{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "890a4b2a-0657-4c5c-8e01-a43dc1ef9c83",
      "metadata": {
        "id": "890a4b2a-0657-4c5c-8e01-a43dc1ef9c83",
        "outputId": "28e04e99-9bef-4fdc-c68c-662555fdb576"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting huggingface_hub\n",
            "  Downloading huggingface_hub-0.27.1-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.9.0)\n",
            "Collecting fsspec>=2023.5.0 (from huggingface_hub)\n",
            "  Downloading fsspec-2024.12.0-py3-none-any.whl.metadata (11 kB)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Collecting tqdm>=4.42.1 (from huggingface_hub)\n",
            "  Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.7/57.7 kB\u001b[0m \u001b[31m989.8 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.4.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.1.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (1.26.13)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
            "Downloading huggingface_hub-0.27.1-py3-none-any.whl (450 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m450.7/450.7 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
            "\u001b[?25hDownloading fsspec-2024.12.0-py3-none-any.whl (183 kB)\n",
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            "\u001b[?25hDownloading tqdm-4.67.1-py3-none-any.whl (78 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tqdm, fsspec, huggingface_hub\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2023.4.0\n",
            "    Uninstalling fsspec-2023.4.0:\n",
            "      Successfully uninstalled fsspec-2023.4.0\n",
            "Successfully installed fsspec-2024.12.0 huggingface_hub-0.27.1 tqdm-4.67.1\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "y1jMmutu5wf2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "dad45200574140679f77e614057581a2",
            "7944e5586186430f951e1802254d865d",
            "30c87d646e28481a846b1ea040caa54c",
            "31841621f67f4825ba987985fb24652c",
            "c774990d583b4b9c85e907467d61bd19",
            "9718ec54e68e4e92b77508a4678e216a",
            "5a6acd0687f046b9837f4a2c8c7caba4",
            "1dddad2ebd0a40fa82d1be41c5b43b35",
            "3b3a0ea44cd545b8ab9f23059ad3a4a8",
            "da7de6d193af4834a97fec1081934358",
            "eb21aee5f018426fa760038cf033e3b3",
            "bf1d288c3e3e4440956d46cf75010a47",
            "65b4826a7fe6428e82e4ffe9ebd85cc9",
            "7ec1e629c682494a8da2b65659069b26",
            "1059dbeec35c40f6a46b614d7bbdb8a6",
            "f43fb8b3fd664068b11927ed5dc07dc9",
            "08b4a516ddce453cb810945912d1b4ef",
            "a32d4044929141f481e5dc204fcdd704",
            "9daf3ea53618493e95396bdbf9e67c06",
            "27fb032a74644c328ab0528d092321af",
            "b633f266a001445390a18cd0253f5f09"
          ]
        },
        "id": "y1jMmutu5wf2",
        "outputId": "b456c8d8-79a0-410e-eae5-9ded7ff7dd58"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b633f266a001445390a18cd0253f5f09",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d9c48fcc-0633-4fd5-9587-0cfda89f419f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d9c48fcc-0633-4fd5-9587-0cfda89f419f",
        "outputId": "c086172c-fbaf-4806-a3dc-c558dc349e2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting unsloth\n",
            "  Downloading unsloth-2025.1.5-py3-none-any.whl.metadata (60 kB)\n",
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            "\u001b[?25hCollecting unsloth_zoo>=2025.1.2 (from unsloth)\n",
            "  Downloading unsloth_zoo-2025.1.3-py3-none-any.whl.metadata (16 kB)\n",
            "Collecting torch>=2.4.0 (from unsloth)\n",
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            "Collecting xformers>=0.0.27.post2 (from unsloth)\n",
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            "\u001b[?25hCollecting datasets>=2.16.0 (from unsloth)\n",
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            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
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            "\u001b[?25hCollecting requests>=2.32.2 (from datasets>=2.16.0->unsloth)\n",
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            "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets>=2.16.0->unsloth)\n",
            "  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub->unsloth) (4.4.0)\n",
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            "  Downloading typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB)\n",
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            "\u001b[?25hCollecting tokenizers<0.22,>=0.21 (from transformers!=4.47.0,>=4.46.1->unsloth)\n",
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            "\u001b[?25hDownloading mdurl-0.1.2-py3-none-any.whl (10.0 kB)\n",
            "Installing collected packages: sentencepiece, pytz, xxhash, wheel, tzdata, typing-extensions, triton, sympy, shtab, safetensors, requests, regex, pyarrow, protobuf, propcache, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, mdurl, hf_transfer, fsspec, frozenlist, docstring-parser, dill, async-timeout, aiohappyeyeballs, typeguard, pandas, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, multidict, markdown-it-py, aiosignal, yarl, tokenizers, rich, nvidia-cusolver-cu12, tyro, transformers, torch, aiohttp, xformers, cut_cross_entropy, bitsandbytes, accelerate, peft, datasets, trl, unsloth_zoo, unsloth\n",
            "  Attempting uninstall: wheel\n",
            "    Found existing installation: wheel 0.41.3\n",
            "    Uninstalling wheel-0.41.3:\n",
            "      Successfully uninstalled wheel-0.41.3\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.4.0\n",
            "    Uninstalling typing_extensions-4.4.0:\n",
            "      Successfully uninstalled typing_extensions-4.4.0\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.1.0\n",
            "    Uninstalling triton-2.1.0:\n",
            "      Successfully uninstalled triton-2.1.0\n",
            "  Attempting uninstall: sympy\n",
            "    Found existing installation: sympy 1.12\n",
            "    Uninstalling sympy-1.12:\n",
            "      Successfully uninstalled sympy-1.12\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.12.0\n",
            "    Uninstalling fsspec-2024.12.0:\n",
            "      Successfully uninstalled fsspec-2024.12.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.0+cu118\n",
            "    Uninstalling torch-2.1.0+cu118:\n",
            "      Successfully uninstalled torch-2.1.0+cu118\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",
            "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 2.5.1 which is incompatible.\n",
            "torchvision 0.16.0+cu118 requires torch==2.1.0, but you have torch 2.5.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed accelerate-1.3.0 aiohappyeyeballs-2.4.4 aiohttp-3.11.11 aiosignal-1.3.2 async-timeout-5.0.1 bitsandbytes-0.45.0 cut_cross_entropy-25.1.1 datasets-3.2.0 dill-0.3.8 docstring-parser-0.16 frozenlist-1.5.0 fsspec-2024.9.0 hf_transfer-0.1.9 markdown-it-py-3.0.0 mdurl-0.1.2 multidict-6.1.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.4.127 pandas-2.2.3 peft-0.14.0 propcache-0.2.1 protobuf-3.20.3 pyarrow-19.0.0 pytz-2024.2 regex-2024.11.6 requests-2.32.3 rich-13.9.4 safetensors-0.5.2 sentencepiece-0.2.0 shtab-1.7.1 sympy-1.13.1 tokenizers-0.21.0 torch-2.5.1 transformers-4.48.0 triton-3.1.0 trl-0.13.0 typeguard-4.4.1 typing-extensions-4.12.2 tyro-0.9.10 tzdata-2024.2 unsloth-2025.1.5 unsloth_zoo-2025.1.3 wheel-0.45.1 xformers-0.0.29.post1 xxhash-3.5.0 yarl-1.18.3\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25hUsing cached ninja-1.11.1.3-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (422 kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: flash-attn\n",
            "  Building wheel for flash-attn (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Created wheel for flash-attn: filename=flash_attn-2.7.3-cp310-cp310-linux_x86_64.whl size=191342964 sha256=cd0ead6ad92bff21f90cf0aecdf859bc7145bf4e7dc8ed1fdc3cc38299651ce4\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/d7/10/a74c9fe5ffe6ff306b27a220b2bf2f37d907b68fdcd138cdda\n",
            "Successfully built flash-attn\n",
            "Installing collected packages: ninja, flash-attn, einops\n",
            "Successfully installed einops-0.8.0 flash-attn-2.7.3 ninja-1.11.1.3\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "! pip install unsloth --upgrade --no-cache-dir\n",
        "import torch\n",
        "if torch.cuda.get_device_capability()[0] >= 8:\n",
        "    !pip install --no-deps packaging ninja einops \"flash-attn>=2.6.3\""
      ]
    },
    {
      "cell_type": "code",
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      "id": "6c7ca839-be08-4b49-ba49-d242cfdaf618",
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        "id": "6c7ca839-be08-4b49-ba49-d242cfdaf618",
        "outputId": "0f0e3793-0049-4ff1-abf4-b877f92284e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "Unsloth: Your Flash Attention 2 installation seems to be broken?\n",
            "A possible explanation is you have a new CUDA version which isn't\n",
            "yet compatible with FA2? Please file a ticket to Unsloth or FA2.\n",
            "We shall now use Xformers instead, which does not have any performance hits!\n",
            "We found this negligible impact by benchmarking on 1x A100.\n",
            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
            "==((====))==  Unsloth 2025.1.5: Fast Llama patching. Transformers: 4.48.0.\n",
            "   \\\\   /|    GPU: NVIDIA A100 80GB PCIe. Max memory: 79.138 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.5.1+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. Triton: 3.1.0\n",
            "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. 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": "98fd34d2d3e0489782c49865876055de",
              "version_major": 2,
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/3.42G [00:00<?, ?B/s]"
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          },
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        },
        {
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            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/158 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "00b69390dac44d25be2e284939584625",
              "version_major": 2,
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            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/4.14k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b19fcd0e689646cc967dc97f3de05391",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/801k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c51ec2553fc74293a440f047a5fc0e90",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e418de8703a4d37adc9077e60144618",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e89feb5ff0a468a99ef24c9ce41de5d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/423 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3079e288b7134ecf945a260ac221b934",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/3.52M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048\n",
        "dtype = None\n",
        "load_in_4bit = False\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(model_name = \"unsloth/SmolLM2-1.7B-Instruct\", max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6296c071-fa11-407d-a6a8-95a160cfb2f5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6296c071-fa11-407d-a6a8-95a160cfb2f5",
        "outputId": "7abd16b8-692c-46c1-bb54-fcea392621ad"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth 2025.1.5 patched 24 layers with 24 QKV layers, 24 O layers and 24 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(model, r = 16, target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16, lora_dropout = 0, bias = \"none\", use_gradient_checkpointing = \"unsloth\", random_state = 1024, use_rslora = False, loftq_config = None,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70a60388-9e5b-4915-a624-3c58dd71c275",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        "id": "70a60388-9e5b-4915-a624-3c58dd71c275",
        "outputId": "cb150b2a-2f44-4c87-9d86-ad5317c93d9a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e5168b14ebf44c28fb4a2cf41c269a5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "hindi-gemma-mini-subset.csv:   0%|          | 0.00/54.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6ca51a2e59a44f4b642df5eabbdc664",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "import os\n",
        "dataset = load_dataset(\"1-800-LLMs/Data-Collection\", data_files=\"hindi-gemma-mini-subset.csv\", split=\"train\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6MNXXvi86Bo5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "6MNXXvi86Bo5",
        "outputId": "d82fcaa1-11df-4431-8a65-ed1e2286b7fe"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Input</th>\n",
              "      <th>Output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Can form 49B be filled on a typewriter?</td>\n",
              "      <td>​Yes. But typing should be in capital letters ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>क्या भाषा आधारित अल्पसंख्यकों के लिए विशेष अधि...</td>\n",
              "      <td>भाषाई अल्पसंख्यकों के विशेष अधिकारी की भूमिका ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>क्या एक अध्यादेश संघ क्षेत्र की विधान सभा द्वा...</td>\n",
              "      <td>नहीं, एक अध्यादेश संघ क्षेत्र की विधान सभा द्व...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Can a law made under clause (1) establish a hi...</td>\n",
              "      <td>Yes, a law made under clause (1) can establish...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>What is the speed limit for electric scooters ...</td>\n",
              "      <td>The speed limit for electric scooters in India...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32716</th>\n",
              "      <td>What is the Constitutional mandate of the Unio...</td>\n",
              "      <td>Union Public Service Commission is a Constitut...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32717</th>\n",
              "      <td>मैंने क्रेडिट/डेबिट कार्ड के जरिए कई बार भुगता...</td>\n",
              "      <td>हाँ, आवेदन नंबर के खिलाफ किए गए अतिरिक्त भुगता...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32718</th>\n",
              "      <td>मैं अपनी ऑनलाइन भर्ती आवेदन (ORA) को पूरा करने...</td>\n",
              "      <td>कृपया इस प्रकार आगे बढ़ें:- चरण 1 - सुनिश्चित ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32719</th>\n",
              "      <td>क्या एक अधिकारी जिसकी ईमानदारी प्रमाणपत्र राज्...</td>\n",
              "      <td>नहीं।</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32720</th>\n",
              "      <td>अगर फोटो इच्छित प्रारूप में नहीं है, तो मैं इस...</td>\n",
              "      <td>यदि छवि किसी अन्य प्रारूप में है जैसे .tiff, ....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32721 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   Input  \\\n",
              "0                Can form 49B be filled on a typewriter?   \n",
              "1      क्या भाषा आधारित अल्पसंख्यकों के लिए विशेष अधि...   \n",
              "2      क्या एक अध्यादेश संघ क्षेत्र की विधान सभा द्वा...   \n",
              "3      Can a law made under clause (1) establish a hi...   \n",
              "4      What is the speed limit for electric scooters ...   \n",
              "...                                                  ...   \n",
              "32716  What is the Constitutional mandate of the Unio...   \n",
              "32717  मैंने क्रेडिट/डेबिट कार्ड के जरिए कई बार भुगता...   \n",
              "32718  मैं अपनी ऑनलाइन भर्ती आवेदन (ORA) को पूरा करने...   \n",
              "32719  क्या एक अधिकारी जिसकी ईमानदारी प्रमाणपत्र राज्...   \n",
              "32720  अगर फोटो इच्छित प्रारूप में नहीं है, तो मैं इस...   \n",
              "\n",
              "                                                  Output  \n",
              "0      ​Yes. But typing should be in capital letters ...  \n",
              "1      भाषाई अल्पसंख्यकों के विशेष अधिकारी की भूमिका ...  \n",
              "2      नहीं, एक अध्यादेश संघ क्षेत्र की विधान सभा द्व...  \n",
              "3      Yes, a law made under clause (1) can establish...  \n",
              "4      The speed limit for electric scooters in India...  \n",
              "...                                                  ...  \n",
              "32716  Union Public Service Commission is a Constitut...  \n",
              "32717  हाँ, आवेदन नंबर के खिलाफ किए गए अतिरिक्त भुगता...  \n",
              "32718  कृपया इस प्रकार आगे बढ़ें:- चरण 1 - सुनिश्चित ...  \n",
              "32719                                              नहीं।  \n",
              "32720  यदि छवि किसी अन्य प्रारूप में है जैसे .tiff, ....  \n",
              "\n",
              "[32721 rows x 2 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = dataset.to_pandas()\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c66206ed-8628-45bf-9c8d-f7d73548e9f0",
      "metadata": {
        "id": "c66206ed-8628-45bf-9c8d-f7d73548e9f0"
      },
      "outputs": [],
      "source": [
        "alpaca_prompt = \"\"\"### INPUT: {} ### RESPONSE: {}\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc3addd1-d898-4334-8cce-df995b964f99",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5e7a468fd6dc4bc09c691d7666c30421",
            "1b6489b45379462ca305c561115209e5",
            "ab2c1f46b5774344b741f13606e074d6",
            "e82cfc71d6df4ab687041139b069dd16",
            "ee67e5c4251d46e3bd0c0422db79a266",
            "2829d1de2ceb4e90a1e7947c9f22f137",
            "8489e83a87354fd7b189b832d8f4bd3c",
            "e90dbb48833a45b7b8c219f016e5e6ac",
            "77e57f57a7874107a8863164cf42c3df",
            "475850d3a3cc4c399f2dacc383643e6c",
            "6ae2cd6684dc46c9980e85f1c5d5fd60",
            "15290529eed746f0bd227ed622191ba0"
          ]
        },
        "id": "fc3addd1-d898-4334-8cce-df995b964f99",
        "outputId": "f705140c-35f3-4af0-eb22-a3b0782e01b9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "15290529eed746f0bd227ed622191ba0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/32721 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "EOS_TOKEN = tokenizer.eos_token\n",
        "def formatting_prompts_func(examples):\n",
        "    inputs = examples[\"Input\"]\n",
        "    outputs = examples[\"Output\"]\n",
        "    texts = []\n",
        "    for input_text, output_text in zip(inputs, outputs):\n",
        "        text = alpaca_prompt.format(input_text, output_text) + EOS_TOKEN\n",
        "        texts.append(text)\n",
        "    return {\"text\": texts}\n",
        "dataset = dataset.map(formatting_prompts_func, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "98c993c3-9845-4275-8438-8ceef5d0e6ee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4b770d73809c41779b6c5f7be290a6b1",
            "305c512c9df3453c961c84fffb534648",
            "7277d74f7222464abd7be1b6716cbeb6",
            "a4f791ee89b84766a5f5c7c4b1ccf777",
            "ebcc824743634be1884f0dd9a2ec9e41",
            "5beded1acaae4500bfda16233b442ec4",
            "e182c00dacfe4c4185a45964a7b9931d",
            "2844a002c1a64903892285dfc64ec25d",
            "8ad54af3e1f642f99c6415b6fc162bc8",
            "a731913d2f8542a1afe391e8012884f9",
            "422451ba560544f1af96e883d37a31c2",
            "165e7b40e66d45798753f4c108b63d61"
          ]
        },
        "id": "98c993c3-9845-4275-8438-8ceef5d0e6ee",
        "outputId": "1d418439-d9a7-41d5-f6e9-abc867f91b97"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "165e7b40e66d45798753f4c108b63d61",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/32721 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def count_tokens(example):\n",
        "    return {\"input_token_count\": len(tokenizer.tokenize(str(example.get(\"Input\", \"\"))))}\n",
        "dataset = dataset.map(count_tokens)\n",
        "dataset = dataset.sort(\"input_token_count\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ceFDXKVU7U3x",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "94b56f00232a41bcb4af46125f98539a",
            "b2ec97cc62614a708cdf18ab0750a87d",
            "9134968e985c49d0b98eb25ded4b65cb",
            "55a1bb9551ac4b63bd9edb7384f63b17",
            "00e6c6c8d1fb447c89cd290d96446c4d",
            "d707c391b7d74644ae098ad5cc095451",
            "69dd474441d94e5784fe69d9ed06a984",
            "95d6d4e09f844f338296ebf9d37b3dc5",
            "8f7eb280e2ce4695a942129808650b8a",
            "f9d270cc61694eee9a054c6eeb7df930",
            "4b176c4c041c4085950029e1e27ca94a",
            "9995de66ebb5486ea7e800923bd1a0f3"
          ]
        },
        "id": "ceFDXKVU7U3x",
        "outputId": "19c1fa9a-003b-402b-d797-c120b7ece6ff"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9995de66ebb5486ea7e800923bd1a0f3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Flattening the indices:   0%|          | 0/32721 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import concatenate_datasets\n",
        "last_200 = dataset.select(range(len(dataset) - 200, len(dataset)))\n",
        "remaining = dataset.select(range(len(dataset) - 200))\n",
        "dataset = concatenate_datasets([last_200, remaining])\n",
        "dataset = dataset.flatten_indices()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f98893b-dace-422f-93da-04a7f8eb71f2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "12fecd83cf8048fe85c7a44de970ccb0",
            "85e396bc539e47ebb88b59a60128b38a"
          ]
        },
        "id": "3f98893b-dace-422f-93da-04a7f8eb71f2",
        "outputId": "a25c139a-b591-4c2b-fb03-4c893844a24b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "85e396bc539e47ebb88b59a60128b38a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/32721 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\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,\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 8,\n",
        "        gradient_accumulation_steps = 12,\n",
        "        num_train_epochs = 1,\n",
        "        learning_rate = 2e-5,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        logging_steps = 100,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 1024,\n",
        "        output_dir = \"outputs\",\n",
        "        report_to = \"none\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e6f6b98-01d5-4088-a284-f37fb660e761",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "9e6f6b98-01d5-4088-a284-f37fb660e761",
        "outputId": "4792ae90-eba6-436d-c13c-6ec81952890f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 32,721 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 8 | Gradient Accumulation steps = 12\n",
            "\\        /    Total batch size = 96 | Total steps = 340\n",
            " \"-____-\"     Number of trainable parameters = 18,087,936\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='340' max='340' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [340/340 1:08:46, 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>1.150100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.058200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.036000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ZJNyEwMBOel",
      "metadata": {
        "colab": {
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            "8af45b5896dd43318c4e703a789c61f7"
          ]
        },
        "id": "4ZJNyEwMBOel",
        "outputId": "2f3990b2-aa81-4fb5-8ec7-475f3395a517"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Saving LoRA adapters. Please wait...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "81a1c706e4a44b35a072861d77e87333",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/584 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8af45b5896dd43318c4e703a789c61f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/72.4M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved lora model to https://huggingface.co/DrishtiSharma/smollm2-1.7b-instruct-hindi-subset\n"
          ]
        }
      ],
      "source": [
        "if True: model.push_to_hub_merged(\"DrishtiSharma/smollm2-1.7b-instruct-hindi-subset\", tokenizer, save_method = \"lora\", token = )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "OnyiEx-wyJFR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466,
          "referenced_widgets": [
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          "text": [
            "Unsloth: You are pushing to hub, but you passed your HF username = DrishtiSharma.\n",
            "We shall truncate DrishtiSharma/smollm2-1.7b-instruct-hindi-subset-fp16 to smollm2-1.7b-instruct-hindi-subset-fp16\n"
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            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 682.79 out of 1007.69 RAM for saving.\n",
            "Unsloth: Saving model... This might take 5 minutes ...\n"
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          "text": [
            "100%|██████████| 24/24 [00:00<00:00, 130.11it/s]\n"
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          "text": [
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          "text": [
            "Done.\n",
            "Saved merged model to https://huggingface.co/DrishtiSharma/smollm2-1.7b-instruct-hindi-subset-fp16\n"
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        "if True: model.push_to_hub_merged(\"DrishtiSharma/smollm2-1.7b-instruct-hindi-subset-fp16\", tokenizer, save_method = \"merged_16bit\", token = \"\")"
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