>3.015600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>3.003700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>2.936700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>2.928200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>2.899600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>2.902700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>2.848400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>2.888800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>2.883600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/mahdihassanzadeh/phi-1_5-finetuned/commit/15bc2099dff9548b6c181dfa942d972ee54c69c6', commit_message='End of training', commit_description='', oid='15bc2099dff9548b6c181dfa942d972ee54c69c6', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **مدل را در سایت هاگینگ فیس  آپلود میکنیم.**"
      ],
      "metadata": {
        "id": "Dih_zEfxAx7K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "from transformers import AutoModelForCausalLM\n",
        "import torch\n",
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True, torch_dtype=torch.float32)\n",
        "peft_model = PeftModel.from_pretrained(model, \"mahdihassanzadeh/phi-1_5-finetuned\", from_transformers=True)\n",
        "model = peft_model.merge_and_unload()\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532,
          "referenced_widgets": [
            "6374517b78ae42529b7a7c9957493ecb",
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            "8ad55905a341488fa6f1d66413708559",
            "a01e47e449f046cea6272451c88592e3",
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            "94c79ca8ed8e4702a346656ff7a13856",
            "3520ee27217c4a9faf280b56dfc753de"
          ]
        },
        "id": "0TXvLmflL6K9",
        "outputId": "21a9f94d-1a6f-4f67-cb7b-4f143bfb24a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_config.json:   0%|          | 0.00/605 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6374517b78ae42529b7a7c9957493ecb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/25.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08f08dff8ca44041aea621e7aa76acc0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PhiForCausalLM(\n",
              "  (model): PhiModel(\n",
              "    (embed_tokens): Embedding(51200, 2048)\n",
              "    (embed_dropout): Dropout(p=0.0, inplace=False)\n",
              "    (layers): ModuleList(\n",
              "      (0-23): 24 x PhiDecoderLayer(\n",
              "        (self_attn): PhiAttention(\n",
              "          (q_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          (k_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          (v_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          (dense): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          (rotary_emb): PhiRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): PhiMLP(\n",
              "          (activation_fn): NewGELUActivation()\n",
              "          (fc1): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (fc2): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "        )\n",
              "        (input_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "      )\n",
              "    )\n",
              "    (final_layernorm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=2048, out_features=51200, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"mahdihassanzadeh/phi-1_5-finetuned\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197,
          "referenced_widgets": [
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            "0e70c68366a0401aaed12b43d44c7e22",
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