Llama3_2_(1B_and_3B)_Conversational.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/callor/Callor-Artificial-Intelligence-2025/blob/master/Llama3_2_(1B_and_3B)_Conversational.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1zhvQ1q4GLq"
      },
      "source": [
        "Google Colab instance! 에서 실행한다 \"*Runtime*\" 이나 \"*Run all*\" 을 눌러 실행한다.\n",
        "\n",
        "<div class=\"align-center\">\n",
        "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
        "<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a>\n",
        "\n",
        "Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
        "</div>\n",
        "\n",
        "컴퓨터에 Unsloth를 설치하려면 [여기](https://docs.unsloth.ai/get-started/installing-+-updating) Github 페이지의 설치 지침을 따르세요.\n",
        "\n",
        "[데이터 준비](#Data), [훈련](#Train), [모델 실행](#Inference) 및 [저장](#Save) 방법을 배우게 됩니다.\n",
        "\n",
        "\n",
        "전체문서 [model uploads](https://docs.unsloth.ai/get-started/all-our-models) 와 [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n",
        "                    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9l6Wx3sS4GLt"
      },
      "source": [
        "### News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPFd1PA24GLt"
      },
      "source": [
        "**[신규]** Phi-4의 정확도를 크게 높여주는 Phi-4의 많은 버그를 수정했습니다. [blogpost](https://unsloth.ai/blog/phi4)\n",
        "\n",
        "**[신규]** 버그 수정을 포함한 모든 Phi-4 모델 업로드를 볼 수 있습니다.[dynamic 4-bit quants](https://unsloth.ai/blog/dynamic-4bit),  \n",
        "GGUF 와 기타 [here](https://huggingface.co/collections/unsloth/phi-4-all-versions-677eecf93784e61afe762afa)\n",
        "\n",
        "[신규] 2024년 11월부터 Vision finetuning 을 Unsloth가 지원합니다.[vision finetuning](https://unsloth.ai/blog/vision)!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjPGy2jT4GLt"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "tKCCIood4GLu"
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      "outputs": [],
      "source": [
        "%%capture\n",
        "# Normally using pip install unsloth is enough\n",
        "# 일반적으로 pip install unsloth를 사용하면 충분합니다\n",
        "\n",
        "# Temporarily as of Jan 31st 2025, Colab has some issues with Pytorch\n",
        "# 일시적으로 2025년 1월 31일 현재 Colab은 Pytorch에 문제가 있습니다\n",
        "\n",
        "# Using pip install unsloth will take 3 minutes, whilst the below takes <1 minute:\n",
        "# pip install unsloth를 사용하면 3분이 걸리지만 아래는 1분 미만이 걸립니다.\n",
        "\n",
        "!pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n",
        "!pip install --no-deps cut_cross_entropy unsloth_zoo\n",
        "!pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n",
        "!pip install --no-deps unsloth"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJ7Ros0Q4GLv"
      },
      "source": [
        "### Unsloth"
      ]
    },
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          "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 2025.1.8: Fast Llama patching. Transformers: 4.47.1.\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.741 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.5.1+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.1.0\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29. 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"
          ]
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              "model_id": "fef5d6bd8d22478392d871597fa2dab0"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048 # 선택해 주세요! RoPE 스케일링을 내부적으로 자동 지원합니다!\n",
        "dtype = None # 자동 감지에는 없음. Tesla T4, V100의 경우 Float16, Ampere+의 경우 Bfloat16\n",
        "load_in_4bit = True # 메모리 사용량을 줄이기 위해 4비트 양자화를 사용합니다. False가 될 수 있습니다.\n",
        "\n",
        "# 4비트 사전 양자화 모델을 지원하여 다운로드 속도를 4배 높이고 OOM은 없습니다.\n",
        "fourbit_models = [\n",
        "    \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\",      # Llama-3.1 2x faster\n",
        "    \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n",
        "    \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n",
        "    \"unsloth/Meta-Llama-3.1-405B-bnb-4bit\",    # 4bit for 405b!\n",
        "    \"unsloth/Mistral-Small-Instruct-2409\",     # Mistral 22b 2x faster!\n",
        "    \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
        "    \"unsloth/Phi-3.5-mini-instruct\",           # Phi-3.5 2x faster!\n",
        "    \"unsloth/Phi-3-medium-4k-instruct\",\n",
        "    \"unsloth/gemma-2-9b-bnb-4bit\",\n",
        "    \"unsloth/gemma-2-27b-bnb-4bit\",            # Gemma 2x faster!\n",
        "\n",
        "    \"unsloth/Llama-3.2-1B-bnb-4bit\",           # NEW! Llama 3.2 models\n",
        "    \"unsloth/Llama-3.2-1B-Instruct-bnb-4bit\",\n",
        "    \"unsloth/Llama-3.2-3B-bnb-4bit\",\n",
        "    \"unsloth/Llama-3.2-3B-Instruct-bnb-4bit\",\n",
        "\n",
        "    \"unsloth/Llama-3.3-70B-Instruct-bnb-4bit\" # NEW! Llama 3.3 70B!\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/Llama-3.2-3B-Instruct\", # or choose \"unsloth/Llama-3.2-1B-Instruct\"\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
      },
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!  \n",
        "이제 LoRA 어댑터를 추가하여 모든 매개변수의 1~10%만 업데이트하면 됩니다!"
      ]
    },
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        },
        "id": "6bZsfBuZDeCL",
        "outputId": "7f8a896e-6e3b-4281-d4f9-6fafe7ed8218"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2025.1.8 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\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, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vITh0KVJ10qX"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "We now use the `Llama-3.1` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. But we convert it to HuggingFace's normal multiturn format `(\"role\", \"content\")` instead of `(\"from\", \"value\")`/ Llama-3 renders multi turn conversations like below:\n",
        "\n",
        "이제 대화 스타일 미세 조정을 위해 `Llama-3.1` 형식을 사용합니다. ShareGPT 스타일의 [Maxime Labonne의 FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) 데이터 세트를 사용합니다. 하지만 HuggingFace의 일반 멀티턴 형식인 `(\"role\", \"content\")`로 변환합니다. `(\"from\", \"value\")`/ Llama-3는 아래와 같이 멀티턴 대화를 렌더링합니다.\n",
        "\n",
        "\n",
        "```\n",
        "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
        "\n",
        "Hey there! How are you?<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "I'm great thanks!<|eot_id|>\n",
        "```\n",
        "\n",
        "We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3` and more.\n",
        "\n",
        "우리는 `get_chat_template` 함수를 사용하여 올바른 채팅 템플릿을 가져옵니다. 우리는 `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3` 등을 지원합니다."
      ]
    },
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/100000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "225532228cad467c963b06e00133bde1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth.chat_templates import get_chat_template\n",
        "\n",
        "tokenizer = get_chat_template(\n",
        "    tokenizer,\n",
        "    chat_template = \"llama-3.1\",\n",
        ")\n",
        "\n",
        "def formatting_prompts_func(examples):\n",
        "    convos = examples[\"conversations\"]\n",
        "    texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n",
        "    return { \"text\" : texts, }\n",
        "pass\n",
        "\n",
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"mlabonne/FineTome-100k\", split = \"train\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9CBpiISFa6C"
      },
      "source": [
        "We now use `standardize_sharegpt` to convert ShareGPT style datasets into HuggingFace's generic format. This changes the dataset from looking like:\n",
        "\n",
        "이제 `standardize_sharegpt`를 사용하여 ShareGPT 스타일 데이터 세트를 HuggingFace의 일반 형식으로 변환합니다. 이렇게 하면 데이터 세트가 다음과 같이 변경됩니다.\n",
        "\n",
        "```\n",
        "{\"from\": \"system\", \"value\": \"You are an assistant\"}\n",
        "{\"from\": \"human\", \"value\": \"What is 2+2?\"}\n",
        "{\"from\": \"gpt\", \"value\": \"It's 4.\"}\n",
        "```\n",
        "to\n",
        "```\n",
        "{\"role\": \"system\", \"content\": \"You are an assistant\"}\n",
        "{\"role\": \"user\", \"content\": \"What is 2+2?\"}\n",
        "{\"role\": \"assistant\", \"content\": \"It's 4.\"}\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "8433d40447564ae194562d6b355cc6c1",
            "fca1de0091cb47df997b9f8aede73fdd",
            "7d564986021a432d92eb64d7f0a5e4a0",
            "7e074524c7344805a52414071792bed8",
            "7d0da880e64f44a584e92624ea1a7de8",
            "9c6d727def56442eae1aea1b2abb5acd",
            "47f54294daf54997a35a09c5a5a2c866",
            "e96605aa38844552b3156eb756016b41",
            "dfbf32178fb04711ac247877b284c4e7",
            "f38ce4d464e3452d94ca6949dc2994b6",
            "2707ee814aff483291cc4035192101a0",
            "a1809dfd1774404e812a9a1b0dd05392",
            "2ad18eba70d74289a04db598dae0ddf1",
            "f21a4a23a0214c60ba3079c371c2becf",
            "d9c0d8cb7dae456898519a33e1296b67",
            "76e6b4d0d6bb431a9d457b3ceea9224b",
            "2b88bc9b1ae64fb0b70386f526c7dcc1",
            "cfc2f7b277174f51a026e243b71a12a5",
            "5208338e2fd641a098415a0d10831c25",
            "4ca0dd79991e48fbbc182bd3795b51dd",
            "a5a0ceda7ea04ece952312dc8584f528",
            "980f376c848947209a1144f7b412118c"
          ]
        },
        "id": "oPXzJZzHEgXe",
        "outputId": "405c25c4-a9fc-4af3-de1a-df777fcf1169"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Standardizing format:   0%|          | 0/100000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8433d40447564ae194562d6b355cc6c1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a1809dfd1774404e812a9a1b0dd05392"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth.chat_templates import standardize_sharegpt\n",
        "dataset = standardize_sharegpt(dataset)\n",
        "dataset = dataset.map(formatting_prompts_func, batched = True,)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndDUB23CGAC5"
      },
      "source": [
        "We look at how the conversations are structured for item 5:\n",
        "\n",
        "5번 항목에 대한 대화가 어떻게 구성되어 있는지 살펴보겠습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gGFzmplrEy9I",
        "outputId": "fa806056-9331-4827-cb5c-cb8704d5ed01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'content': 'How do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?',\n",
              "  'role': 'user'},\n",
              " {'content': 'Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.',\n",
              "  'role': 'assistant'}]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "dataset[5][\"conversations\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GfzTdMtvGE6w"
      },
      "source": [
        "And we see how the chat template transformed these conversations.  \n",
        "그리고 채팅 템플릿이 어떻게 이런 대화를 변형했는지 살펴보겠습니다.\n",
        "\n",
        "**[Notice]** Llama 3.1 Instruct's default chat template default adds `\"Cutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\"`, so do not be alarmed!  \n",
        "**[공지]** Llama 3.1 Instruct의 기본 채팅 템플릿 기본값은 `\"Cutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\"`를 추가하므로 놀라지 마세요.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "vhXv0xFMGNKE",
        "outputId": "545c4a5d-a1bb-4d3d-e68c-8e06dc4da466"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nHow do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nAstronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|eot_id|>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "dataset[5][\"text\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAEIeSQ3xdS"
      },
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n",
        "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!\n",
        "\n",
        "### 모델 학습\n",
        "이제 Huggingface TRL의 `SFTTrainer`를 사용해 보겠습니다! 자세한 문서는 여기에서 확인하세요: [TRL SFT 문서](https://huggingface.co/docs/trl/sft_trainer). 속도를 높이기 위해 60단계를 거치지만, 전체 실행을 위해 `num_train_epochs=1`을 설정하고 `max_steps=None`을 끌 수 있습니다. 또한 TRL의 `DPOTrainer`도 지원합니다!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "33929a971047495b96d381bb95c6c603",
            "3e874255520043eeaf10134cf015a89d",
            "44496489f242419fb1e5dac2537161da",
            "0728ab589f83492391133f848dccf6eb",
            "987bd9a42e654005a4b99bb10325e92b",
            "43f4f31c7ceb47ccb86e80747afe595c",
            "62ebd2faf7474070ac56ea0c6b5a8867",
            "3d688a37eb354b83a82811281444c6b6",
            "9ff8d6098a0c42b282ff19140a68e3a9",
            "83df890f90eb4313ab774ae959c86c6a",
            "9d250d8f76d348dfb740e2537647b397"
          ]
        },
        "id": "95_Nn-89DhsL",
        "outputId": "222a9197-49e1-48f5-a1c2-2a31f11317bf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/100000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33929a971047495b96d381bb95c6c603"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments, DataCollatorForSeq2Seq\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",
        "    data_collator = DataCollatorForSeq2Seq(tokenizer = tokenizer),\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",
        "        # num_train_epochs = 1, # Set this for 1 full training run.\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",
        "        report_to = \"none\", # Use this for WandB etc\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_sGp5XlG6dq"
      },
      "source": [
        "We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs.\n",
        "\n",
        "또한 Unsloth의 `train_on_completions` 메서드를 사용하여 보조 출력에 대해서만 학습하고 사용자 입력에 대한 손실은 무시합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "896e263b63da46fc9f10c7cc15243123",
            "2014def3e3744fb0b0d6a985cc246933",
            "3499e501cff241e48792b25c16ecfb0e",
            "26080ffeb055470bb170d65ed1c36b7c",
            "5e44045a791648639e79bcb14af0bf3a",
            "81c51b806633400dad14ede5a577fee1",
            "d88aeb0ad628492bbfdfc477f30c9fdb",
            "58ccee147a494e398467de73d5e666e3",
            "944ab18d0433416b8a855d9a51dad034",
            "058ed9e66bb440beb70ec56b80e4d25f",
            "b9413933e3b54d02b8c941810cbe1e2b"
          ]
        },
        "id": "juQiExuBG5Bt",
        "outputId": "ab362ef7-3f83-4389-8156-4c85abc8c621"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "896e263b63da46fc9f10c7cc15243123"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth.chat_templates import train_on_responses_only\n",
        "trainer = train_on_responses_only(\n",
        "    trainer,\n",
        "    instruction_part = \"<|start_header_id|>user<|end_header_id|>\\n\\n\",\n",
        "    response_part = \"<|start_header_id|>assistant<|end_header_id|>\\n\\n\",\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dv1NBUozV78l"
      },
      "source": [
        "We verify masking is actually done:\n",
        "\n",
        "마스킹이 실제로 수행되었는지 확인합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "LtsMVtlkUhja",
        "outputId": "4ecc257c-63fc-4a81-f378-93a6eb085927"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nHow do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nAstronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|eot_id|>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "tokenizer.decode(trainer.train_dataset[5][\"input_ids\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "_rD6fl8EUxnG",
        "outputId": "270827c9-a929-4366-8b64-c42762573ec7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'                                                                \\n\\nAstronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|eot_id|>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "space = tokenizer(\" \", add_special_tokens = False).input_ids[0]\n",
        "tokenizer.decode([space if x == -100 else x for x in trainer.train_dataset[5][\"labels\"]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3enWUM0jV-jV"
      },
      "source": [
        "We can see the System and Instruction prompts are successfully masked!\n",
        "\n",
        "시스템 및 지침 프롬프트가 성공적으로 마스크된 것을 볼 수 있습니다!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ejIt2xSNKKp",
        "outputId": "77ef6288-1fc0-489d-b382-77cd66500c66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = Tesla T4. Max memory = 14.741 GB.\n",
            "2.66 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.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "80c132ff-8c91-4039-9b35-f554df2cc027"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 100,000 | 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 = 24,313,856\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 07:46, 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>1</td>\n",
              "      <td>0.776500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.828400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.068000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.886500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.752500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.911400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.613800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.993000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.852300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.749400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.880800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.091900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.936800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.637200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.867900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.633500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.995000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.825400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.763900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.922000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.897100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.847700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>1.028900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.870000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.634000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.825100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.825600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.783800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.080700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.028000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.708700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.539300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.653200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.573500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.758400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.996400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.896600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.712900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.777600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.995900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.738900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.996800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.772000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.806900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.761100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.859200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.787000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.648100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.016400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.025800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.456300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.904100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.309100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.686900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>1.057000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.115100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.720900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.830300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.751400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.917300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCqnaKmlO1U9",
        "outputId": "2f8c0c85-a30f-4696-a6c4-9738122ffa5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "487.1554 seconds used for training.\n",
            "8.12 minutes used for training.\n",
            "Peak reserved memory = 3.973 GB.\n",
            "Peak reserved memory for training = 1.313 GB.\n",
            "Peak reserved memory % of max memory = 26.952 %.\n",
            "Peak reserved memory for training % of max memory = 8.907 %.\n"
          ]
        }
      ],
      "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(\n",
        "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
        ")\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": "markdown",
      "metadata": {
        "id": "ekOmTR1hSNcr"
      },
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference(추론)\n",
        "Let's run the model! You can change the instruction and input - leave the output blank!  \n",
        "모델을 실행해 보겠습니다! 명령어와 입력을 변경할 수 있습니다. 출력은 비워 두세요!\n",
        "\n",
        "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**  \n",
        "**[신규] Llama-3.1 8b Instruct에 대한 무료 Colab에서 2배 더 빠른 추론을 시도해 보세요 [여기](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**\n",
        "\n",
        "We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why.  \n",
        "`min_p = 0.1`과 `temperature = 1.5`를 사용합니다. 자세한 내용은 이 [트윗](https://x.com/menhguin/status/1826132708508213629)을 읽어보세요."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kR3gIAX-SM2q",
        "outputId": "2074b55f-63ec-42fb-d4b9-50da7e7eb93f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nContinue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\n1, 13, 21, 34, 55, 89.<|eot_id|>']"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "from unsloth.chat_templates import get_chat_template\n",
        "\n",
        "tokenizer = get_chat_template(\n",
        "    tokenizer,\n",
        "    chat_template = \"llama-3.1\",\n",
        ")\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": \"Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,\"},\n",
        "]\n",
        "inputs = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize = True,\n",
        "    add_generation_prompt = True, # Must add for generation\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(input_ids = inputs, max_new_tokens = 64, use_cache = True,\n",
        "                         temperature = 1.5, min_p = 0.1)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrSvZObor0lY"
      },
      "source": [
        " You 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",
        " 또한 지속적인 추론을 위해 `TextStreamer`를 사용할 수 있습니다. 이를 통해 전체 시간을 기다리지 않고도 토큰별로 생성된 토큰을 볼 수 있습니다!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2pEuRb1r2Vg",
        "outputId": "8cd663bd-620f-4a05-feb3-8c8337cfea6e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sure! The Fibonacci sequence continues as follows:\n",
            "\n",
            "9, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269, 2178309, 3524578, 5702887, 9227465, 14930352, \n"
          ]
        }
      ],
      "source": [
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": \"Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,\"},\n",
        "]\n",
        "inputs = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize = True,\n",
        "    add_generation_prompt = True, # Must add for generation\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
        "_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n",
        "                   use_cache = True, temperature = 1.5, min_p = 0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models, 미세 조정된 모델 저장, 로딩\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",
        "최종 모델을 LoRA 어댑터로 저장하려면 Huggingface의 `push_to_hub`를 사용하여 온라인 저장하거나 `save_pretrained`를 사용하여 로컬 저장하세요.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!\n",
        "\n",
        "**[참고]** 이렇게 하면 LoRA 어댑터만 저장되고 전체 모델은 저장되지 않습니다. 16비트 또는 GGUF로 저장하려면 아래로 스크롤하세요!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upcOlWe7A1vc",
        "outputId": "c5ba6358-f3c9-473f-c2c0-66b8a249e2fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('lora_model/tokenizer_config.json',\n",
              " 'lora_model/special_tokens_map.json',\n",
              " 'lora_model/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "model.save_pretrained(\"lora_model\")  # Local saving\n",
        "tokenizer.save_pretrained(\"lora_model\")\n",
        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
      },
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:\n",
        "\n",
        "이제 추론을 위해 방금 저장한 LoRA 어댑터를 로드하려면 `False`를 `True`로 설정합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKX_XKs_BNZR",
        "outputId": "ebfec73c-43ea-4af2-9ef5-f29dd2639ab9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Eiffel Tower, located in the city of Paris, is a tall, iconic tower with a rich history. Constructed for the 1889 World's Fair, it is a significant cultural and historical landmark in the capital of France. It stands approximately 324 meters tall and serves as a symbol of Paris and the French nation. Its four pillars provide support to a square-shaped structure, and it is made primarily of wrought iron. Visitors can access the tower and enjoy stunning views from the top.<|eot_id|>\n"
          ]
        }
      ],
      "source": [
        "if False:\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"lora_model\", # 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",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": \"Describe a tall tower in the capital of France.\"},\n",
        "]\n",
        "inputs = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize = True,\n",
        "    add_generation_prompt = True, # Must add for generation\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
        "_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n",
        "                   use_cache = True, temperature = 1.5, min_p = 0.1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQMjaNrjsU5_"
      },
      "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**.\n",
        "\n",
        "Hugging Face의 `AutoModelForPeftCausalLM`도 사용할 수 있습니다. `unsloth`가 설치되지 않은 경우에만 사용하세요. `4bit` 모델 다운로드가 지원되지 않고 Unsloth의 **추론이 2배 더 빠르기 때문에** 절망적으로 느릴 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "yFfaXG0WsQuE"
      },
      "outputs": [],
      "source": [
        "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": "markdown",
      "metadata": {
        "id": "f422JgM9sdVT"
      },
      "source": [
        "### Saving to float16 for VLLM, VLLM을 위해 float16에 저장\n",
        "\n",
        "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens.\n",
        "\n",
        "`float16`에 직접 저장하는 것도 지원합니다. float16의 경우 `merged_16bit`를 선택하고 int4의 경우 `merged_4bit`를 선택하세요. 또한 `lora` 어댑터를 폴백으로 허용합니다. `push_to_hub_merged`를 사용하여 Hugging Face 계정에 업로드하세요! 개인 토큰은 https://huggingface.co/settings/tokens에서 확인할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "iHjt_SMYsd3P"
      },
      "outputs": [],
      "source": [
        "# Merge to 16bit\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
        "\n",
        "# Merge to 4bit\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
        "\n",
        "# Just LoRA adapters\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCv4vXHd61i7"
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      "source": [
        "### GGUF / llama.cpp Conversion\n",
        "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
        "\n",
        "`GGUF` / `llama.cpp`에 저장하려면 이제 네이티브로 지원합니다! `llama.cpp`를 복제하고 기본적으로 `q8_0`에 저장합니다. `q4_k_m`과 같은 모든 방법을 허용합니다. 로컬 저장에는 `save_pretrained_gguf`를 사용하고 HF에 업로드하려면 `push_to_hub_gguf`를 사용합니다.\n",
        "\n",
        "\n",
        "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
        "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
        "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
        "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
        "\n",
        "지원되는 일부 양적 방법(전체 목록은 [Wiki 페이지](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)에서 확인):\n",
        "* `q8_0` - 빠른 변환. 리소스 사용량이 높지만 일반적으로 허용 가능.\n",
        "* `q4_k_m` - 권장. attention.wv 및 feed_forward.w2 텐서의 절반에는 Q6_K를 사용하고 그 외에는 Q4_K를 사용합니다.\n",
        "* `q5_k_m` - 권장. attention.wv와 feed_forward.w2 텐서의 절반에 Q6_K를 사용하고, 그 외에는 Q5_K를 사용합니다.\n",
        "\n",
        "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n",
        "\n",
        "[**신규**] Ollama로 미세 조정하고 자동 내보내기를 하려면 [Ollama 노트북](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)을 사용해 보세요.\n"
      ]
    },
    {
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      "source": [
        "# Save to 8bit Q8_0\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
        "# And change hf to your username!\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
        "\n",
        "# Save to 16bit GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
        "\n",
        "# Save to q4_k_m GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n",
        "\n",
        "# Save to multiple GGUF options - much faster if you want multiple!\n",
        "if False:\n",
        "    model.push_to_hub_gguf(\n",
        "        \"hf/model\", # Change hf to your username!\n",
        "        tokenizer,\n",
        "        quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
        "        token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
        "    )"
      ]
    },
    {
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      "metadata": {
        "id": "4Mg7jSUA4GL5"
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      "source": [
        "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n",
        "\n",
        "이제 llama.cpp의 `model-unsloth.gguf` 파일이나 `model-unsloth-Q4_K_M.gguf` 파일을 사용하거나 Jan이나 Open WebUI와 같은 UI 기반 시스템을 사용하세요. Jan은 [여기](https://github.com/janhq/jan)에 설치하고 Open WebUI는 [여기](https://github.com/open-webui/open-webui)에 설치할 수 있습니다.\n",
        "\n",
        "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
        "\n",
        "끝났습니다! Unsloth에 대한 질문이 있으면 [Discord](https://discord.gg/unsloth) 채널이 있습니다! 버그를 발견하거나 최신 LLM 정보를 계속 업데이트하고 싶거나 도움이 필요하거나 프로젝트에 참여하고 싶다면 Discord에 가입하세요!\n",
        "\n",
        "\n",
        "Some other links(다른 링크):\n",
        "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)  \n",
        "Llama 3.2 대화형 노트북. [무료 Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)\n",
        "\n",
        "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)  \n",
        "Ollama에 finetunes 저장. [무료 notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
        "\n",
        "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)  \n",
        "Llama 3.2 Vision finetuning - 방사선 촬영 사용 사례. [무료 Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
        "\n",
        "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!  \n",
        "DPO, ORPO, 지속적인 사전 훈련, 대화 미세 조정 등에 대한 노트북을 [문서](https://docs.unsloth.ai/get-started/unsloth-notebooks)에서 확인하세요!\n",
        "\n",
        "\n",
        "<div class=\"align-center\">\n",
        "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
        "  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
        "\n",
        "  Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
        "</div>\n",
        "\n",
        "\n",
        "\n",
        "도움이 필요하면 Discord에 가입하세요 + ⭐️ <i>별표 표시 <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
        "</div>\n"
      ]
    }
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