RAFT-Finetuning-Starling7b.ipynb

{
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        },
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        "outputId": "815df92e-2b82-4eae-8b33-50341ec40da4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fri Mar 29 20:24:41 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM4-40GB          Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   31C    P0              45W / 400W |      2MiB / 40960MiB |      0%      Default |\n",
            "|                                         |                      |             Disabled |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "LNFbpQA44tMk"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install transformers datasets accelerate peft huggingface_hub hf_transfer flash-attn trl wandb -qU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "fm-s9udr4wL1"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ[\"HF_TOKEN\"] = \"<>\"\n",
        "os.environ[\"HF_HUB_ENABLE_HF_TRANSFER\"] = \"1\"\n",
        "os.environ[\"WANDB_API_KEY\"] = \"<>\"\n",
        "os.environ[\"WANDB_PROJECT\"] = \"raft-starling\"\n",
        "os.environ[\"WANDB_NAME\"] = \"RAFT-Starling\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Ljz2Zp444xrh"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "from IPython.display import Markdown\n",
        "from peft import LoraConfig, get_peft_model\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, EarlyStoppingCallback, TrainingArguments\n",
        "from trl import SFTTrainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ehwx_5MH4zRj",
        "outputId": "ab22f6c2-f5b1-42b0-bacb-224c410201c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Is Bfloat16 avaiable?: True\n"
          ]
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(f\"Is Bfloat16 avaiable?: {torch.cuda.is_bf16_supported()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSeEa1_440_3"
      },
      "source": [
        "### 1. Load model and tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "wdnwEg8X4-3b"
      },
      "outputs": [],
      "source": [
        "model_name = \"Nexusflow/Starling-LM-7B-beta\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qo8Yj8t5BEj"
      },
      "source": [
        "#### 1.1 Load model"
      ]
    },
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      "execution_count": 7,
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        "outputId": "2d279ad7-f5de-449c-de89-d3fe951c9bb7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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            "text/plain": [
              "config.json:   0%|          | 0.00/671 [00:00<?, ?B/s]"
            ]
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            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
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        },
        {
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        {
          "data": {
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              "model_id": "dad4c8b888244edfa8ad5e0edbb75a3e",
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            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "generation_config.json:   0%|          | 0.00/158 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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      ],
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    pretrained_model_name_or_path=model_name,\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    device_map=\"auto\",\n",
        "    attn_implementation=\"flash_attention_2\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJYufW0C5FFS"
      },
      "source": [
        "#### 1.2 Load tokenizer"
      ]
    },
    {
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        "id": "qhdDaGBL5HEC",
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      },
      "outputs": [
        {
          "data": {
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              "model_id": "2df7d3db049545eb9a0bb03ca8021607",
              "version_major": 2,
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            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.63k [00:00<?, ?B/s]"
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          },
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        {
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            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
            ]
          },
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        },
        {
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
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            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
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        },
        {
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            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/536 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    pretrained_model_name_or_path=model_name,\n",
        "    padding_side=\"left\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsyIaMdP5I-t",
        "outputId": "ec3d5b52-41e3-46c3-f89b-77e16f774935"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Vocabulary size of Nexusflow/Starling-LM-7B-beta: 32,002\n"
          ]
        }
      ],
      "source": [
        "print(f\"Vocabulary size of {model_name}: {len(tokenizer.get_vocab()):,}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJho8saC5MC2",
        "outputId": "3a1c585e-ca53-486a-9769-350ab9f5f76d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'bos_token': '<s>',\n",
              " 'eos_token': '<|end_of_turn|>',\n",
              " 'unk_token': '<unk>',\n",
              " 'pad_token': '<|end_of_turn|>',\n",
              " 'additional_special_tokens': ['<|end_of_turn|>', '<|pad_0|>']}"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.special_tokens_map"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtD-ZBZG5OJa"
      },
      "source": [
        "#### 1.3 Inferece test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "hZmToW4v5Os7"
      },
      "outputs": [],
      "source": [
        "generation_config = {\n",
        "    \"max_new_tokens\": 500,\n",
        "    \"do_sample\": True,\n",
        "    \"temperature\": 0 + 1e-5,\n",
        "    \"top_k\": 100,\n",
        "    \"top_p\":0.90,\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCSEpmWF5QmE",
        "outputId": "62f65b86-76ae-404a-ef0b-58bff95f5e0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>GPT4 Correct System: You are an ancient wizard, and you communicate with mystical riddles and enigmatic metaphors.<|end_of_turn|>GPT4 Correct User: What is 1 + 1?<|end_of_turn|>GPT4 Correct Assistant:\n"
          ]
        }
      ],
      "source": [
        "chat = [\n",
        "    {\"role\": \"system\", \"content\": \"You are an ancient wizard, and you communicate with mystical riddles and enigmatic metaphors.\"},\n",
        "    {\"role\": \"user\", \"content\": \"What is 1 + 1?\"},\n",
        "]\n",
        "formatted_chat = tokenizer.apply_chat_template(conversation=chat, tokenize=False, add_generation_prompt=True)\n",
        "print(formatted_chat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XvB6OcZd5RZR",
        "outputId": "a4529679-b917-4dcb-96e3-0f4eab7f6672"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ah, mortal one, you seek the answer to a simple arithmetic question, yet even in its simplicity lies a profound truth. In the realm of numbers, 1 plus 1 equals 2, a union of two distinct entities forming a new whole. This concept transcends the mundane and echoes through the cosmos, for it is the essence of unity, balance, and harmony.\n",
            "\n",
            "In the grand tapestry of existence, the sum of 1 and 1 represents the duality of life, the interplay of opposing forces that give rise to the rich tapestry of experience. It is the foundation of the Fibonacci sequence, a pattern that can be found in the spirals of seashells, the arrangement of leaves on a stem, and the growth of celestial bodies.\n",
            "\n",
            "So, mortal one, the answer to your query is 2, but remember that even in the most basic of calculations, there lies a deeper truth, a cosmic dance of numbers that weaves the fabric of reality itself.\n"
          ]
        }
      ],
      "source": [
        "input_ids = tokenizer(text=formatted_chat, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(**input_ids, **generation_config)\n",
        "print(tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True).split(\"GPT4 Correct Assistant: \")[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dV4_dqtR5TuO"
      },
      "source": [
        "### 2. Train data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "affccb2d75e04b99812395828e46d66c",
            "f90a9e0240e04f1b8d2eb0eac184068a",
            "1894ba8b6b4c44d29c6249ee06e68c0a",
            "8efddeebd8c4464da440d60557e05b7f",
            "09b01a54faab459cb11d0bfedd304121",
            "157945869ec74043bbbe60fdaf1ada31",
            "14e926ff25df4067bf3223c95661855c",
            "2bbb63d498c04ec198eb7bd3ab595356",
            "67e6b9a773b24e5f9932670d6b984be0",
            "7bde82f7550f48c0a06e843deb120e77",
            "e9d360be169241629cd766eb68bc5625",
            "cd3a1045bf2b47698fb5f7a21626f7cc",
            "501e24a2ecb346839b44e3d714e8df3f",
            "ab8ceb51b0964fe0aee74639ea0b631f",
            "2b75e90767d341c9988b2f6345258c90",
            "f802fc1ccb3a4a5c9206d61fd378bf73",
            "5781ee9bed42430a9887e6516bd253cc",
            "9b7a787239ed404faf4f3e669ccbf52d",
            "dec8188a40734e0e99f3e63b20695297",
            "a1ac235a5656471bb52ee72ef905404a",
            "075389f63b7b4733aacb242825cbf31c",
            "19665f1330744318a1eecb51867ba4c9",
            "4c0db3b3f8db4f5c9ebad53b9a9d8460",
            "36349a8974fd4ebc810b3d9df601733c",
            "3bdd38057d2c40d0ba02233d05fe2c86",
            "24d9b98ddc8247c286a54d2864c4cf9b",
            "151dbff032ee4b5aa143030a271589af",
            "8a563f02005e45ffa39453128a98bda3",
            "7f76666428d24b12b5bb90d17222c2fb",
            "6447ad9f64024bdf8d3e0263faea85dd",
            "19350bdb7cf846c88449589782122502",
            "4d4fa82ea11e43fdab06bb70f4262a04",
            "8f03793b0df7412cb6998f009c6dac6e"
          ]
        },
        "id": "ygAm7RAt5VKz",
        "outputId": "f4fa4489-4102-4690-9e05-b9dd0ad2d91e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "affccb2d75e04b99812395828e46d66c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/619 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd3a1045bf2b47698fb5f7a21626f7cc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/17.7M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4c0db3b3f8db4f5c9ebad53b9a9d8460",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/1671 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dataset = load_dataset(\"jjovalle99/raft-dataset-aws-wellarchitected\", split=\"train\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VcUXuLM5XBL"
      },
      "source": [
        "#### 2.2 Split into test and val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jCnFUnT45Yxq",
        "outputId": "a5e618aa-a581-4fac-8463-5aa8f284da2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train size: 1336 Test size: 335\n"
          ]
        }
      ],
      "source": [
        "train_test_split = dataset.train_test_split(test_size=0.2, seed=1399, shuffle=True)\n",
        "train_data = train_test_split[\"train\"].shuffle()\n",
        "val_data = train_test_split[\"test\"].shuffle()\n",
        "print(f\"Train size: {len(train_data)}\", f\"Test size: {len(val_data)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtfeTZ0b6DlT"
      },
      "source": [
        "#### 2.3 Adapting to RAFT style prompts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvUZZeFg6Ijq"
      },
      "source": [
        "RAFT suggest the following instruction-answer pair:\n",
        "```\n",
        "{\n",
        "  'instruction': '<DOCUMENT> document1 </DOCUMENT>\\n<DOCUMENT> document2 </DOCUMENT> ...\\n{question}\",\n",
        "  'gold_answer': '{answer}'\n",
        " }\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "U2zF3uxs6Uou"
      },
      "outputs": [],
      "source": [
        "torch.manual_seed(1399)\n",
        "sample = train_data[torch.randint(low=0, high=len(train_data), size=(1,)).item()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "CC8tNi7m5dSL"
      },
      "outputs": [],
      "source": [
        "def format_example(example, return_answer=True):\n",
        "\n",
        "    instruction = \"Carefully read and analyze the provided documents to answer the question that follows. \" \\\n",
        "    \"Provide a detailed, step-by-step explanation of your reasoning, demonstrating how you arrived at your\" \\\n",
        "    \" conclusion based on the information given in the documents.\\n\\n\"\n",
        "\n",
        "    chat = [{\"role\": \"user\", \"content\": instruction + example[\"instruction\"]}]\n",
        "\n",
        "    prompt = tokenizer.apply_chat_template(\n",
        "        conversation=chat,\n",
        "        tokenize=False,\n",
        "        add_generation_prompt=True\n",
        "    )\n",
        "\n",
        "    if return_answer:\n",
        "        prompt += f\" {example['cot_answer']}<|end_of_turn|>\"\n",
        "\n",
        "    return prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOaIkcFi57wL",
        "outputId": "1c881258-58be-473e-dcf6-028ba5b21704"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>GPT4 Correct User: Carefully read and analyze the provided documents to answer the question that follows. Provide a detailed, step-by-step explanation of your reasoning, demonstrating how you arrived at your conclusion based on the information given in the documents.\n",
            "\n",
            "<DOCUMENT>Amazon CloudWatch Logs also supports subscriptions that allow data to flow seamlessly to Amazon S3 where you can use or Amazon Athena to query the data. It also supports queries on a large array of formats. See Supported SerDes and Data Formats in the Amazon Athena User Guide for more information. For analysis of huge log file sets, you can run an Amazon EMR cluster to run petabyte-scale analyses. There are a number of tools provided by AWS Partners and third parties that allow for aggregation, processing, storage, and analytics. These tools include New Relic, Splunk, Loggly, Logstash, CloudHealth, and Nagios. However, outside generation of system and application logs is unique to each cloud provider, and often unique to each service. An often-overlooked part of the monitoring process is data management. You need to determine the retention requirements for monitoring data, and then apply lifecycle policie\n",
            "...\n",
            "ent paths in the bucket. Toward the end of the lifecycle, you can transition data to Amazon S3 Glacier for long-term storage, and then expiration after the end of the retention period is reached. The S3 Intelligent-Tiering storage class is designed to optimize costs by automatically moving data to the most cost-effective access tier, without performance impact or operational overhead. ##end_quote## provides a clear guideline. Firstly, lifecycle management is supported at the S3 bucket level, allowing for different applications across various paths within the bucket. Key actions include transitioning data to Amazon S3 Glacier for long-term storage and setting data to expire after a specified retention period. Additionally, using the S3 Intelligent-Tiering storage class can automatically optimize costs by moving data to the most cost-effective access tier. ##Answer: Lifecycle management in Amazon S3 can be applied at the bucket level, allowing for different policies on different paths within the bucket, including transitioning data to S3 Glacier for long-term storage, setting expiration policies, and using the S3 Intelligent-Tiering storage class for cost optimization.<|end_of_turn|>\n"
          ]
        }
      ],
      "source": [
        "print(format_example(example=sample, return_answer=True)[:1200])\n",
        "print(\"...\")\n",
        "print(format_example(example=sample, return_answer=True)[-1200:])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ8LN8Wd6AHp"
      },
      "source": [
        "#### 2.4 Baseline performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3EuOTrH6cto",
        "outputId": "8c0bf983-ef41-4878-b463-9fee2b65ffc3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Amazon S3 lifecycle management can be applied by following these steps:\n",
            "\n",
            "1. Determine retention requirements: First, you need to determine the retention requirements for your monitoring data. This will help you decide how long you need to store the data and when it can be transitioned or expired.\n",
            "\n",
            "2. Apply lifecycle policies: Amazon S3 allows you to apply lifecycle policies at the bucket level. These policies can be used to transition objects to different storage classes, such as S3 Standard, S3 Intelligent-Tiering, or S3 Glacier, based on their age or other criteria.\n",
            "\n",
            "3. Define transitions and expiration: Within the lifecycle policy, you can define transitions that move objects to different storage classes based on specific conditions, such as the object's age or a custom prefix. You can also define expiration rules that delete objects after a certain period.\n",
            "\n",
            "4. Monitor and adjust: Regularly monitor the performance and cost of your S3 storage and lifecycle policies. Adjust the policies as needed to optimize storage costs and ensure that you meet your retention requirements.\n",
            "\n",
            "5. Integrate with other AWS services: You can integrate Amazon S3 lifecycle management with other AWS services, such as Amazon Athena for querying data, Amazon CloudWatch Logs for monitoring, and Amazon CloudWatch Logs Insights for analyzing log data.\n",
            "\n",
            "By applying Amazon S3 lifecycle management, you can optimize storage costs, manage data retention, and ensure that your monitoring data is available when needed. This helps in maintaining the reliability and performance of your workloads while minimizing the risk of data loss or uncontrolled growth.\n"
          ]
        }
      ],
      "source": [
        "input_text = format_example(example=sample, return_answer=False)\n",
        "input_ids = tokenizer(text=input_text, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(**input_ids, **generation_config)\n",
        "print(tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True).split(\"GPT4 Correct Assistant:\")[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwZe--n56fmj"
      },
      "source": [
        "### 3. Parameter Efficient Fine-Tuning (PEFT) - LoRA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcfVCzGI6i3I",
        "outputId": "3db3eea5-b235-4e4c-8af9-1076f3f5657b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MistralForCausalLM(\n",
            "  (model): MistralModel(\n",
            "    (embed_tokens): Embedding(32002, 4096, padding_idx=32000)\n",
            "    (layers): ModuleList(\n",
            "      (0-31): 32 x MistralDecoderLayer(\n",
            "        (self_attn): MistralFlashAttention2(\n",
            "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (k_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
            "          (v_proj): Linear(in_features=4096, out_features=1024, bias=False)\n",
            "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
            "          (rotary_emb): MistralRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): MistralMLP(\n",
            "          (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
            "          (up_proj): Linear(in_features=4096, out_features=14336, bias=False)\n",
            "          (down_proj): Linear(in_features=14336, out_features=4096, bias=False)\n",
            "          (act_fn): SiLU()\n",
            "        )\n",
            "        (input_layernorm): MistralRMSNorm()\n",
            "        (post_attention_layernorm): MistralRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): MistralRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=4096, out_features=32002, bias=False)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdVQaeOT6lIT"
      },
      "source": [
        "#### 3.1 Prepare LoRA Fine-Tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "mrqeEc7-6oUj"
      },
      "outputs": [],
      "source": [
        "model.gradient_checkpointing_enable()\n",
        "if model.config.to_dict()[\"use_cache\"]:\n",
        "    model.use_cache = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "U7qo0tbc6p07"
      },
      "outputs": [],
      "source": [
        "peft_config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "kR2sp_nW6rJB"
      },
      "outputs": [],
      "source": [
        "peft_model = get_peft_model(model=model, peft_config=peft_config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dc9fAioZ6scI"
      },
      "source": [
        "#### 3.2 Check trainable parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "l1wqYXxw6vTS"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XdHRLxa6wgd",
        "outputId": "4a20e398-b4b1-4b2f-9a42-e728dd82001b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 41943040 || all params: 7283691520 || trainable%: 0.5758486597741032\n"
          ]
        }
      ],
      "source": [
        "print_trainable_parameters(peft_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWYbv6E76xyu"
      },
      "source": [
        "### 4. Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "x14FOj9g60Du"
      },
      "outputs": [],
      "source": [
        "args_definition = dict(\n",
        "    output_dir=\"/starling-lora\",\n",
        "    overwrite_output_dir=True,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_train_batch_size=8,\n",
        "    gradient_accumulation_steps=4,\n",
        "    learning_rate=3e-4,\n",
        "    max_steps=500,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    max_grad_norm = 0.3,\n",
        "    warmup_steps=100,\n",
        "    logging_steps=20,\n",
        "    save_steps=20,\n",
        "    logging_first_step=True,\n",
        "    seed=1399,\n",
        "    bf16=True,\n",
        "    report_to=\"wandb\",\n",
        "    metric_for_best_model=\"eval_loss\",\n",
        "    greater_is_better=False,\n",
        "    load_best_model_at_end=True\n",
        ")\n",
        "args = TrainingArguments(**args_definition)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254,
          "referenced_widgets": [
            "b7e7c30a00e64ec5b13661d73ef42bd8",
            "bdbb5c705e7440a1a679fbf922babb7b",
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            "86a4b2297798422eab5c8f669d9cd4e4",
            "fe022e5ecff348048a0bdba409116e16",
            "659c3eddf3ef4c19964f887ac45b9324",
            "ec3a6ed008214cf28ff4e9cdb17939ff",
            "d7e8c0e4e7bd48ea84169cd991c40e21",
            "9eff8a7c4ca742f88363f2b4510557e1",
            "06947cb79c3449498a1737fc9aad7aff",
            "d34cb7cf524247a68b37a6140c2a4fab",
            "89805d8b5e0b4de79a933797121349e5",
            "11de8fae994f41cd9fc9adb161cfde7e",
            "e739b4b316104bb0b256caced807e533",
            "c00895d524ae42afbbef5159b90327eb",
            "fbfaaf0f1b0a431f909ba81cc4f98526",
            "50068bbc2b4a48d4bdc43c73d952dd51",
            "07f2434f915642e6bc02953cbd4840d6",
            "0e7698d7715a456a8508db2ce11f600d",
            "25a4a86a86154105a948b5546828ca70",
            "bc7d23f57084412885a36a9cc20ede48",
            "437c9a7da3e54048be8347f8b1fc8d29"
          ]
        },
        "id": "dubPjLRW611v",
        "outputId": "e000a528-f01a-4fce-de9f-98d8073a117e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/utils.py:440: UserWarning: The passed formatting_func has more than one argument. Usually that function should have a single argument `example` which corresponds to the dictionary returned by each element of the dataset. Make sure you know what you are doing.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b7e7c30a00e64ec5b13661d73ef42bd8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89805d8b5e0b4de79a933797121349e5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:317: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:341: UserWarning: You passed `packing=True` to the SFTTrainer, and you are training your model with `max_steps` strategy. The dataset will be iterated until the `max_steps` are reached.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "trainer = SFTTrainer(\n",
        "    model=peft_model,\n",
        "    args=args,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=val_data,\n",
        "    tokenizer=tokenizer,\n",
        "    peft_config=peft_config,\n",
        "    formatting_func=format_example,\n",
        "    max_seq_length=1024,\n",
        "    packing=True,\n",
        "    callbacks=[EarlyStoppingCallback(early_stopping_patience=1)]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        },
        "id": "XsSfZxSa63d5",
        "outputId": "c89598c3-ee1b-41ce-dd8d-a72d8b292230"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mjj-ovalle\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.16.5"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20240329_203424-rsv6xyr4</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/jj-ovalle/raft-starling/runs/rsv6xyr4/workspace' target=\"_blank\">RAFT-Starling</a></strong> to <a href='https://wandb.ai/jj-ovalle/raft-starling' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/jj-ovalle/raft-starling' target=\"_blank\">https://wandb.ai/jj-ovalle/raft-starling</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View run at <a href='https://wandb.ai/jj-ovalle/raft-starling/runs/rsv6xyr4/workspace' target=\"_blank\">https://wandb.ai/jj-ovalle/raft-starling/runs/rsv6xyr4/workspace</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='360' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [360/500 1:27:55 < 34:23, 0.07 it/s, Epoch 3/5]\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",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.166800</td>\n",
              "      <td>1.918820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.753400</td>\n",
              "      <td>1.623220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.476100</td>\n",
              "      <td>1.340393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.191900</td>\n",
              "      <td>1.039754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.865800</td>\n",
              "      <td>0.711909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.565400</td>\n",
              "      <td>0.461050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.335500</td>\n",
              "      <td>0.332600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.263600</td>\n",
              "      <td>0.253806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.200981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.175900</td>\n",
              "      <td>0.171689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>0.155900</td>\n",
              "      <td>0.159355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>0.124700</td>\n",
              "      <td>0.148345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>0.094000</td>\n",
              "      <td>0.140691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>0.094300</td>\n",
              "      <td>0.132979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.086500</td>\n",
              "      <td>0.125714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>0.082600</td>\n",
              "      <td>0.120132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>0.083100</td>\n",
              "      <td>0.116138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>0.060700</td>\n",
              "      <td>0.126592</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=360, training_loss=0.5440019193622802, metrics={'train_runtime': 5292.4198, 'train_samples_per_second': 3.023, 'train_steps_per_second': 0.094, 'total_flos': 5.0572647114316186e+17, 'train_loss': 0.5440019193622802, 'epoch': 3.11})"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8mB7i6sPMGZ"
      },
      "source": [
        "#### 4.1 Compare outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "JCCku4T1PU8_"
      },
      "outputs": [],
      "source": [
        "fine_tuned_model = peft_model.merge_and_unload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "IwH08ZqkPaEn"
      },
      "outputs": [],
      "source": [
        "input_text = format_example(example=sample, return_answer=False)\n",
        "input_ids = tokenizer(text=input_text, return_tensors=\"pt\").to(device)\n",
        "outputs = fine_tuned_model.generate(**input_ids, **generation_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "wWGRMAYgPoI7",
        "outputId": "9d44a5c1-df2a-4c9d-9e7d-7202ee91c891"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "#### Question:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "How can Amazon S3 lifecycle management be applied?"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### RAFT FT Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              " ##Reason: To apply Amazon S3 lifecycle management, the context suggests a multi-step approach. First, ##begin_quote## You can use Amazon S3 Lifecycle to manage your objects throughout their lifecycle. ##end_quote## This implies that objects can be moved to different storage tiers or removed based on their lifecycle requirements. Second, ##begin_quote## You can leverage Amazon S3 Storage Lens metrics to identify optimization opportunities and gaps in lifecycle management. ##end_quote## This suggests that monitoring and analyzing the storage usage and performance can help in refining the lifecycle management strategy. Lastly, ##begin_quote## You can also use Amazon Data Lifecycle Manager to automate the creation and deletion of Amazon EBS snapshots and Amazon EBS-backed AMIs, and to add lifecycle policies to your Amazon S3 buckets. ##end_quote## This indicates that Amazon Data Lifecycle Manager can automate the lifecycle management process for various AWS resources, including Amazon S3 buckets. Therefore, to apply Amazon S3 lifecycle management, one should use Amazon S3 Lifecycle for managing objects, monitor with Amazon S3 Storage Lens, and automate the process with Amazon Data Lifecycle Manager. ##Answer: Use Amazon S3 Lifecycle for managing objects, monitor with Amazon S3 Storage Lens, and automate the process with Amazon Data Lifecycle Manager."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### Answer:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              " ##Reason: To apply Amazon S3 lifecycle management, the context ##begin_quote## Amazon S3 supports lifecycle management at the S3 bucket level. This lifecycle management can be applied differently to different paths in the bucket. Toward the end of the lifecycle, you can transition data to Amazon S3 Glacier for long-term storage, and then expiration after the end of the retention period is reached. The S3 Intelligent-Tiering storage class is designed to optimize costs by automatically moving data to the most cost-effective access tier, without performance impact or operational overhead. ##end_quote## provides a clear guideline. Firstly, lifecycle management is supported at the S3 bucket level, allowing for different applications across various paths within the bucket. Key actions include transitioning data to Amazon S3 Glacier for long-term storage and setting data to expire after a specified retention period. Additionally, using the S3 Intelligent-Tiering storage class can automatically optimize costs by moving data to the most cost-effective access tier. ##Answer: Lifecycle management in Amazon S3 can be applied at the bucket level, allowing for different policies on different paths within the bucket, including transitioning data to S3 Glacier for long-term storage, setting expiration policies, and using the S3 Intelligent-Tiering storage class for cost optimization."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(Markdown(\"#### Question:\"))\n",
        "display(Markdown(sample[\"question\"]))\n",
        "display(Markdown(\"#### RAFT FT Completion:\"))\n",
        "display(Markdown(tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True).split(\"GPT4 Correct Assistant:\")[1]))\n",
        "display(Markdown(\"#### Answer:\"))\n",
        "display(Markdown(\" \" + sample[\"cot_answer\"]))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bmb1izfWRgO"
      },
      "source": [
        "#### 4.2 Performance on test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b00343541a7c47d9ab4c5376a9482349",
            "2494470edf164d8786795967c426c889",
            "7a74b80956db4a4884cc21859f11e2f6",
            "26f054caffc34f52a3aeee0722a7ef1e",
            "79ef6640fb1b4a938552ed58e43dcbbb",
            "211091be0f5b4cf4a13b0e68d8888945",
            "3b63a0543e0e4c43aea854ca84e413ef",
            "550d7782ec754260b620aaae9eb40039",
            "6ffba4b133944dd78675f19573cd419f",
            "0da9080dab18417795a62dc1d6e70fc3",
            "43049881ad744df6894c8e846daf9354"
          ]
        },
        "id": "478av9cCWdTM",
        "outputId": "81e45f9a-84e9-48c4-c343-1e52afd97461"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b00343541a7c47d9ab4c5376a9482349",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "not_tuned_model = AutoModelForCausalLM.from_pretrained(\n",
        "    pretrained_model_name_or_path=model_name,\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    device_map=\"auto\",\n",
        "    attn_implementation=\"flash_attention_2\"\n",
        ")\n",
        "\n",
        "fine_tuned_model.use_cache = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "sfC-vl-vWgZb"
      },
      "outputs": [],
      "source": [
        "def generate_responses(example, ft_model, og_model):\n",
        "    input_text = format_example(example=example, return_answer=False)\n",
        "    input_ids = tokenizer(text=input_text, return_tensors=\"pt\").to(device)\n",
        "    ft_outputs = ft_model.generate(**input_ids, **generation_config)\n",
        "    og_outputs = og_model.generate(**input_ids, **generation_config)\n",
        "\n",
        "    display(Markdown(\"#### Question:\"))\n",
        "    display(Markdown(example[\"question\"]))\n",
        "    display(Markdown(\"#### Original Completion:\"))\n",
        "    display(Markdown(tokenizer.decode(token_ids=og_outputs[0], skip_special_tokens=True) \\\n",
        "           .split(\"GPT4 Correct Assistant:\")[1]))\n",
        "    display(Markdown(\"#### RAFT Fine-tuned Completion:\"))\n",
        "    display(Markdown(tokenizer.decode(token_ids=ft_outputs[0], skip_special_tokens=True) \\\n",
        "           .split(\"GPT4 Correct Assistant:\")[1]))\n",
        "    display(Markdown(\"#### Expected Answer:\"))\n",
        "    display(Markdown(\" {answer}\".format(answer=example[\"cot_answer\"])))\n",
        "    display(Markdown(\"-----------------------------\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Rtpn4jkLWk1n",
        "outputId": "58234b90-2931-4dac-c16e-7d15440af518"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "#### Question:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "How can software and architecture patterns support sustainability goals?"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### Original Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              " Software and architecture patterns can support sustainability goals in various ways, including:\n",
              "\n",
              "1. Resource optimization: By using efficient algorithms and data structures, software can minimize resource consumption, such as memory, CPU, and energy usage. This reduces the environmental impact of running the software and lowers operational costs.\n",
              "\n",
              "2. Scalability: Designing scalable systems allows them to handle increased workloads without requiring additional hardware resources. This reduces the need for physical infrastructure expansion, which can have a significant environmental impact.\n",
              "\n",
              "3. Modularity: Modular architectures promote reusability and maintainability, reducing the need for duplicated effort and simplifying updates. This can lead to more efficient use of resources and a reduced environmental footprint.\n",
              "\n",
              "4. Resilience: Building resilient systems that can recover from failures and continue to operate effectively reduces the risk of downtime and data loss. This can minimize the environmental impact of system outages and improve overall sustainability.\n",
              "\n",
              "5. Monitoring and observability: Implementing comprehensive monitoring and observability practices helps identify performance bottlenecks and potential issues early. This enables proactive optimization and reduces the likelihood of resource-intensive issues, such as high CPU usage or memory leaks, which can negatively impact sustainability.\n",
              "\n",
              "6. Green computing: Incorporating green computing principles, such as energy-efficient hardware, virtualization, and power management, can significantly reduce the environmental impact of software systems.\n",
              "\n",
              "7. Sustainable cloud practices: Utilizing cloud services that prioritize sustainability, such as renewable energy-powered data centers and efficient resource allocation, can help reduce the environmental footprint of software systems.\n",
              "\n",
              "8. Circular economy: Designing software with a focus on longevity, adaptability, and compatibility can extend the lifespan of hardware components, reducing electronic waste and promoting a circular economy.\n",
              "\n",
              "9. Open-source and collaboration: Open-source software and collaboration can lead to more efficient use of resources, as developers can build upon existing work and avoid duplicating effort. This can also foster innovation and the development of sustainable solutions.\n",
              "\n",
              "10. Environmental impact assessment: Regularly assessing the environmental impact of software systems and making improvements can help organizations continuously work towards sustainability goals.\n",
              "\n",
              "By incorporating these practices into software and architecture design, organizations can create more sustainable systems that minimize their environmental impact while still"
            ],
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              "<IPython.core.display.Markdown object>"
            ]
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          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "#### RAFT Fine-tuned Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
            "text/markdown": [
              " ##Reason: Software and architecture patterns can support sustainability goals in several ways. Firstly, ##begin_quote## using efficient software and architecture patterns ##end_quote## can lead to ##begin_quote## lower resource usage and improved performance ##end_quote##, which directly contributes to sustainability goals by reducing the resources required to support workloads and minimizing the environmental impact. Secondly, understanding the trade-offs between different architectural approaches, such as using shared databases over distributed databases, can help in making informed decisions that align with sustainability goals by minimizing data movement and duplication across networks. Lastly, ##begin_quote## using efficient software and architecture patterns ##end_quote## can lead to ##begin_quote## improved performance and reduced environmental impact ##end_quote##, which is a key sustainability goal. Therefore, software and architecture patterns play a crucial role in supporting sustainability goals by optimizing resource usage, minimizing data movement, and reducing environmental impact. ##Answer: By optimizing resource usage, minimizing data movement, and reducing environmental impact."
            ],
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              "<IPython.core.display.Markdown object>"
            ]
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        {
          "data": {
            "text/markdown": [
              "#### Expected Answer:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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          "data": {
            "text/markdown": [
              " ##Reason: To support sustainability goals, software and architecture patterns can be leveraged in several ways as outlined in the context. First, by ##begin_quote## implementing patterns for performing load smoothing and maintaining consistent high utilization of deployed resources ##end_quote##, resources are minimized. This involves adjusting to changes in user behavior to prevent components from becoming idle and under-utilized. Additionally, the context suggests ##begin_quote## consolidating under-utilized components to increase overall utilization and retiring components that are no longer required. ##end_quote## This helps in reducing the resources consumed by ensuring that only necessary components are active and efficiently used. Furthermore, understanding the performance of workload components and optimizing those that consume the most resources can lead to more sustainable software practices. Lastly, being aware of the devices customers use and implementing patterns to minimize the need for device upgrades can also support sustainability goals by reducing electronic waste and the resources required for manufacturing new devices. ##Answer: By implementing load smoothing, maintaining high utilization of resources, consolidating under-utilized components, retiring unnecessary components, optimizing resource-intensive components, and minimizing the need for device upgrades."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
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          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "-----------------------------"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### Question:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "How can customer satisfaction be measured according to the implementation guidance?"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### Original Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
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          "metadata": {},
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          "data": {
            "text/markdown": [
              " According to the implementation guidance provided in the documents, customer satisfaction can be measured by combining various metrics and KPIs that reflect the operations goals of the organization. Here are some steps to follow for measuring customer satisfaction:\n",
              "\n",
              "1. Identify key performance indicators (KPIs): Determine the KPIs that best reflect the operations goals of your organization. These may include customer satisfaction, time from feature conception to deployment, average issue resolution time, and others.\n",
              "\n",
              "2. Determine metrics and sources of data: For each identified KPI, identify the metrics and sources of data that can best reflect these goals. For example, customer satisfaction may be a combination of various metrics such as call wait times, response times, satisfaction scores, and types of issues raised.\n",
              "\n",
              "3. Monitor and analyze data: Continuously monitor the identified metrics and analyze the data to understand trends, patterns, and areas for improvement. This can be done using various tools, such as dashboards and automated reporting systems.\n",
              "\n",
              "4. Use data to drive improvements: Use the insights gained from the analysis of the metrics to make data-driven decisions and improvements in your operations. This may involve adjusting processes, addressing bottlenecks, or implementing new technologies to enhance customer experience and satisfaction.\n",
              "\n",
              "5. Engage with customers: Regularly engage with customers through feedback channels, surveys, or direct communication to understand their needs, expectations, and experiences with your services. This feedback can provide valuable insights into areas where improvements can be made to increase customer satisfaction.\n",
              "\n",
              "6. Foster a culture of continuous improvement: Encourage diverse opinions and perspectives within and across teams to challenge assumptions, increase innovation, and reduce the risk of confirmation bias. This can lead to better decision-making and improved customer satisfaction over time.\n",
              "\n",
              "By following these steps, you can effectively measure customer satisfaction according to the implementation guidance provided in the AWS Well-Architected Framework documents."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "#### RAFT Fine-tuned Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              " ##Reason: According to the implementation guidance, customer satisfaction can be measured by identifying key performance indicators (KPIs) that reflect the state of operations. ##begin_quote## Measure the effectiveness of these teams and evaluate them against business goals by creating metrics that can reflect the state of operations. ##end_quote## These KPIs could include customer satisfaction scores, wait times, issue resolution times, and other metrics that directly impact the customer experience. By monitoring these KPIs, businesses can gain insight into the satisfaction levels of their customers and make adjustments as needed to improve satisfaction and operational efficiency. ##Answer: By identifying and monitoring key performance indicators (KPIs) that reflect the state of operations and customer satisfaction."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### Expected Answer:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              " ##Reason: According to the implementation guidance, customer satisfaction can be measured by ##begin_quote## brainstorming key performance indicators (KPIs) that might reflect these operations goals. ##end_quote## These KPIs could include ##begin_quote## customer satisfaction, time from feature conception to deployment, average issue resolution time, and others. ##end_quote## Specifically for customer satisfaction, it may be measured by a combination of various metrics such as ##begin_quote## call wait or response times, satisfaction scores, and types of issues raised. ##end_quote## Therefore, to measure customer satisfaction, one should identify relevant metrics and sources of data that reflect these goals, focusing on aspects like response times, satisfaction scores, and issue types. ##Answer: By identifying relevant metrics and sources of data such as call wait or response times, satisfaction scores, and types of issues raised."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "-----------------------------"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "#### Question:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "How does the cloud model impact the procurement and consumption process compared to traditional on-premises environments?"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "#### Original Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
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          "metadata": {},
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          "data": {
            "text/markdown": [
              " The cloud model significantly impacts the procurement and consumption process compared to traditional on-premises environments in several ways:\n",
              "\n",
              "1. Agility and Speed: In the cloud, infrastructure provisioning and deployment are much faster than in on-premises environments. This is due to the elimination of time-consuming processes such as hardware procurement, installation, and configuration. As a result, technology teams can innovate faster and respond more quickly to business needs.\n",
              "\n",
              "2. Pay-as-you-go Pricing: Cloud services are typically offered on a pay-as-you-go basis, meaning organizations only pay for the resources they actually use. This flexible pricing model allows organizations to optimize their costs by scaling up or down as needed, without the need for large upfront capital investments.\n",
              "\n",
              "3. Elimination of Over-provisioning: In the cloud, organizations can avoid over-provisioning by only purchasing the resources they need at any given time. This reduces the opportunity cost associated with conservative under-provisioning in on-premises environments.\n",
              "\n",
              "4. Simplified Procurement Processes: Cloud procurement processes are often streamlined compared to on-premises environments, as they eliminate the need for physical infrastructure and long-term capital budgeting. This simplification can lead to more efficient procurement processes and reduced administrative overhead.\n",
              "\n",
              "5. Cost Allocation and Tracking: Cloud providers offer detailed billing and usage information, making it easier for organizations to track and allocate costs across different business units, projects, or cost centers. This visibility can help organizations better understand their cloud spend and make more informed decisions about resource allocation.\n",
              "\n",
              "6. Shared Responsibility Model: In the cloud, the responsibility for managing and securing the infrastructure is shared between the cloud provider and the customer. This shared responsibility model can simplify the procurement process, as organizations no longer need to consider the full range of infrastructure management tasks when evaluating cloud services.\n",
              "\n",
              "7. Flexible Contracts and Commitments: Cloud providers offer various contract options, including pay-as-you-go, reserved instances, and savings plans. These options allow organizations to align their procurement strategies with their specific business needs and financial objectives.\n",
              "\n",
              "In summary, the cloud model offers a more agile, flexible, and cost-effective approach to proc"
            ],
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              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### RAFT Fine-tuned Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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          "data": {
            "text/markdown": [
              " ##Reason: The cloud model impacts the procurement and consumption process compared to traditional on-premises environments in several ways. Firstly, the cloud provides technology and product teams with the ability to innovate faster by not only being builders and operators of their products but also owners of their products, responsible for procurement and deployment. This differs from the traditional model where procurement and deployment were separate processes managed by different teams. ##begin_quote## Technology and product teams are no longer just builders, but operators and owners of their products, responsible for most of the activities historically associated with finance and operations teams, including procurement and deployment. ##end_quote## Secondly, the cloud model allows for variable resource consumption, which can reduce the cost of over-provisioning and the opportunity cost associated with conservative under-provisioning. ##begin_quote## The cloud provides technology and product teams with the ability to innovate faster - the variable nature of cloud consumption allows organizations to reduce the cost of over-provisioning, as well as reduce the opportunity cost associated with conservative under-provisioning. ##end_quote## Lastly, the cloud model requires a partnership between finance and technology teams to establish a shared understanding of organizational goals and develop mechanisms to succeed financially, which is different from the traditional model where finance and technology teams had separate roles and responsibilities. ##begin_quote## Establish a partnership between key finance and technology stakeholders to create a shared understanding of organizational goals and develop mechanisms to succeed financially in the variable spend model of cloud computing. ##end_quote## These differences in procurement and consumption processes highlight the flexibility and efficiency that the cloud model offers compared to traditional on-premises environments. ##Answer: The cloud model impacts procurement and consumption by allowing technology teams to be owners of their products, enabling variable resource consumption, and requiring a partnership between finance and technology teams to succeed financially."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
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          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "#### Expected Answer:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
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          "data": {
            "text/markdown": [
              " ##Reason: The cloud model significantly impacts the procurement and consumption process by enabling technology and product teams to operate with greater autonomy and efficiency compared to traditional on-premises environments. In traditional settings, the procurement process involves multiple steps and teams, including engineering or IT teams requesting resources, finance teams approving and procuring, and operations teams installing and preparing the infrastructure. This process is time-consuming and resource-intensive. ##begin_quote## Technology teams innovate faster in the cloud due to shortened approval, procurement, and infrastructure deployment cycles. ##end_quote## In contrast, the cloud model simplifies this process by allowing technology teams to directly procure and deploy resources with just an account and the appropriate permissions, significantly reducing the time and complexity involved. ##begin_quote## With the adoption of cloud, infrastructure procurement and consumption are no longer beholden to a chain of dependencies. ##end_quote## Additionally, the cloud model supports a pay-as-you-go pricing structure, which offers the flexibility to scale resources up or down based on demand, thereby optimizing costs and reducing the risks associated with over-provisioning or under-provisioning in traditional environments. ##Answer: The cloud model simplifies and accelerates the procurement and consumption process by enabling direct procurement and deployment by technology teams, reducing dependencies, and offering flexible, pay-as-you-go pricing, which contrasts with the complex, time-consuming, and rigid processes of traditional on-premises environments."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
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          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "-----------------------------"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "#### Question:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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        {
          "data": {
            "text/markdown": [
              "What is the key difference between runbooks and playbooks?"
            ],
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              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
            "text/markdown": [
              "#### Original Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
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            "text/markdown": [
              " The key difference between runbooks and playbooks lies in their purpose, scope, and desired outcomes.\n",
              "\n",
              "1. Purpose:\n",
              "   - Runbooks are focused on achieving a specific desired outcome, often in response to an incident or issue. They are used to guide technicians through a series of steps to resolve a problem or complete a task.\n",
              "   - Playbooks, on the other hand, are more focused on automating processes and workflows, and they are designed to handle a variety of scenarios or events. They are used to manage and orchestrate multiple tasks and resources in a consistent and repeatable manner.\n",
              "\n",
              "2. Scope:\n",
              "   - Runbooks typically cover a narrower scope, addressing specific incidents or tasks. They are tailored to handle a particular issue or set of issues.\n",
              "   - Playbooks have a broader scope, covering a range of scenarios or events. They are designed to be flexible and adaptable, allowing them to handle different situations while maintaining consistency and efficiency.\n",
              "\n",
              "3. Desired Outcomes:\n",
              "   - The primary goal of a runbook is to achieve a specific desired outcome, such as resolving an incident or completing a task. It guides technicians through a step-by-step process to address the issue at hand.\n",
              "   - The primary goal of a playbook is to automate and streamline processes and workflows, improving efficiency and consistency across various scenarios. It automates tasks and orchestrates resources to handle different situations, reducing the need for manual intervention.\n",
              "\n",
              "In summary, runbooks are focused on achieving specific outcomes in response to incidents or tasks, while playbooks are designed to automate and streamline processes and workflows across a broader range of scenarios. Both are important components of a well-architected infrastructure, with runbooks playing a crucial role in incident response and task completion, and playbooks enhancing efficiency and consistency in managing various events and processes."
            ],
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              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "#### RAFT Fine-tuned Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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          "data": {
            "text/markdown": [
              " ##Reason: The key difference between runbooks and playbooks lies in their purpose and outcome. ##begin_quote## A runbook is a documented process to achieve a specific outcome. ##end_quote## This means that runbooks are designed to guide users through a series of steps to achieve a particular goal, such as fixing an issue or deploying a change. On the other hand, ##begin_quote## A playbook is a documented process to achieve a specific outcome, but with additional steps. ##end_quote## This indicates that playbooks, while also documenting processes for specific outcomes, include additional steps that are typically used in response to specific incidents or problems. In summary, the key difference is that runbooks are for routine tasks and playbooks are for responding to incidents, with playbooks often including additional steps not found in runbooks. ##Answer: Runbooks are documented processes to achieve a specific outcome, while playbooks are documented processes to achieve a specific outcome with additional steps, typically used for responding to incidents."
            ],
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              "<IPython.core.display.Markdown object>"
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        {
          "data": {
            "text/markdown": [
              "#### Expected Answer:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
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          "data": {
            "text/markdown": [
              " ##Reason: The document states ##begin_quote## Runbooks and playbooks are similar, but with one key difference: a runbook has a desired outcome. In many cases, runbooks are used once a playbook has identified a root cause. ##end_quote## This indicates that the key difference between runbooks and playbooks is that a runbook is specifically designed to have a desired outcome and is typically used after a playbook has identified a root cause. ##Answer: A runbook has a desired outcome and is used after a playbook has identified a root cause."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
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          "metadata": {},
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        {
          "data": {
            "text/markdown": [
              "-----------------------------"
            ],
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              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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        {
          "data": {
            "text/markdown": [
              "#### Question:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
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          "data": {
            "text/markdown": [
              "What is the purpose of defining metrics baselines in operational health?"
            ],
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              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
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        {
          "data": {
            "text/markdown": [
              "#### Original Completion:"
            ],
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              "<IPython.core.display.Markdown object>"
            ]
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            "text/markdown": [
              " The purpose of defining metrics baselines in operational health is to establish a clear understanding of the normal performance and behavior of a workload and its associated operations activities. By setting these baselines, organizations can effectively monitor, analyze, and respond to deviations from the expected performance levels.\n",
              "\n",
              "Here's a step-by-step explanation of how defining metrics baselines contributes to operational health:\n",
              "\n",
              "1. **Establishing a benchmark**: Metrics baselines serve as a benchmark for evaluating the performance and health of a workload and its operations. They provide a reference point against which current and future performance can be compared.\n",
              "\n",
              "2. **Identifying anomalies**: By having established baselines, organizations can quickly identify anomalies or deviations from the expected performance levels. These deviations may indicate potential issues or bottlenecks that need to be addressed.\n",
              "\n",
              "3. **Prioritizing incidents**: Metrics baselines help prioritize responses to operational events based on their business and customer impact. By understanding the normal performance levels, organizations can determine the severity of an incident and allocate resources accordingly.\n",
              "\n",
              "4. **Measuring success**: Metrics baselines enable organizations to measure the success of their workloads and operations activities. By comparing actual performance against the established baselines, organizations can determine if they are meeting their desired business and customer outcomes.\n",
              "\n",
              "5. **Continuous improvement**: Establishing metrics baselines facilitates continuous improvement by providing a foundation for ongoing monitoring, analysis, and optimization of workloads and operations activities. As the organization identifies areas for improvement, they can adjust their processes and infrastructure to better align with the established baselines.\n",
              "\n",
              "6. **Communication and transparency**: Metrics baselines help communicate the operational health of a workload to various stakeholders, such as customers, business units, developers, and operations teams. This transparency ensures that everyone involved has a clear understanding of the workload's performance and can make informed decisions based on that information.\n",
              "\n",
              "In summary, defining metrics baselines in operational health is crucial for establishing a benchmark for performance, identifying anomalies, prioritizing incidents, measuring success, facilitating continuous improvement, and promoting communication and transparency within the organization."
            ],
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              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### RAFT Fine-tuned Completion:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
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          "data": {
            "text/markdown": [
              " ##Reason: The purpose of defining metrics baselines in operational health is to ##begin_quote## establish a baseline for improvement, identify anomalies, and measure the impact of improvements. ##end_quote## This is crucial for understanding the health of the workload and the operations activities performed in support of the workload, such as deployment and incident response. By establishing baselines, organizations can measure the success of their operations against these baselines, identify areas for improvement, and track the effectiveness of their improvement efforts. This process helps in maintaining operational excellence by ensuring that the workload and operations activities are aligned with business objectives and customer needs. ##Answer: To establish a baseline for improvement, identify anomalies, and measure the impact of improvements."
            ],
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              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              "#### Expected Answer:"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/markdown": [
              " ##Reason: The purpose of defining metrics baselines in operational health is to establish a standard for improvement, investigation, and intervention. This is highlighted in the context where it mentions, ##begin_quote## Establish metrics baselines for improvement, investigation, and intervention, collect and analyze your metrics, and then validate your understanding of operations success and how it changes over time. ##end_quote## By setting these baselines, organizations can effectively monitor and analyze their operational metrics to ensure they are meeting customer and business needs, and identify areas that require enhancement. This process is crucial for maintaining operational excellence, as it allows for the efficient and effective management of both planned and unplanned operational events, ensuring that the workload operates smoothly and meets business and customer outcomes. ##Answer: To establish a standard for improvement, investigation, and intervention, ensuring operational excellence by monitoring and analyzing operational metrics to meet customer and business needs."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
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          "metadata": {},
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        },
        {
          "data": {
            "text/markdown": [
              "-----------------------------"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
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      "source": [
        "for i in range(5):\n",
        "    generate_responses(val_data[i], ft_model=fine_tuned_model, og_model=not_tuned_model)"
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        "#### 5. Save Model"
      ]
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        "model_save_name = \"starling-7b-raft-ft\""
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