AI_Planet_LLM_Bootcamp_Challenge.ipynb

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        "## Question 1\n",
        "- Use Transformers model and build a Code Generation application on [evolved codealpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset\n",
        "\n",
        "#### Fine-Tuning LLMs using Qlora\n",
        "\n",
        "In this notebook, we fine-tuned [Orca-3b](https://huggingface.co/pankajmathur/orca_mini_3b) on evolved codealpaca to perform Code Generation tasks. This is an example on how we can leverage on Colab's free T4 GPU to fine-tune LLMs using QloRA.\n",
        "\n",
        "#### Setup\n",
        "\n",
        "Run the cells below to setup and install the required libraries. For our experiment we will need `accelerate`, `peft`, `transformers`, `datasets`, `scipy` and `TRL` to leverage `SFTTrainer`. We will use `bitsandbytes` to quantize the base model into 4bit. We will also install `einops` but it is mainly used for loading falcon."
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            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.9/133.9 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install transformers==4.30\n",
        "!pip install -q -U trl accelerate sentencepiece git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U datasets bitsandbytes einops scipy wandb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the Evolved codealpaca dataset from HuggingFace"
      ],
      "metadata": {
        "id": "BpfLMIGQ2zwN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset_name = 'theblackcat102/evol-codealpaca-v1'\n",
        "dataset = load_dataset(dataset_name, split=\"train\")"
      ],
      "metadata": {
        "id": "FcDl8vwb2y1E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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            "f97c0c13c66244779694d430a0f7c49f",
            "d1e28a882b1c4d4592e96efb4aaddf28"
          ]
        },
        "outputId": "208a8953-6b66-4580-a097-8d12c8b0bf07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e363b2b898c541569c753f53088103c1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d0ed18ca11442f0a21eb876a387f25f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/255M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3cf20e742fb45fb86c7102e43aeaa73"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "69c3e34e45d7447886579d675255734c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b3c39cdf4664d5ea98cabb704570cec"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqePeY4iMDah",
        "outputId": "30e581e3-5f57-4abc-c08f-3feaa9cf7221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['instruction', 'output'],\n",
              "    num_rows: 111272\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elhwNE-6MYWo",
        "outputId": "b51bdc0b-5edd-4924-b9fa-f596c3547ed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'instruction': \"Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list.\\n\\nThe script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. \\n\\n```python\\n  # Establish an integer list\\n  arr = [1, 2, 3, 4]\\n\\n  # Determine the length of the list\\n  n = len(arr)\\n\\n  # Traverse the list and output each individual element\\n  for i in range(n+1):\\n      print(arr[i])\\n```\",\n",
              " 'output': '```python\\n# Establish an integer list\\narr = [1, 2, 3, 4]\\n\\n# Determine the length of the list\\nn = len(arr)\\n\\n# Initialize index at 0\\ni = 0\\n\\n# Traverse the list and output each individual element\\nwhile i < n:\\n    print(arr[i])\\n    i += 1\\n```\\nIn the given code, it tries to access `arr[n]` which is out of bounds as python list indexing starts from 0 and ends at n-1 for a list of length n.\\nAlso, I used `while` loop as per your request. By initializing `i=0` before the loop and `i += 1` within the loop, we can iterate over the list. This iteration continues until `i` is less than `n`, the length of the list. The `while` loop will not proceed if the list is empty so it correctly handles empty lists.'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['instruction'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "cD1QleMB4R5L",
        "outputId": "93c580aa-392d-4fc1-98ad-2ace8c3be750"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Please amend the subsequent Python script so that it includes a 'while' loop rather than the existing 'for' loop, which iterates through the items of an integer list.\\n\\nThe script currently has a bug where it attempts to print an object that is outside the bounds of the list. Fix this error and modify the script to use 'while' instead of 'for' loop. Ensure your script correctly handles empty lists. \\n\\n```python\\n  # Establish an integer list\\n  arr = [1, 2, 3, 4]\\n\\n  # Determine the length of the list\\n  n = len(arr)\\n\\n  # Traverse the list and output each individual element\\n  for i in range(n+1):\\n      print(arr[i])\\n```\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will subset the dataset to only 1000 samples due to the duration limitations on Colab's free usage of their T4 GPU"
      ],
      "metadata": {
        "id": "Epi3lDffphc2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# subset dataset to 1000 samples due to the limitation on Colab's free tier GPU usage\n",
        "samp_dataset = dataset.shuffle(seed=42).select(range(1000))\n",
        "\n",
        "len(samp_dataset)"
      ],
      "metadata": {
        "id": "esIii1IbNO11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aba1dffd-af2b-4ef3-8e15-b60a867a3d6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the model"
      ],
      "metadata": {
        "id": "zAf37T5d4J48"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "from accelerate import Accelerator\n",
        "\n",
        "model_name = \"psmathur/orca_mini_3b\"\n",
        "# model_name = \"microsoft/phi-1_5\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "  load_in_4bit=True,\n",
        "  bnb_4bit_quant_type=\"nf4\",\n",
        "  bnb_4bit_compute_dtype=torch.bfloat16,\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "  model_name,\n",
        "  quantization_config=bnb_config,\n",
        "  trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
          "referenced_widgets": [
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        },
        "id": "IRbKxyi66nzu",
        "outputId": "1e4e9192-9d3b-4d9f-fb25-5146b66a26e2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)ur/orca_mini_3b/resolve/main/config.json:   0%|          | 0.00/553 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)esolve/main/pytorch_model.bin.index.json:   0%|          | 0.00/21.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5996aac5ec304dd6ba9c134652c6b454"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00001-of-00003.bin:   0%|          | 0.00/4.99G [00:00<?, ?B/s]"
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00002-of-00003.bin:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26ea2b92aca149ccb28e74d3b9f66cb0"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00003-of-00003.bin:   0%|          | 0.00/3.72G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9281d1b8b3a24ad6954f85131b386609"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b672573a19e4031ad654239063328c1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)i_3b/resolve/main/generation_config.json:   0%|          | 0.00/132 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1835624568d74ef28cd688b5971ae443"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's also load the tokenizer"
      ],
      "metadata": {
        "id": "6_8JkjeFFds3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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        },
        "id": "Jrx8eARAAgBr",
        "outputId": "a933626a-3dc0-4fa8-d6dc-f2277ae43e9b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)ni_3b/resolve/main/tokenizer_config.json:   0%|          | 0.00/700 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c9362781a154712afc38b8e3785d0bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/534k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5d20a7dff7a440fbf714d98c7c35e63"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)_3b/resolve/main/special_tokens_map.json:   0%|          | 0.00/208 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d7e95c6329a482db4723b80e81e31c4"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Asking orca to generate scikit-learn linear regression codes before fine-tuning"
      ],
      "metadata": {
        "id": "ZdKtW2KfEJaH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "#generate text function\n",
        "def generate_text(system, instruction, input=None):\n",
        "\n",
        "    if input:\n",
        "        prompt = f\"### System:\\n{system}\\n\\n### User:\\n{instruction}\\n\\n### Input:\\n{input}\\n\\n### Response:\\n\"\n",
        "    else:\n",
        "        prompt = f\"### System:\\n{system}\\n\\n### User:\\n{instruction}\\n\\n### Response:\\n\"\n",
        "\n",
        "    tokens = tokenizer.encode(prompt)\n",
        "    tokens = torch.LongTensor(tokens).unsqueeze(0)\n",
        "    tokens = tokens.to('cuda')\n",
        "\n",
        "    instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024, 'top_k': 50}\n",
        "\n",
        "    length = len(tokens[0])\n",
        "    with torch.no_grad():\n",
        "        rest = model.generate(\n",
        "            input_ids=tokens,\n",
        "            max_length=length+instance['generate_len'],\n",
        "            use_cache=True,\n",
        "            do_sample=True,\n",
        "            top_p=instance['top_p'],\n",
        "            temperature=instance['temperature'],\n",
        "            top_k=instance['top_k']\n",
        "        )\n",
        "    output = rest[0][length:]\n",
        "    string = tokenizer.decode(output, skip_special_tokens=True)\n",
        "    return f'[!] Response: {string}'\n",
        "\n",
        "# Sample test instruction used by Youtuber Sam Witteveen https://www.youtube.com/@samwitteveenai\n",
        "system = 'You are an AI assistant that follows instruction extremely well. Help as much as you can.'\n",
        "instruction = 'Can you help me with scikit-learn linear regression codes'\n",
        "print(generate_text(system, instruction))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gZxQYC5g4No",
        "outputId": "586cd55d-2ee2-4827-83b7-8084719e65b0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[!] Response: Yes, I can help you with scikit-learn linear regression codes. However, I will need some more information about your problem to provide you with the correct code. Can you please provide me with the following details:\n",
            "\n",
            "1. What is the input data that you want to use for the linear regression?\n",
            "2. What is the target variable that you want to predict?\n",
            "3. What is the number of features that you want to use for the linear regression?\n",
            "4. What is the type of data that you want to use for the linear regression (numeric, categorical, etc.)?\n",
            "5. What is the output that you want scikit-learn to give you after running the linear regression code?\n",
            "\n",
            "Once you provide me with the above information, I can help you write the scikit-learn linear regression code for your problem.\n",
            "CPU times: user 14.5 s, sys: 29.2 ms, total: 14.5 s\n",
            "Wall time: 15.1 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "  lora_alpha=lora_alpha,\n",
        "  lora_dropout=lora_dropout,\n",
        "  r=lora_r,\n",
        "  bias=\"none\",\n",
        "  task_type=\"CAUSAL_LM\"\n",
        ")"
      ],
      "metadata": {
        "id": "F_-yLFndFovs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the trainer\n",
        "We will use the [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer) from `TRL` library that gives a wrapper around transformers Trainer to easily fine-tune models on instruction based datasets using `PEFT` adapters. Let's first load the training arguments below."
      ],
      "metadata": {
        "id": "mB1j3T2POodO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "output_dir = \"./results\"\n",
        "per_device_train_batch_size = 1\n",
        "gradient_accumulation_steps = 1\n",
        "optim = \"paged_adamw_32bit\"\n",
        "save_steps = 100\n",
        "logging_steps = 10\n",
        "learning_rate = 4e-3\n",
        "max_grad_norm = 0.3\n",
        "max_steps = -1\n",
        "warmup_ratio = 0.03\n",
        "lr_scheduler_type = \"constant\"\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "  output_dir=output_dir,\n",
        "  per_device_train_batch_size=per_device_train_batch_size,\n",
        "  gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "  optim=optim,\n",
        "  save_steps=save_steps,\n",
        "  logging_steps=logging_steps,\n",
        "  learning_rate=learning_rate,\n",
        "  fp16=True,\n",
        "  max_grad_norm=max_grad_norm,\n",
        "  max_steps=max_steps,\n",
        "  warmup_ratio=warmup_ratio,\n",
        "  group_by_length=True,\n",
        "  lr_scheduler_type=lr_scheduler_type,\n",
        "  num_train_epochs=1,\n",
        ")"
      ],
      "metadata": {
        "id": "YDiIdD5fOyFv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then finally pass everything to the trainer"
      ],
      "metadata": {
        "id": "B7ZI13i3O3-A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "def formatting_prompts_func(example):\n",
        "    output_texts = []\n",
        "    for i in range(len(example['instruction'])):\n",
        "        text = f\"### Instruction: {example['instruction'][i]}\\n ### Response: {example['output'][i]}\"\n",
        "        output_texts.append(text)\n",
        "    return output_texts\n",
        "\n",
        "# def formatting_prompts_func(examples):\n",
        "#     output_text = []\n",
        "#     for i in range(len(examples[\"instruction\"])):\n",
        "#         instruction = examples[\"instruction\"][i]\n",
        "#         response = examples[\"output\"][i]\n",
        "\n",
        "#         text = f'''Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "#         ### Instruction:\n",
        "#         {instruction}\n",
        "\n",
        "#         ### Response:\n",
        "#         {response}\n",
        "#         '''\n",
        "\n",
        "#         output_text.append(text)\n",
        "#     return output_text\n",
        "\n",
        "max_seq_length = 2048\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "  model=model,\n",
        "  train_dataset=samp_dataset,\n",
        "  peft_config=peft_config,\n",
        "  formatting_func=formatting_prompts_func, # instruction dataset usually contains more than one column (instruction + output)\n",
        "  #dataset_text_field=\"text\",  # for non-instruction dataset\n",
        "  max_seq_length=max_seq_length,\n",
        "  tokenizer=tokenizer,\n",
        "  args=training_arguments,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "b494a83cf61d4786a62be0713b1f6e67",
            "8715c170ed5b4a01b4dc9ef560e7de6b",
            "d4ccca996aad4eeca95bd4f0eafa6df8",
            "b5aaf782cce94ae28df88b3c5ed59865",
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            "692fd1c211e341d7b88eb010d5a69347",
            "39000b54d4d144898d9959a91b2430e2",
            "9a330ab0d774445ea5811ff80b90b573"
          ]
        },
        "id": "GvWjocSPO7M7",
        "outputId": "1d423fea-9ad3-47f3-cd08-9f7e46aa8953"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:107: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b494a83cf61d4786a62be0713b1f6e67"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will also pre-process the model by upcasting the layer norms in float 32 for more stable training"
      ],
      "metadata": {
        "id": "u75QffY2PFU_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        "  if \"norm\" in name:\n",
        "    module = module.to(torch.float32)"
      ],
      "metadata": {
        "id": "xa8fVnvgPF8-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train the model\n",
        "Now let's train the model! Simply call `trainer.train()`"
      ],
      "metadata": {
        "id": "FCZRA6OXPQok"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "d6vNc96tPTPh",
        "outputId": "8e1675b8-238a-4483-e8cb-09805773af99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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              "Run data is saved locally in <code>/content/wandb/run-20231005_010648-1vl8mp82</code>"
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              "Syncing run <strong><a href='https://wandb.ai/leongkwokhing/huggingface/runs/1vl8mp82' target=\"_blank\">rural-blaze-2</a></strong> to <a href='https://wandb.ai/leongkwokhing/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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              " View project at <a href='https://wandb.ai/leongkwokhing/huggingface' target=\"_blank\">https://wandb.ai/leongkwokhing/huggingface</a>"
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              " View run at <a href='https://wandb.ai/leongkwokhing/huggingface/runs/1vl8mp82' target=\"_blank\">https://wandb.ai/leongkwokhing/huggingface/runs/1vl8mp82</a>"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
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            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1000/1000 38:20, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.878100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.884600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.839900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.582700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.763000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>2.468700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.830700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.414700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.566600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.427500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.613100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.836400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.517700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.850200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.599500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>1.965200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>1.711200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.845700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.968100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.653500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>1.614900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>1.794700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>1.846600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.753200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.701600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>1.776500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>2.075600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>2.036600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290</td>\n",
              "      <td>2.013400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.818600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>310</td>\n",
              "      <td>2.127700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>1.808900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>330</td>\n",
              "      <td>1.886100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>1.949700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>2.184900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>2.314300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>370</td>\n",
              "      <td>1.740400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>380</td>\n",
              "      <td>1.909800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>390</td>\n",
              "      <td>1.806100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.777600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>1.727600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>420</td>\n",
              "      <td>1.773700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>430</td>\n",
              "      <td>1.988400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>440</td>\n",
              "      <td>1.923800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>1.823000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>460</td>\n",
              "      <td>2.037600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>470</td>\n",
              "      <td>1.841800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>480</td>\n",
              "      <td>1.884300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>490</td>\n",
              "      <td>2.193300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>510</td>\n",
              "      <td>1.947000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>520</td>\n",
              "      <td>1.737500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>530</td>\n",
              "      <td>1.784300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>540</td>\n",
              "      <td>1.633600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>2.212300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>560</td>\n",
              "      <td>1.853700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>570</td>\n",
              "      <td>1.891900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>580</td>\n",
              "      <td>1.853400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>590</td>\n",
              "      <td>2.121200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.856900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>610</td>\n",
              "      <td>1.613600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>620</td>\n",
              "      <td>2.010400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>630</td>\n",
              "      <td>1.679800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>640</td>\n",
              "      <td>2.035400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>2.128600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>660</td>\n",
              "      <td>1.503500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>670</td>\n",
              "      <td>1.836400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>680</td>\n",
              "      <td>1.974000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>690</td>\n",
              "      <td>1.882900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>2.095900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>710</td>\n",
              "      <td>2.191900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>720</td>\n",
              "      <td>1.953600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>730</td>\n",
              "      <td>2.045000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>740</td>\n",
              "      <td>1.826100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>2.086700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>760</td>\n",
              "      <td>2.109800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>770</td>\n",
              "      <td>2.101200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>780</td>\n",
              "      <td>2.166400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>790</td>\n",
              "      <td>1.915600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>2.083200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>810</td>\n",
              "      <td>1.875500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>820</td>\n",
              "      <td>1.755100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>830</td>\n",
              "      <td>1.668900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>840</td>\n",
              "      <td>1.792800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>2.421500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>860</td>\n",
              "      <td>1.823400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>870</td>\n",
              "      <td>1.970200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>880</td>\n",
              "      <td>1.816500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>890</td>\n",
              "      <td>2.181500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>2.050600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>910</td>\n",
              "      <td>1.951700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>920</td>\n",
              "      <td>2.063100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>930</td>\n",
              "      <td>2.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>940</td>\n",
              "      <td>2.367300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>2.110900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>960</td>\n",
              "      <td>1.981300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>970</td>\n",
              "      <td>1.931400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>980</td>\n",
              "      <td>2.195500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>990</td>\n",
              "      <td>2.121000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>2.338800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1000, training_loss=1.910161961555481, metrics={'train_runtime': 2321.9024, 'train_samples_per_second': 0.431, 'train_steps_per_second': 0.431, 'total_flos': 7181226034752000.0, 'train_loss': 1.910161961555481, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `SFTTrainer` will take care of properly saving only the adapters during training instead of saving the entire model"
      ],
      "metadata": {
        "id": "11O60Zy39pVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model # Take care of distributed/parallel training\n",
        "model_to_save.save_pretrained(\"outputs\")"
      ],
      "metadata": {
        "id": "bYGRiVcVFshA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_config = LoraConfig.from_pretrained('outputs')\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "id": "Z3v8l4k0Z1yo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prompting the fine-tuned model to perform code generation"
      ],
      "metadata": {
        "id": "WbnNCCJr4-it"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "text = '''### User: I need codes for cars detection using deep learning\\n\n",
        "### Assistant: '''\n",
        "device = \"cuda:0\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\", return_token_type_ids=False).to(device)\n",
        "outputs = model.generate(**inputs, max_new_tokens=1024)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dd0dsAqVZ5R3",
        "outputId": "95d65cb0-f812-4928-c047-08abf3d9b9f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### User: I need codes for cars detection using deep learning\n",
            "\n",
            "### Assistant: \n",
            "Here are some codes for cars detection using deep learning:\n",
            "\n",
            "1. MobileNet: \n",
            "\n",
            "```\n",
            "import tensorflow as tf\n",
            "from tensorflow_keras.models import Sequential\n",
            "from tensorflow_keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
            "\n",
            "model = Sequential()\n",
            "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
            "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
            "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
            "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
            "model.add(Flatten())\n",
            "model.add(Dense(128, activation='relu'))\n",
            "model.add(Dense(10, activation='softmax'))\n",
            "\n",
            "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
            "model.fit(x_train, y_train, epochs=10, batch_size=32)\n",
            "```\n",
            "\n",
            "2. VGG16:\n",
            "\n",
            "```\n",
            "import tensorflow as tf\n",
            "from tensorflow_keras.models import Sequential\n",
            "from tensorflow_keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
            "\n",
            "model = Sequential()\n",
            "model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
            "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
            "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
            "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
            "model.add(Flatten())\n",
            "model.add(Dense(100, activation='relu'))\n",
            "model.add(Dense(10, activation='softmax'))\n",
            "\n",
            "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
            "model.fit(x_train, y_train, epochs=10, batch_size=32)\n",
            "```\n",
            "\n",
            "3. ResNet50:\n",
            "\n",
            "```\n",
            "import tensorflow as tf\n",
            "from tensorflow_keras.models import Sequential\n",
            "from tensorflow_keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
            "\n",
            "model = Sequential()\n",
            "model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
            "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
            "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
            "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
            "model.add(Flatten())\n",
            "model.add(Dense(100, activation='relu'))\n",
            "model.add(Dense(10, activation='softmax'))\n",
            "\n",
            "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
            "model.fit(x_train, y_train, epochs=10, batch_size=32)\n",
            "```\n",
            "\n",
            "4. FasterRCNN:\n",
            "\n",
            "```\n",
            "import tensorflow as tf\n",
            "from tensorflow_keras.models import Sequential\n",
            "from tensorflow_keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, InceptionV3\n",
            "\n",
            "model = Sequential()\n",
            "model.add(Conv2D(64, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
            "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
            "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
            "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
            "model.add(Flatten())\n",
            "model.add(Dense(100, activation='\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "text = '''### User: Can you help me with scikit-learn linear regression codes?\\n\n",
        "### Assistant: '''\n",
        "device = \"cuda:0\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\", return_token_type_ids=False).to(device)\n",
        "outputs = model.generate(**inputs, max_new_tokens=1024)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9uHZSQGiBcg",
        "outputId": "f03a2096-5da5-4bd1-8063-c200caa9bdcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### User: Can you help me with scikit-learn linear regression codes?\n",
            "\n",
            "### Assistant: \n",
            "Sure, I can help you with scikit-learn linear regression codes. Here's an example code for a simple linear regression model:\n",
            "\n",
            "```python\n",
            "import pandas as pd\n",
            "from sklearn.linear_model import LinearRegression\n",
            "\n",
            "# Load the data\n",
            "data = pd.read_csv('data.csv')\n",
            "\n",
            "# Split the data into features and target\n",
            "X = data.drop('target', axis=1)\n",
            "y = data['target']\n",
            "\n",
            "# Create a linear regression object\n",
            "model = LinearRegression()\n",
            "\n",
            "# Fit the model to the data\n",
            "model.fit(X, y)\n",
            "\n",
            "# Print the coefficients of the model\n",
            "print(model.coef_)\n",
            "```\n",
            "\n",
            "This code loads a dataset from a CSV file, splits it into features and target, creates a linear regression object, fits the model to the data, and then prints the coefficients of the model. You can modify this code to work with your own data and customize the model to fit your specific needs.\n",
            "CPU times: user 1min 58s, sys: 244 ms, total: 1min 58s\n",
            "Wall time: 2min 4s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "isY2O4dGdrVr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Question 2\n",
        "\n",
        "- Use LangChain to build an ChatGPT-like ChatBot application\n"
      ],
      "metadata": {
        "id": "w5KQgEW3dye4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain chromadb pypdf sentence_transformers InstructorEmbedding streamlit bitsandbytes ctransformers[cuda] accelerate einops safetensors xformers\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git"
      ],
      "metadata": {
        "id": "UiOFNlrBdvl2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d519d300-6c1b-4be0-edeb-0f875249ec59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime>=1.14.1->chromadb)\n",
            "  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n",
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            "Building wheels for collected packages: sentence_transformers, pypika\n",
            "  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence_transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=0dac4c3bee7c4383ba384ed34c32cce070394d23b6dafbd471f3f04adf95e7ce\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built sentence_transformers pypika\n",
            "Installing collected packages: sentencepiece, pypika, monotonic, InstructorEmbedding, bitsandbytes, websockets, watchdog, validators, uvloop, typing-extensions, smmap, safetensors, python-dotenv, pypdf, pulsar-client, overrides, nvidia-cuda-runtime-cu12, nvidia-cublas-cu12, mypy-extensions, marshmallow, jsonpointer, humanfriendly, httptools, h11, einops, chroma-hnswlib, bcrypt, backoff, watchfiles, uvicorn, typing-inspect, starlette, pydeck, posthog, jsonpatch, huggingface-hub, gitdb, coloredlogs, xformers, tokenizers, onnxruntime, langsmith, gitpython, fastapi, dataclasses-json, ctransformers, accelerate, transformers, langchain, chromadb, streamlit, sentence_transformers\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed InstructorEmbedding-1.0.1 accelerate-0.24.0 backoff-2.2.1 bcrypt-4.0.1 bitsandbytes-0.41.1 chroma-hnswlib-0.7.3 chromadb-0.4.14 coloredlogs-15.0.1 ctransformers-0.2.27 dataclasses-json-0.6.1 einops-0.7.0 fastapi-0.104.0 gitdb-4.0.11 gitpython-3.1.40 h11-0.14.0 httptools-0.6.1 huggingface-hub-0.17.3 humanfriendly-10.0 jsonpatch-1.33 jsonpointer-2.4 langchain-0.0.322 langsmith-0.0.51 marshmallow-3.20.1 monotonic-1.6 mypy-extensions-1.0.0 nvidia-cublas-cu12-12.3.2.9 nvidia-cuda-runtime-cu12-12.3.52 onnxruntime-1.16.1 overrides-7.4.0 posthog-3.0.2 pulsar-client-3.3.0 pydeck-0.8.1b0 pypdf-3.16.4 pypika-0.48.9 python-dotenv-1.0.0 safetensors-0.4.0 sentence_transformers-2.2.2 sentencepiece-0.1.99 smmap-5.0.1 starlette-0.27.0 streamlit-1.27.2 tokenizers-0.14.1 transformers-4.34.1 typing-extensions-4.8.0 typing-inspect-0.9.0 uvicorn-0.23.2 uvloop-0.19.0 validators-0.22.0 watchdog-3.0.0 watchfiles-0.21.0 websockets-12.0 xformers-0.0.22.post4\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*** Note:\n",
        "\n",
        "`Restart runtime` if Colab complains about missing packages that was `pip install` above"
      ],
      "metadata": {
        "id": "6fLKjY43ti8n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import"
      ],
      "metadata": {
        "id": "rTPOw66EfmC8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import BitsAndBytesConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
        "\n",
        "from langchain import HuggingFacePipeline\n",
        "from langchain import PromptTemplate, LLMChain\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.document_loaders import TextLoader\n",
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain.document_loaders import DirectoryLoader\n",
        "from langchain.llms import LlamaCpp\n",
        "\n",
        "from InstructorEmbedding import INSTRUCTOR\n",
        "from langchain.embeddings import HuggingFaceInstructEmbeddings"
      ],
      "metadata": {
        "id": "TorKAa-2flR-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define LLM"
      ],
      "metadata": {
        "id": "N6XXGGrgoM5M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import CTransformers\n",
        "\n",
        "# from ctransformers import AutoConfig\n",
        "# from ctransformers import AutoModelForCausalLM\n",
        "\n",
        "# config = AutoConfig.from_pretrained(\"TheBloke/Mistral-7B-v0.1-GGUF\")\n",
        "# config.config.max_new_tokens = 2000\n",
        "# config.config.context_length = 4000\n",
        "\n",
        "# llm = AutoModelForCausalLM.from_pretrained(\"TheBloke/Mistral-7B-v0.1-GGUF\", model_file=\"mistral-7b-v0.1.Q5_K_M.gguf\", model_type=\"mistral\",gpu_layers=0, config=config)\n",
        "\n",
        "config = {'max_new_tokens': 1024, 'temperature': 0, 'context_length': 1024}\n",
        "llm = CTransformers(model='TheBloke/Mistral-7B-Instruct-v0.1-GGUF',model_file=\"mistral-7b-instruct-v0.1.Q4_K_M.gguf\", config=config, n_ctx=4096)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "6288727c74904268af1bef92da29cace",
            "434929aaf03349e8842cf2d5e423f4a8",
            "a493db39060e4e1586eddc47ee90d50f",
            "c357483bc8af477e8cc2fcbd10cdcad8",
            "aef0b29c7d9347b6bf059c1a0265411f",
            "0e1c3ffe897a4019be00b0677320ac6b",
            "cf43d5d5f8d047fa9336769446c3dae9",
            "3ab93fa6c6da442188d81f379ce43ed4",
            "afe89ac68237444281b6b0953baf5d89",
            "40dfb88356784515934cc7d8e9b6666d",
            "40239547e5bc47cfa1c7abae2e769b1c",
            "1baec73ce40e403aa924fccd12670359",
            "2439c4c0a1f840ab8fc0c63423eebd4f",
            "95433ee338dd452d9349d3359e198094",
            "f140004eb8e04dafb3fc0b9e41c44185",
            "4840cb3d04cf4477b699e4e4f125c386",
            "347289bd2cc14591a816bd58424ee0c7",
            "e7a9857875254c31a7227326650ed429",
            "d734684fd7624f5aa84de21deab2d00e",
            "ccab0ab463564700aac4653c686646c4",
            "3b1c76d214a94459811fac16157b9832",
            "0fd0915b3d2146909db4949ed5c562e6"
          ]
        },
        "id": "cGx21GVRtU_i",
        "outputId": "08ddf66d-8695-4c83-abd5-a4e8929b8617"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 1 files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6288727c74904268af1bef92da29cace"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 1 files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1baec73ce40e403aa924fccd12670359"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading PDF documents"
      ],
      "metadata": {
        "id": "3W-Qtb0VJgsx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and process the text files\n",
        "# loader = TextLoader('single_text_file.txt')\n",
        "loader = DirectoryLoader('/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data', glob=\"./*.pdf\", loader_cls=PyPDFLoader)\n",
        "\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "R8Qb7dDVYXDd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting the text into\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=300)\n",
        "texts = text_splitter.split_documents(documents)"
      ],
      "metadata": {
        "id": "gVKN20h-Ykrp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(texts)"
      ],
      "metadata": {
        "id": "jXbYijtdYxRC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c06ea1c-1123-44ec-85ab-3c7eb18cc172"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "instructor_embeddings = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-large\",\n",
        "                                                      model_kwargs={'device': 'cuda:0'})"
      ],
      "metadata": {
        "id": "8W0Wq1IJYxUw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9806dac8-f325-4b3e-eb6a-07e23caaed1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "load INSTRUCTOR_Transformer\n",
            "max_seq_length  512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating DB"
      ],
      "metadata": {
        "id": "xGUQ7TgoLCC2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embed and store the texts\n",
        "# Supplying a persist_directory will store the embeddings on disk\n",
        "persist_directory = 'db'\n",
        "\n",
        "## Here is the nmew embeddings being used\n",
        "embedding = instructor_embeddings\n",
        "\n",
        "vectordb = Chroma.from_documents(documents=texts,\n",
        "                                 embedding=embedding,\n",
        "                                 persist_directory=persist_directory)\n",
        "\n",
        "# persiste the db to disk\n",
        "vectordb.persist()\n",
        "vectordb = None"
      ],
      "metadata": {
        "id": "YV40q6MvK-4z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we can load the persisted database from disk, and use it as normal.\n",
        "vectordb = Chroma(persist_directory=persist_directory,\n",
        "                  embedding_function=embedding)"
      ],
      "metadata": {
        "id": "PysfajssZRlC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Make a retriever"
      ],
      "metadata": {
        "id": "X5u6jP3kPXp2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vectordb.as_retriever(search_kwargs={\"k\": 3})"
      ],
      "metadata": {
        "id": "rZt7JcI6PX9M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Make a chain"
      ],
      "metadata": {
        "id": "hlRpErxBaeoq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create the chain to answer questions\n",
        "qa_chain = RetrievalQA.from_chain_type(llm=llm,\n",
        "                                  chain_type=\"stuff\",\n",
        "                                  retriever=retriever,\n",
        "                                  return_source_documents=True)"
      ],
      "metadata": {
        "id": "SkR4-DxLagoU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Cite sources\n",
        "\n",
        "import textwrap\n",
        "\n",
        "def wrap_text_preserve_newlines(text, width=110):\n",
        "    # Split the input text into lines based on newline characters\n",
        "    lines = text.split('\\n')\n",
        "\n",
        "    # Wrap each line individually\n",
        "    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]\n",
        "\n",
        "    # Join the wrapped lines back together using newline characters\n",
        "    wrapped_text = '\\n'.join(wrapped_lines)\n",
        "\n",
        "    return wrapped_text\n",
        "\n",
        "def process_llm_response(llm_response):\n",
        "    print(wrap_text_preserve_newlines(llm_response['result']))\n",
        "    print('\\n\\nSources:')\n",
        "    for source in llm_response[\"source_documents\"]:\n",
        "        print(source.metadata['source'])"
      ],
      "metadata": {
        "id": "Kxc-hgB7amfr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# example\n",
        "query = \"How many players can battleline accomodate?\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ],
      "metadata": {
        "id": "LrUMtd7narSx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3655b362-ca65-4f9e-f7e2-27f232a17712"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The Battle Line game can accommodate two players.\n",
            "\n",
            "\n",
            "Sources:\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# example\n",
        "query = \"How to win the game?\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJ2Vz2AtupK3",
        "outputId": "b7ec1bec-c025-4dd6-ed01-5ea1126e9a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " To win the game, a player must either claim three adjacent Flags or any five Flags. The game ends immediately\n",
            "when this happens, and the player who achieved the victory scores five points while the loser gets as many\n",
            "points as the number of Flags he has claimed. If several games are played, the winner always scores five\n",
            "points, and the loser gets as many points as the number of Flags he has claimed. The game can be played with\n",
            "advanced rules, where players can use\n",
            "\n",
            "\n",
            "Sources:\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "CPU times: user 7min 3s, sys: 1.08 s, total: 7min 4s\n",
            "Wall time: 4min 46s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# example\n",
        "query = \"Who designed battleline game\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ebjHIh0FxZFr",
        "outputId": "2c5ba54f-1c2b-43f6-8bbe-b66ccbd11546"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Reiner Knizia\n",
            "\n",
            "\n",
            "Sources:\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "CPU times: user 6min 3s, sys: 885 ms, total: 6min 4s\n",
            "Wall time: 4min 3s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# example\n",
        "query = \"Can you explain how to play Battleline?\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FezFV2n52Uy",
        "outputId": "f2900df9-cf8b-422b-aee5-b6a4daf395fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Sure! Battleline is a card game where two players face off against each other in an ancient battle. Each\n",
            "player starts with 60 Troop cards and 60 Tactics cards, which are divided into six different colors. The goal\n",
            "of the game is to win by having more Flags on your side of the battle line at the end of the game.\n",
            "\n",
            "Players take turns selecting either one Troop card or one Tactics card from their hand and placing it face\n",
            "\n",
            "\n",
            "Sources:\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "CPU times: user 7min 10s, sys: 1.12 s, total: 7min 11s\n",
            "Wall time: 4min 41s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# example\n",
        "query = \"Can you explain how to play Battleline?\"\n",
        "llm_response = qa_chain(query)\n",
        "process_llm_response(llm_response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKfVEcfy6ECa",
        "outputId": "2bdba122-7963-4db5-b377-3acbd4105150"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Battleline is a two-player strategy game where players aim to create powerful formations on their side of the\n",
            "battle line to beat the formations on the opponent's side of the respective Flags. The first player to win\n",
            "three adjacent Flags (a Breakthrough) or any five Flags (an Envelopment) achieves victory. Players take turns\n",
            "selecting either one Troop card or one Tactics card from their hand and placing it face up on their side of\n",
            "the battle line. At the end of their turn, they draw one card to refresh their hand to seven. The Flags are\n",
            "won by formations of Troop cards played adjacent to the Flags. Tactics cards can be used to influence the\n",
            "formations.\n",
            "\n",
            "\n",
            "Sources:\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "/content/drive/MyDrive/colab/AI Planet LLM Bootcamp/data/BattleLineRules.pdf\n",
            "CPU times: user 18min 52s, sys: 11min 23s, total: 30min 16s\n",
            "Wall time: 1h 3min 59s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iPyXboF1aixT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}