llama2_fine_tuning.ipynb

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            "Collecting huggingface-hub\n",
            "  Downloading huggingface_hub-0.17.2-py3-none-any.whl (294 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/294.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m174.1/294.9 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (3.12.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (4.66.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (23.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (2023.7.22)\n",
            "Installing collected packages: huggingface-hub\n",
            "Successfully installed huggingface-hub-0.17.2\n"
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            "Collecting transformers\n",
            "  Downloading transformers-4.33.2-py3-none-any.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m88.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "Installing collected packages: tokenizers, safetensors, transformers\n",
            "Successfully installed safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MASE6htUaYp_",
        "outputId": "db9677d7-6075-409a-bfec-f76b12fb05da"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.33.2)\n",
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            "Collecting sentencepiece (from sentence-transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.17.2)\n",
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            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (2023.6.3)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers) (0.13.3)\n",
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            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (1.3.2)\n",
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            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence-transformers) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->sentence-transformers) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence-transformers\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=3dced46a3d5443b8ac150d3d266977c3d47c777737ec370500d44d68b3082ef0\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2 sentencepiece-0.1.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97n8T16QaV2r",
        "outputId": "be9849f4-5d59-4aaf-bdf5-104f5bdbba0e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.23.0-py3-none-any.whl (258 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/258.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/258.1 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.1/258.1 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.12.2)\n",
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            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.10.0->accelerate) (3.27.4.1)\n",
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            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.23.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bitsandbytes-cuda110 bitsandbytes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rgcl0xbWaeoV",
        "outputId": "62208d34-63c6-4554-b9cc-965fdc2ef7f2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting bitsandbytes-cuda110\n",
            "  Downloading bitsandbytes_cuda110-0.26.0.post2-py3-none-any.whl (3.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.41.1-py3-none-any.whl (92.6 MB)\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",
            "\u001b[?25hInstalling collected packages: bitsandbytes-cuda110, bitsandbytes\n",
            "Successfully installed bitsandbytes-0.41.1 bitsandbytes-cuda110-0.26.0.post2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JQKpB2zan1d",
        "outputId": "23ad7306-c233-4511-9204-3e202cc90047"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain\n",
            "  Downloading langchain-0.0.299-py3-none-any.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.1)\n",
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            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain)\n",
            "  Downloading dataclasses_json-0.6.0-py3-none-any.whl (27 kB)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Collecting langsmith<0.1.0,>=0.0.38 (from langchain)\n",
            "  Downloading langsmith-0.0.40-py3-none-any.whl (39 kB)\n",
            "Requirement already satisfied: numexpr<3.0.0,>=2.8.4 in /usr/local/lib/python3.10/dist-packages (from langchain) (2.8.5)\n",
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            "Installing collected packages: mypy-extensions, marshmallow, jsonpointer, typing-inspect, langsmith, jsonpatch, dataclasses-json, langchain\n",
            "Successfully installed dataclasses-json-0.6.0 jsonpatch-1.33 jsonpointer-2.4 langchain-0.0.299 langsmith-0.0.40 marshmallow-3.20.1 mypy-extensions-1.0.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vtVq6Ebarba",
        "outputId": "36773a75-80b7-4735-f7ef-a25a96505bfe"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "\u001b[?25hCollecting multiprocess (from datasets)\n",
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            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 multiprocess-0.70.15 xxhash-3.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install faiss-cpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VptYaznzauux",
        "outputId": "57ad2ce1-afd8-4b85-ea53-b101fc0294b7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting faiss-cpu\n",
            "  Downloading faiss_cpu-1.7.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.6/17.6 MB\u001b[0m \u001b[31m56.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: faiss-cpu\n",
            "Successfully installed faiss-cpu-1.7.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Llama 2 model from Huggingface"
      ],
      "metadata": {
        "id": "pQKEjd1dZyMi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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          ]
        },
        "id": "Mkrf9wgzZqB2",
        "outputId": "913aed0c-fd9f-425a-f013-f529db2c0afc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd41bcb28a1c45e8a817f119f2d0c3f2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import cuda, bfloat16\n",
        "import transformers\n",
        "\n",
        "model_id = 'meta-llama/Llama-2-13b-chat-hf'\n",
        "# 4-bit Quanityzation to load Llama 2 with less GPU memory\n",
        "bnb_config = transformers.BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=bfloat16\n",
        ")\n",
        "\n",
        "# Llama 2 Tokenizer\n",
        "tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "# Llama 2 Model\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    trust_remote_code=True,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map='auto',\n",
        ")\n",
        "model.eval()\n",
        "\n",
        "# Our text generator\n",
        "generator = transformers.pipeline(\n",
        "    model=model, tokenizer=tokenizer,\n",
        "    task='text-generation',\n",
        "    temperature=0.1,\n",
        "    max_new_tokens=500,\n",
        "    repetition_penalty=1.1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401,
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        "outputId": "390cfb45-c5f6-4417-8faa-dadc676cc4b4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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        {
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            ],
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prompt Engineering**"
      ],
      "metadata": {
        "id": "qC7sY8Yrc-Tl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Zero shot prompt"
      ],
      "metadata": {
        "id": "cPSjaLQJdBuo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "basic_prompt = \"\"\"\n",
        "<s>[INST] <<SYS>>\n",
        "\n",
        "You are a helpful assistant\n",
        "\n",
        "<</SYS>>\n",
        "\n",
        "What is 1 + 1? [/INST]\n",
        "\"\"\"\n",
        "print(generator(basic_prompt)[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdrG2uzAcLEg",
        "outputId": "6e43ca6e-2d45-4afc-f2b3-aeb33261b6b3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "<s>[INST] <<SYS>>\n",
            "\n",
            "You are a helpful assistant\n",
            "\n",
            "<</SYS>>\n",
            "\n",
            "What is 1 + 1? [/INST]\n",
            "\n",
            "Oh my, that's a simple one! The answer to 1 + 1 is... (drumroll please)... 2! 😊\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"\n",
        "<s>[INST] <<SYS>>\n",
        "\n",
        "You are a helpful assistant.\n",
        "\n",
        "<</SYS>>\n",
        "\n",
        "Classify the text into neutral, negative or positive.\n",
        "Text: I think the food was okay. [/INST]\n",
        "\"\"\"\n",
        "print(generator(prompt)[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3s9qSnfucPVS",
        "outputId": "3910e677-fec2-4e8a-9b2f-06bdf95d63a1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "<s>[INST] <<SYS>>\n",
            "\n",
            "You are a helpful assistant.\n",
            "\n",
            "<</SYS>>\n",
            "\n",
            "Classify the text into neutral, negative or positive.\n",
            "Text: I think the food was okay. [/INST]\n",
            "\n",
            "Neutral. The word \"okay\" is a neutral term and does not convey a particularly positive or negative sentiment.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One shot prompt"
      ],
      "metadata": {
        "id": "SU5lghKNdHNr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"\n",
        "<s>[INST] <<SYS>>\n",
        "\n",
        "You are a helpful assistant.\n",
        "\n",
        "<</SYS>>\n",
        "\n",
        "Classify the text into neutral, negative or positive.\n",
        "Text: I think the food was alright.\n",
        "Sentiment:\n",
        "[/INST]\n",
        "\n",
        "Neutral</s><s>\n",
        "\n",
        "[INST]\n",
        "Classify the text into neutral, negative or positive.\n",
        "Text: I think the food was okay.\n",
        "Sentiment:\n",
        "[/INST]\n",
        "\"\"\"\n",
        "print(generator(prompt)[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYZ2k4yFcS8p",
        "outputId": "3ee85437-2108-4a32-e605-7ca6d1a0f50a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "<s>[INST] <<SYS>>\n",
            "\n",
            "You are a helpful assistant.\n",
            "\n",
            "<</SYS>>\n",
            "\n",
            "Classify the text into neutral, negative or positive.\n",
            "Text: I think the food was alright.\n",
            "Sentiment:\n",
            "[/INST]\n",
            "\n",
            "Neutral</s><s>\n",
            "\n",
            "[INST]\n",
            "Classify the text into neutral, negative or positive.\n",
            "Text: I think the food was okay.\n",
            "Sentiment:\n",
            "[/INST]\n",
            "\n",
            "Neutral\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chain of thought"
      ],
      "metadata": {
        "id": "BOO_X3xSdJ4V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"\n",
        "<s>[INST] <<SYS>>\n",
        "\n",
        "You are a helpful assistant.\n",
        "\n",
        "<</SYS>>\n",
        "\n",
        "Do the odd numbers in this group add up to an even number? 3, 5, 15, 32.\n",
        "Solve by breaking the problem into steps.\n",
        "Identify the odd numbers, add them, and indicate whether the result is odd or even.[/INST]\n",
        "\"\"\"\n",
        "print(generator(prompt)[0][\"generated_text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Dwi413UcgP6",
        "outputId": "8a88fa84-e6ca-464f-a17f-6e7793399b9f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "<s>[INST] <<SYS>>\n",
            "\n",
            "You are a helpful assistant.\n",
            "\n",
            "<</SYS>>\n",
            "\n",
            "Do the odd numbers in this group add up to an even number? 3, 5, 15, 32.\n",
            "Solve by breaking the problem into steps.\n",
            "Identify the odd numbers, add them, and indicate whether the result is odd or even.[/INST]\n",
            "\n",
            "Hello! I'd be happy to help you with that. Let's break down the problem into steps:\n",
            "\n",
            "Step 1: Identify the odd numbers in the group.\n",
            "\n",
            "The odd numbers in the group are:\n",
            "\n",
            "3, 5, 15\n",
            "\n",
            "Step 2: Add the odd numbers together.\n",
            "\n",
            "3 + 5 + 15 = 23\n",
            "\n",
            "Step 3: Determine if the result is odd or even.\n",
            "\n",
            "23 is an odd number, so the sum of the odd numbers in the group is odd.\n",
            "\n",
            "Therefore, the odd numbers in the group add up to an odd number.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Retrieval Augmented Generation (RAG)**"
      ],
      "metadata": {
        "id": "TkEQ85KvctKf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Our knowledge base\n",
        "knowledge_base = [\n",
        "    \"On July 18, 2023, in partnership with Microsoft, Meta announced LLaMA-2, the next generation of LLaMA.\" ,\n",
        "    \"Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) \",\n",
        "    \"The fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases.\",\n",
        "    \"Meta trained and released LLaMA-2 in three model sizes: 7, 13, and 70 billion parameters.\",\n",
        "    \"The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models.\",\n",
        "    \"The accompanying preprint also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets.\"\n",
        "]\n",
        "with open(r'knowledge_base.txt', 'w') as fp:\n",
        "    fp.write('\\n'.join(knowledge_base))"
      ],
      "metadata": {
        "id": "qUzfgeiIcyu1"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "# Embedding Model for converting text to numerical representations\n",
        "embedding_model = HuggingFaceEmbeddings(\n",
        "    model_name='sentence-transformers/all-MiniLM-L6-v2'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
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        "outputId": "b0284d32-59c0-41c7-d4ef-aa7fc923962c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create word embedding and store it in a local vector db"
      ],
      "metadata": {
        "id": "A3hTw4UvddHZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.document_loaders import TextLoader\n",
        "\n",
        "# Load documents and split them\n",
        "documents = TextLoader(\"knowledge_base.txt\").load()\n",
        "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
        "docs = text_splitter.split_documents(documents)\n",
        "\n",
        "# Create local vector database\n",
        "db = FAISS.from_documents(docs, embedding_model)"
      ],
      "metadata": {
        "id": "SrNNmUYWdW_w"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create RAG pipeline"
      ],
      "metadata": {
        "id": "TKPv028OdcF6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.chains import RetrievalQA\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "\n",
        "# Load LLM into LangChain\n",
        "llm = HuggingFacePipeline(pipeline=generator)\n",
        "\n",
        "# RAG Pipeline\n",
        "rag = RetrievalQA.from_chain_type(\n",
        "    llm=llm, chain_type='stuff',\n",
        "    retriever=db.as_retriever()\n",
        ")"
      ],
      "metadata": {
        "id": "P_En8g4Kdaqo"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Results without RAG"
      ],
      "metadata": {
        "id": "3t8DHc7ne4KG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "llm('What is Llama 2?')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "E9YAwPaue3So",
        "outputId": "8fc422d5-e3eb-4717-e3c6-eb41d171b2ba"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n\\nLlama 2 is a cryptocurrency that was created as a parody of the popular cryptocurrency Dogecoin. It was designed to be a more serious and less meme-focused alternative to Dogecoin, with a focus on privacy and security.\\n\\nLlama 2 uses a proof-of-work consensus algorithm and has a block time of 1 minute. It also features a decentralized governance system that allows holders of the llama token to vote on proposals for the development and growth of the network.\\n\\nOne of the key features of Llama 2 is its use of \"llama-proof\" transactions, which are designed to be more secure and private than traditional cryptocurrency transactions. These transactions use a combination of zero-knowledge proofs and homomorphic encryption to allow users to prove ownership of assets without revealing any sensitive information about themselves or their transactions.\\n\\nOverall, Llama 2 is a unique and innovative cryptocurrency that is focused on privacy, security, and decentralized governance. While it may not have the same level of meme appeal as Dogecoin, it has the potential to offer a more serious and reliable alternative for those looking for a privacy-focused cryptocurrency.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Results with RAG"
      ],
      "metadata": {
        "id": "YjT8m4PofTPs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rag('What is Llama 2?')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKtUvfK-fUpq",
        "outputId": "5a7a1db7-f1ec-4674-8dd5-c26ccf25ff41"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'query': 'What is Llama 2?',\n",
              " 'result': ' Llama 2 is a collection of pretrained and fine-tuned large language models (LLMs) announced by Meta on July 18, 2023, in partnership with Microsoft.'}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ecLeH0eZfbJo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}