summary_LLM_model.ipynb

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   "metadata": {
    "id": "HRSWeK5LMooR"
   },
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
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    "outputId": "70b1ab01-b837-4c80-923b-b118da0f8adb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  /content/drive/My Drive/Content-Summary_CleanData.zip\n",
      "  inflating: /content/extracted_files/Content-Summary_CleanData.csv  \n"
     ]
    }
   ],
   "source": [
    "!unzip \"/content/drive/My Drive/Content-Summary_CleanData.zip\" -d \"/content/extracted_files\""
   ]
  },
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      "text/plain": [
       "                                             Content  \\\n",
       "0  New York police are concerned drones could bec...   \n",
       "1  By . Ryan Lipman . Perhaps Australian porn sta...   \n",
       "2  This was, Sergio Garcia conceded, much like be...   \n",
       "3  An Ebola outbreak that began in Guinea four mo...   \n",
       "4  By . Associated Press and Daily Mail Reporter ...   \n",
       "\n",
       "                                             Summary  \n",
       "0  Police have investigated criminals who have ri...  \n",
       "1  Porn star Angela White secretly filmed sex act...  \n",
       "2  American draws inspiration from fellow country...  \n",
       "3  World Health Organisation: 635 infections and ...  \n",
       "4  A sinkhole opened up at 5:15am this morning in...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('/content/extracted_files/Content-Summary_CleanData.csv')\n",
    "df.head()"
   ]
  },
  {
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      "Collecting triton (from unsloth_zoo>=2024.11.8->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
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      "Collecting trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9 (from unsloth_zoo>=2024.11.8->unsloth@ git+https://github.com/unslothai/unsloth.git->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
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      "\u001b[?25hBuilding wheels for collected packages: unsloth\n",
      "  Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for unsloth: filename=unsloth-2024.11.10-py3-none-any.whl size=166794 sha256=1ef085d9a5d3cf86493d5e01274602558de42688cf16e32182ecd0322064e051\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-c64avm22/wheels/ed/d4/e9/76fb290ee3df0a5fc21ce5c2c788e29e9607a2353d8342fd0d\n",
      "Successfully built unsloth\n",
      "Installing collected packages: xxhash, unsloth, triton, shtab, protobuf, hf_transfer, fsspec, dill, multiprocess, tyro, cut_cross_entropy, bitsandbytes, datasets, trl, unsloth_zoo\n",
      "  Attempting uninstall: protobuf\n",
      "    Found existing installation: protobuf 4.25.5\n",
      "    Uninstalling protobuf-4.25.5:\n",
      "      Successfully uninstalled protobuf-4.25.5\n",
      "  Attempting uninstall: fsspec\n",
      "    Found existing installation: fsspec 2024.10.0\n",
      "    Uninstalling fsspec-2024.10.0:\n",
      "      Successfully uninstalled fsspec-2024.10.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n",
      "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed bitsandbytes-0.44.1 cut_cross_entropy-24.11.4 datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 hf_transfer-0.1.8 multiprocess-0.70.16 protobuf-3.20.3 shtab-1.7.1 triton-3.1.0 trl-0.12.1 tyro-0.9.2 unsloth-2024.11.10 unsloth_zoo-2024.11.8 xxhash-3.5.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "id": "17a3df5b5fbc464e89e695d34b59973d",
       "pip_warning": {
        "packages": [
         "google"
        ]
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xformers\n",
      "  Downloading xformers-0.0.28.post3-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (1.0 kB)\n",
      "Requirement already satisfied: trl in /usr/local/lib/python3.10/dist-packages (0.12.1)\n",
      "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.13.2)\n",
      "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (1.1.1)\n",
      "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.44.1)\n",
      "Requirement already satisfied: triton in /usr/local/lib/python3.10/dist-packages (3.1.0)\n",
      "Downloading xformers-0.0.28.post3-cp310-cp310-manylinux_2_28_x86_64.whl (16.7 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.7/16.7 MB\u001b[0m \u001b[31m37.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: xformers\n",
      "Successfully installed xformers-0.0.28.post3\n"
     ]
    }
   ],
   "source": [
    "#  install unsloth , xformers (flash attention) and all other packages !\n",
    "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
    "\n",
    "# we have to check which torch version for Xformers (2.3 -> 0.0.27)\n",
    "from torch import __version__; from packaging.version import Version as V\n",
    "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n",
    "\n",
    "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323,
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    },
    "id": "Vfo98EBlNRS9",
    "outputId": "a7c7dfb1-59d1-463a-e3b6-4485f54f350c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
      "==((====))==  Unsloth 2024.11.10: Fast Llama patching. Transformers:4.46.2.\n",
      "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform: Linux.\n",
      "O^O/ \\_/ \\    Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
      "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
      " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d94dce730733462ea0992dd55e170040",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c5df0c133364f2a98651942119288b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/230 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4a23ca6d9444de492992db111c9da73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bc384873f40440169e013752b6f04cb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91561d0afe694867baa90a9a9133a90a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/345 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from unsloth import FastLanguageModel\n",
    "import torch\n",
    "\n",
    "max_seq_length = 2048\n",
    "dtype = None\n",
    "load_in_4bit = True\n",
    "\n",
    "model,tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name = \"unsloth/Meta-Llama-3.1-8B\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dtype = dtype,\n",
    "    load_in_4bit = load_in_4bit,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "riWS0AFnNjDP",
    "outputId": "011eb9f1-adef-47aa-a71d-b6238cbcc024"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unsloth 2024.11.10 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
     ]
    }
   ],
   "source": [
    "model = FastLanguageModel.get_peft_model(\n",
    "    model,\n",
    "    r=16,\n",
    "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
    "                    \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0,\n",
    "    bias=\"none\",\n",
    "    use_gradient_checkpointing=\"unsloth\",\n",
    "    random_state=3407,\n",
    "    use_rslora=False,\n",
    "    loftq_config=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "fxCH8M9jNvzf"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y0le7-giOOxP"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "sNrvHFD8OPJh"
   },
   "outputs": [],
   "source": [
    "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
    "\n",
    "### Instruction:\n",
    "Your task is to understand the provided text in English and generate a summary and a title for it in english. The result should be formatted in JSON. Follow these steps:\n",
    "\n",
    "1- Comprehension:\n",
    "Understand the provided English text.\n",
    "2- Summary Generation:\n",
    "Create a brief and concise summary of the text in English.\n",
    "3- Title Generation:\n",
    "Generate a relevant title in English that accurately reflects the content and theme of the text.\n",
    "4-Output Formatting:\n",
    "Format the summary and title into a JSON object with the keys 'summary' and 'title'.\n",
    "\n",
    "### Input:\n",
    "{}\n",
    "\n",
    "### Response:\n",
    "{}\"\"\"\n",
    "\n",
    "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
    "def formatting_prompts_func(examples):\n",
    "    inputs       = examples[\"Content\"]\n",
    "    outputs      = examples[\"Summary\"]\n",
    "    texts = []\n",
    "    for input, output in zip(inputs, outputs):\n",
    "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
    "        text = alpaca_prompt.format(input, output) + EOS_TOKEN\n",
    "        texts.append(text)\n",
    "    return { \"text\" : texts, }\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "U0lH3NiiP1F7"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "SSBRiHy8RbKI"
   },
   "outputs": [],
   "source": [
    "# reducing data set from 600k to 1000\n",
    "df_subset = df.iloc[:1000]\n",
    "df_subset.to_csv(\"summary1000.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "SD6Tf-s6SI35"
   },
   "outputs": [],
   "source": [
    "df_1000 = pd.read_csv('/content/summary1000.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "LBPIqia0SI7O"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227,
     "referenced_widgets": [
      "1e53bbe6126949c4a4451d3419963f05",
      "f89570a2a30e4550a440e143a8f818c2",
      "f1c0823e1bb749cb8bae8ce3fd40e1fd",
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      "6702b39d77eb40e6893e432279028275",
      "aac7d328782d4d1ea45685617b024e2b",
      "e93cf50a5c8844809d45143d2715116d",
      "f1dfeb69154244cc8e3f0f1f5eb0c094",
      "fa34b9531e674617a91ab79d5b000ed0",
      "ebf43e30118f46bbad8e08ffccc7c5d7",
      "4163eff3279041719a9d7f8c9ed55958",
      "c484c928deae40b8b994854346ef4db5",
      "6542828c1e2c4735934fbbd3ec306400"
     ]
    },
    "id": "T1sZGO3MRDnk",
    "outputId": "d17014f5-ef87-4088-9279-c65d4735c072"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e53bbe6126949c4a4451d3419963f05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "943899bdfecc489292c5279250300ede",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset({\n",
      "    features: ['Content', 'Summary'],\n",
      "    num_rows: 800\n",
      "})\n",
      "Dataset({\n",
      "    features: ['Content', 'Summary'],\n",
      "    num_rows: 200\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from datasets import load_dataset\n",
    "# Split your DataFrame into training and testing sets (80% train, 20% test)\n",
    "train_df, test_df = train_test_split(df_1000, test_size=0.2)\n",
    "\n",
    "# Save the splits to separate CSV files\n",
    "train_df.to_csv('train_dataset.csv', index=False)\n",
    "test_df.to_csv('test_dataset.csv', index=False)\n",
    "\n",
    "# Load the datasets\n",
    "train_dataset = load_dataset(\"csv\", data_files=\"train_dataset.csv\")\n",
    "test_dataset = load_dataset(\"csv\", data_files=\"test_dataset.csv\")\n",
    "\n",
    "print(train_dataset['train'])\n",
    "print(test_dataset['train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wI7RDnjQTP2A",
    "outputId": "c5137bad-9618-495a-f6a1-ad0c7ae13024"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['Content', 'Summary'],\n",
       "    num_rows: 800\n",
       "})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "uhR913mKSdmJ"
   },
   "outputs": [],
   "source": [
    "#convert the dataset to dataframe\n",
    "train_df = train_dataset['train'].to_pandas(train_dataset['train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MYUjAMcWSdob",
    "outputId": "44ad44dd-2ad5-40c1-92ac-e886a27721e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "Ynoja9BcSdqt"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datasets import Dataset\n",
    "\n",
    "# convert the dataframe to dataset\n",
    "dataset = Dataset.from_pandas(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RXBsH4mESdtP",
    "outputId": "49a09aaa-953c-4791-f453-5e99cb4576bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['Content', 'Summary'],\n",
       "    num_rows: 800\n",
       "})"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "eaf27df8814d4b61b4fe19acb0243a31",
      "7ed85f44914e4e7291bdc6b6ce4f136b",
      "c455e311bba24d5dbb7a8c7ffadc8218",
      "39144254c90147129233d88364245f47",
      "77edf49c23d641e8be55c95ee790e2a7",
      "72ff412453244d1ba9ad28f47240cd4b",
      "755f29313fa04c0fbee195e632b2244b",
      "15e9899604394907b2a76c521e2e87c8",
      "1647bae450b04cb8a73ce987e8d7ef9b",
      "faaa5868b344432d85ddde4460eac37c",
      "a9a6c13611e44defaf6edc62e004bacd"
     ]
    },
    "id": "O_hbDVuvSdwq",
    "outputId": "38fbdacd-e1ab-4725-cac8-663ae84903ab"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eaf27df8814d4b61b4fe19acb0243a31",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/800 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = dataset.map(formatting_prompts_func, batched = True,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5U8nIAy8Z1GK"
   },
   "source": [
    "\n",
    "# Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
     "referenced_widgets": [
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      "45a1557a991640e9b78c535ce1881d2c",
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      "85692b19249e42128a015a900c0310b3"
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    },
    "id": "9MWBLaZGUlRc",
    "outputId": "79e6f432-2e44-423b-d9dc-20116052f1a9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db9d7aceddc84f7aa0df072d952fc6ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map (num_proc=2):   0%|          | 0/800 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "max_steps is given, it will override any value given in num_train_epochs\n"
     ]
    }
   ],
   "source": [
    "from trl import SFTTrainer\n",
    "from transformers import TrainingArguments\n",
    "from unsloth import is_bfloat16_supported\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model = model,\n",
    "    tokenizer = tokenizer,\n",
    "    train_dataset = dataset,\n",
    "    dataset_text_field = \"text\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dataset_num_proc = 2,\n",
    "    packing = False, # Can make training 5x faster for short sequences.\n",
    "    args = TrainingArguments(\n",
    "        per_device_train_batch_size = 2,\n",
    "        gradient_accumulation_steps = 4,\n",
    "        warmup_steps = 5,\n",
    "        max_steps = 60,\n",
    "        learning_rate = 2e-4,\n",
    "        fp16 = not is_bfloat16_supported(),\n",
    "        bf16 = is_bfloat16_supported(),\n",
    "        logging_steps = 1,\n",
    "        optim = \"adamw_8bit\",\n",
    "        weight_decay = 0.01,\n",
    "        lr_scheduler_type = \"linear\",\n",
    "        seed = 3407,\n",
    "        output_dir = \"outputs\",\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GiVB6_OHWj_e",
    "outputId": "76cfd8f3-17f9-4369-c853-031b3ef54acf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU = Tesla T4. Max memory = 14.748 GB.\n",
      "6.004 GB of memory reserved.\n"
     ]
    }
   ],
   "source": [
    "#@title Show current memory stats\n",
    "gpu_stats = torch.cuda.get_device_properties(0)\n",
    "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
    "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
    "print(f\"{start_gpu_memory} GB of memory reserved.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "w2c7icLCXkHq",
    "outputId": "520d1914-fcc7-4069-c7a0-9f577b33ed84"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
      "   \\\\   /|    Num examples = 800 | Num Epochs = 1\n",
      "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
      "\\        /    Total batch size = 8 | Total steps = 60\n",
      " \"-____-\"     Number of trainable parameters = 41,943,040\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
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       "                newScript.onerror = reject;\n",
       "                newScript.onload = resolve;\n",
       "                document.body.appendChild(newScript);\n",
       "                newScript.src = url;\n",
       "            });\n",
       "            }\n",
       "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
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       "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
       "            document.body.appendChild(iframe)\n",
       "            const handshake = new Postmate({\n",
       "                container: iframe,\n",
       "                url: 'https://wandb.ai/authorize'\n",
       "            });\n",
       "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
       "            handshake.then(function(child) {\n",
       "                child.on('authorize', data => {\n",
       "                    clearTimeout(timeout)\n",
       "                    resolve(data)\n",
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       "            })\n",
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       "    "
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
      "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ··········\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.18.7"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/content/wandb/run-20241202_172919-253qvvs1</code>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/awesome2000/huggingface/runs/253qvvs1' target=\"_blank\">outputs</a></strong> to <a href='https://wandb.ai/awesome2000/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br/>"
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       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/awesome2000/huggingface' target=\"_blank\">https://wandb.ai/awesome2000/huggingface</a>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/awesome2000/huggingface/runs/253qvvs1' target=\"_blank\">https://wandb.ai/awesome2000/huggingface/runs/253qvvs1</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='26' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [26/60 12:52 < 18:14, 0.03 it/s, Epoch 0.25/1]\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>1</td>\n",
       "      <td>2.086200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.200500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.926700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.979600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2.169600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.951700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.908400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.948300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.892600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.928100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.776100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.721600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.713900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.838700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.606700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.702000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.777200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.602400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.751100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.766000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.742100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.694700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.824900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer_stats = trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "P1s-eVj5aaMj"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 219
    },
    "id": "aKCIxfaqeWwc",
    "outputId": "b81460f7-0307-4a91-cd3d-4c31bec321be"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'torch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-21a4da9893ae>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#@title Show final memory and time stats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mused_memory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_memory_reserved\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mused_memory_for_lora\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_gpu_memory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mused_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory\u001b[0m         \u001b[0;34m/\u001b[0m\u001b[0mmax_memory\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlora_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory_for_lora\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mmax_memory\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
     ]
    }
   ],
   "source": [
    "#@title Show final memory and time stats\n",
    "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
    "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
    "used_percentage = round(used_memory         /max_memory*100, 3)\n",
    "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
    "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
    "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
    "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
    "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
    "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
    "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UfJJtc1ZeXoY"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5_Z1MhcSe94d"
   },
   "source": [
    "# Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "b53ZRmRMe3M5"
   },
   "outputs": [],
   "source": [
    "# article  = df.sample(1).iloc[0]['Content']\n",
    "\n",
    "article =\"\"\"\n",
    "The Maldives government will ban Israelis from the Indian Ocean archipelago known for white sand beaches and luxury resorts as public anger in the predominantly Muslim nation rises over the war in Gaza.\n",
    "President Mohamed Muizzu has “resolved to impose a ban on Israeli passports”, a spokesman for his office said in a statement, without giving details of when the new law would take effect.\n",
    "Muizzu also announced a national fundraising campaign called “Maldivians in Solidarity with Palestine”. Nearly 11,000 Israelis visited the Maldives last year, which was 0.6 percent of total tourist arrivals.\n",
    "Official data also shows the number of Israelis visiting the Maldives dropped to 528 in the first four months of this year, down 88 percent compared to the corresponding period last year.\n",
    "Opposition parties and government allies in the Maldives have been putting pressure on Muizzu to ban Israelis as a sign of protest against the Gaza war. At least 36,439 Palestinians have been killed and 82,627 wounded in the conflict since October 7.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 146
    },
    "id": "6HVJ0gLRe3QQ",
    "outputId": "f09c2d6c-7d60-40d0-de67-31bf1ad15875"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-2c2757241461>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Content'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "# alpaca_prompt = Copied from above\n",
    "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    alpaca_prompt.format(\n",
    "        article, # input\n",
    "        \"\", # output - leave this blank for generation!\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
    "tokenizer.batch_decode(outputs)\n",
    "\n"
   ]
  },
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   "execution_count": null,
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    "\n",
    "# we can also use a TextStreamer for continuous inference - so you can see the generation token by token, instead of waiting the whole time!\n",
    "\n",
    "# alpaca_prompt = Copied from above\n",
    "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    alpaca_prompt.format(\n",
    "        article, # input\n",
    "        \"\", # output - leave this blank for generation!\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "from transformers import TextStreamer\n",
    "text_streamer = TextStreamer(tokenizer)\n",
    "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zUZmAzV0j4JU"
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   "cell_type": "markdown",
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    "id": "xY9n3yVXj5tn"
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   "source": [
    "# Saving, loading finetuned models\n",
    "To save the final model as LoRA adapters, either use Huggingface's push_to_hub for an online save or save_pretrained for a local save.\n",
    "\n",
    "[NOTE] This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5g4bkhtAj4Sa"
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    "model.save_pretrained(\"lora_model\") # Local saving\n",
    "tokenizer.save_pretrained(\"lora_model\")\n",
    "model.push_to_hub(\"repo\\model_name\", token = \"...\") # Online saving\n",
    "tokenizer.push_to_hub(\"repo\\model_name\", token = \"...\") # Online saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ylJDOFXdj4WH"
   },
   "outputs": [],
   "source": [
    "# Now if you want to load the LoRA adapters we just saved for inference, set False to True:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nR6lGMdlj4Yl"
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    "if False:\n",
    "    from unsloth import FastLanguageModel\n",
    "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "        model_name = \"AhmedBou/Llama-3-EngText-ArabicSummary\", # YOUR MODEL YOU USED FOR TRAINING\n",
    "        max_seq_length = max_seq_length,\n",
    "        dtype = dtype,\n",
    "        load_in_4bit = load_in_4bit,\n",
    "    )\n",
    "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
    "\n",
    "inputs = tokenizer(\n",
    "[\n",
    "    alpaca_prompt.format(\n",
    "        \"\", # input news article\n",
    "        \"\", # output - leave this blank for generation!\n",
    "    )\n",
    "], return_tensors = \"pt\").to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
    "tokenizer.batch_decode(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "99l-Lq55kMCO"
   },
   "source": [
    "You can also use Hugging Face's AutoModelForPeftCausalLM. Only use this if you do not have unsloth installed. It can be hopelessly slow, since 4bit model downloading is not supported, and Unsloth's inference is 2x faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "if False:\n",
    "    # I highly do NOT suggest - use Unsloth if possible\n",
    "    from peft import AutoPeftModelForCausalLM\n",
    "    from transformers import AutoTokenizer\n",
    "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
    "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
    "        load_in_4bit = load_in_4bit,\n",
    "    )\n",
    "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iUkBT7cHkJT9"
   },
   "outputs": [],
   "source": [
    "# And we're done!"
   ]
  },
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   "execution_count": null,
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    "id": "gCQ9_wc5kJWw"
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