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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SiriusArtLtd/DocX/blob/master/rag_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Improving Fine-tuned Model using RAG\n",
        "\n",
        "Code authored by: Shaw Talebi <br>\n",
        "Article link: https://towardsdatascience.com/how-to-improve-llms-with-rag-abdc132f76ac <br>\n",
        "Video link: coming soon! <br>\n",
        "<br>\n",
        "Colab: https://colab.research.google.com/drive/1peJukr-9E1zCo1iAalbgDPJmNMydvQms?usp=sharing"
      ],
      "metadata": {
        "id": "WWiBVBuOUm-G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### imports"
      ],
      "metadata": {
        "id": "imVTIbGDUsRt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install llama-index\n",
        "!pip install llama-index-embeddings-huggingface\n",
        "!pip install peft\n",
        "!pip install auto-gptq\n",
        "!pip install optimum\n",
        "!pip install bitsandbytes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFwm7wDSoF3V",
        "outputId": "991ac34d-4f12-440b-968a-a2e23df797ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting llama-index-program-openai<0.2.0,>=0.1.3 (from llama-index)\n",
            "  Downloading llama_index_program_openai-0.1.4-py3-none-any.whl (4.1 kB)\n",
            "Collecting llama-index-question-gen-openai<0.2.0,>=0.1.2 (from llama-index)\n",
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            "  Downloading dataclasses_json-0.6.4-py3-none-any.whl (28 kB)\n",
            "Collecting deprecated>=1.2.9.3 (from llama-index-core<0.11.0,>=0.10.18->llama-index)\n",
            "  Downloading Deprecated-1.2.14-py2.py3-none-any.whl (9.6 kB)\n",
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            "Building wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53723 sha256=002753922b131e74cca65798bdb9d78f1e6900b254a48c12d5262127ade8e541\n",
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            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, mmh3, dirtyjson, websockets, uvloop, python-dotenv, pypdf, PyMuPDFb, pulsar-client, overrides, orjson, opentelemetry-util-http, opentelemetry-semantic-conventions, opentelemetry-proto, mypy-extensions, marshmallow, importlib-metadata, humanfriendly, httptools, h11, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, typing-inspect, tiktoken, starlette, pymupdf, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, httpcore, coloredlogs, bs4, opentelemetry-sdk, opentelemetry-instrumentation, onnxruntime, kubernetes, httpx, fastapi, dataclasses-json, opentelemetry-instrumentation-asgi, opentelemetry-exporter-otlp-proto-grpc, openai, llamaindex-py-client, opentelemetry-instrumentation-fastapi, llama-index-legacy, llama-index-core, llama-parse, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, chromadb, llama-index-vector-stores-chroma, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-agent-openai, llama-index-program-openai, llama-index-cli, llama-index-question-gen-openai, llama-index\n",
            "  Attempting uninstall: importlib-metadata\n",
            "    Found existing installation: importlib-metadata 7.0.1\n",
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            "Successfully installed PyMuPDFb-1.23.22 asgiref-3.7.2 backoff-2.2.1 bcrypt-4.1.2 bs4-0.0.2 chroma-hnswlib-0.7.3 chromadb-0.4.24 coloredlogs-15.0.1 dataclasses-json-0.6.4 deprecated-1.2.14 dirtyjson-1.0.8 fastapi-0.110.0 h11-0.14.0 httpcore-1.0.4 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 importlib-metadata-6.11.0 kubernetes-29.0.0 llama-index-0.10.18 llama-index-agent-openai-0.1.5 llama-index-cli-0.1.8 llama-index-core-0.10.18.post1 llama-index-embeddings-openai-0.1.6 llama-index-indices-managed-llama-cloud-0.1.3 llama-index-legacy-0.9.48 llama-index-llms-openai-0.1.7 llama-index-multi-modal-llms-openai-0.1.4 llama-index-program-openai-0.1.4 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.8 llama-index-readers-llama-parse-0.1.3 llama-index-vector-stores-chroma-0.1.6 llama-parse-0.3.8 llamaindex-py-client-0.1.13 marshmallow-3.21.1 mmh3-4.1.0 monotonic-1.6 mypy-extensions-1.0.0 onnxruntime-1.17.1 openai-1.13.3 opentelemetry-api-1.23.0 opentelemetry-exporter-otlp-proto-common-1.23.0 opentelemetry-exporter-otlp-proto-grpc-1.23.0 opentelemetry-instrumentation-0.44b0 opentelemetry-instrumentation-asgi-0.44b0 opentelemetry-instrumentation-fastapi-0.44b0 opentelemetry-proto-1.23.0 opentelemetry-sdk-1.23.0 opentelemetry-semantic-conventions-0.44b0 opentelemetry-util-http-0.44b0 orjson-3.9.15 overrides-7.7.0 posthog-3.5.0 pulsar-client-3.4.0 pymupdf-1.23.26 pypdf-4.1.0 pypika-0.48.9 python-dotenv-1.0.1 starlette-0.36.3 tiktoken-0.6.0 typing-inspect-0.9.0 uvicorn-0.27.1 uvloop-0.19.0 watchfiles-0.21.0 websockets-12.0\n",
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            "  Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface)\n",
            "  Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nccl-cu12==2.19.3 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface)\n",
            "  Downloading nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface)\n",
            "  Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting triton==2.2.0 (from torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface)\n",
            "  Downloading triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m167.9/167.9 MB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m80.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.37.0->llama-index-embeddings-huggingface) (2023.12.25)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.37.0->llama-index-embeddings-huggingface) (0.15.2)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.37.0->llama-index-embeddings-huggingface) (0.4.2)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (4.0.3)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated>=1.2.9.3->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (1.14.1)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (3.7.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (2024.2.2)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (1.0.4)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (3.6)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (1.3.1)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (0.14.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk<4.0.0,>=3.8.1->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (8.1.7)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk<4.0.0,>=3.8.1->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (1.3.2)\n",
            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai>=1.1.0->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (1.7.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0,>1.1->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic<3.0,>1.1->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (2.16.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (3.3.2)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub[inference]>=0.19.0->llama-index-embeddings-huggingface) (2.0.7)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy[asyncio]>=1.4.49->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (3.0.3)\n",
            "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect>=0.8.0->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (1.0.0)\n",
            "Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (3.21.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface) (2.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (2023.4)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch<3.0.0,>=2.1.2->llama-index-embeddings-huggingface) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (1.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->llama-index-core<0.11.0,>=0.10.1->llama-index-embeddings-huggingface) (1.16.0)\n",
            "Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, llama-index-embeddings-huggingface\n",
            "  Attempting uninstall: triton\n",
            "    Found existing installation: triton 2.1.0\n",
            "    Uninstalling triton-2.1.0:\n",
            "      Successfully uninstalled triton-2.1.0\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.1.0+cu121\n",
            "    Uninstalling torch-2.1.0+cu121:\n",
            "      Successfully uninstalled torch-2.1.0+cu121\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.1.0+cu121 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n",
            "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n",
            "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\n",
            "torchvision 0.16.0+cu121 requires torch==2.1.0, but you have torch 2.2.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed llama-index-embeddings-huggingface-0.1.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 torch-2.2.1 triton-2.2.0\n",
            "Collecting peft\n",
            "  Downloading peft-0.9.0-py3-none-any.whl (190 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.9/190.9 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft) (2.2.1)\n",
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.66.2)\n",
            "Collecting accelerate>=0.21.0 (from peft)\n",
            "  Downloading accelerate-0.27.2-py3-none-any.whl (279 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m22.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft) (0.4.2)\n",
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            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->peft) (2023.6.0)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->peft) (4.10.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1.3)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.13.0->peft) (12.4.99)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2023.12.25)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (0.15.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n",
            "Installing collected packages: accelerate, peft\n",
            "Successfully installed accelerate-0.27.2 peft-0.9.0\n",
            "Collecting auto-gptq\n",
            "  Downloading auto_gptq-0.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (23.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.5/23.5 MB\u001b[0m \u001b[31m56.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: accelerate>=0.26.0 in /usr/local/lib/python3.10/dist-packages (from auto-gptq) (0.27.2)\n",
            "Collecting datasets (from auto-gptq)\n",
            "  Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
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            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets->auto-gptq)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "Installing collected packages: rouge, gekko, dill, multiprocess, datasets, auto-gptq\n",
            "Successfully installed auto-gptq-0.7.1 datasets-2.18.0 dill-0.3.8 gekko-1.0.7 multiprocess-0.70.16 rouge-1.0.1\n",
            "Collecting optimum\n",
            "  Downloading optimum-1.17.1-py3-none-any.whl (407 kB)\n",
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            "Installing collected packages: optimum\n",
            "Successfully installed optimum-1.17.1\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.43.0-py3-none-manylinux_2_24_x86_64.whl (102.2 MB)\n",
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            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->bitsandbytes) (12.4.99)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->bitsandbytes) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->bitsandbytes) (1.3.0)\n",
            "Installing collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.43.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
        "from llama_index.core import Settings, SimpleDirectoryReader, VectorStoreIndex\n",
        "from llama_index.core.retrievers import VectorIndexRetriever\n",
        "from llama_index.core.query_engine import RetrieverQueryEngine\n",
        "from llama_index.core.postprocessor import SimilarityPostprocessor"
      ],
      "metadata": {
        "id": "QBrjtJTQUp-u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Define Settings"
      ],
      "metadata": {
        "id": "gm2fzGngqRlW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import any embedding model on HF hub (https://huggingface.co/spaces/mteb/leaderboard)\n",
        "Settings.embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
        "# Settings.embed_model = HuggingFaceEmbedding(model_name=\"thenlper/gte-large\") # alternative model\n",
        "\n",
        "Settings.llm = None\n",
        "Settings.chunk_size = 256\n",
        "Settings.chunk_overlap = 25"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351,
          "referenced_widgets": [
            "82620098f9cd4a1abccc77c52e18982b",
            "37c1be672cd24b8c8f7d6209f988e69f",
            "4398e1c067b84bde99211306d5edd6ff",
            "a56d59f0457e45d8a98c395b5aabd8f2",
            "861a503041014d97bf322eae9cad1cbe",
            "4e89cf887bea457d9103672cf88bf0dc",
            "4e296a91f9b54d9bb813e7addc6d2c90",
            "ac2746719ccb41188976dc3bb3a42350",
            "d6f4dacdcab14b9eb89426a75b460d7b",
            "0fba33cb62204a909446e50d427cee80",
            "13f0db13dc644bc7bedb251e3fa53830",
            "843006d75c054d2b8e74bd14e6efde20",
            "53cebdfb801340228709b61de207d273",
            "24ce2fc2c92e404fbc38dfddbc8498db",
            "34fd61b3be674f08aa35af4cead7eaaf",
            "fe598449449e4d5eade4a9487651d52d",
            "60a14f019f77423fb085400bb4d51e36",
            "405c08ec6cfa4d8bbf58be3d7be80e24",
            "f6ec81bfb76f474999f92d93be176fa2",
            "25d372eb0ab34689848c704895f138f7",
            "f82ed155df734f16b76f37769e72f1d6",
            "dcf15d6e08e94810b92cddd6461c93e6",
            "4091e7fd93e44297bdae41cfe115ecfd",
            "9a3e527d99d74adfb74066dcaf8b46f4",
            "bd1e61e67eff4072a9ad9dcd8a3687ca",
            "38ac6fd24d5a449c9223b003e5e7cbf0",
            "813c5afcd5f741a398bba20ec58f7ea8",
            "4207cc261cef442597b3739e8b4631ac",
            "2cd4a33cea8f49f6a4a6af665b435650",
            "31ca63e289b94820a971adc6c34bcf36",
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            "3f78b01c526b4860b36eaaf49c5b8392",
            "f39832a0067b4de2bd093de5b25292c6",
            "f692e70a0f1240e89709214406476975",
            "653fb93920064a0a8fb578d30f08f5c4",
            "5522eb38a973474cb14f27170c62ab95",
            "435006ced92148c39c0ec1e02b10a2c1",
            "b120638c81fd49be94976b8fe6bc7ecc",
            "a19a18b4afe5485ea325133fab080999",
            "baac51d3ed414acd9fb44b2782a32808",
            "fce0367449544a3f851aca1d2ea1b56e",
            "1aea0ffcfb6b4d369946f405b794d2da",
            "de9f992fdd824ed5978006293b6c1cf1",
            "2b8c3974c321467293af65fc704c42ac",
            "790d261faf2d447f8b45ce1c274151a2",
            "827a9ec816d7461f84635fd7e51aaa0b",
            "d927d9bfb88e4530a496fde529592ef0",
            "419bb76545a646cdb292bf3e14fff8e4",
            "3ef05f6a52ed4755936b94979814e0cc",
            "b880a1ea405449acbd63ec497ef74315",
            "045ca33e719448c9a4c365586924d72c",
            "17df5e2958a7488b85334f09dc3eddc5",
            "68eed345d6544395a1e7995205d88402",
            "ef5091296b814751b15f18d308e8cb24",
            "6c8d21aacfe2496b9862dba85ed053c2",
            "4babb1438fd348b2b3052d59a60e8ed7",
            "b0a258fb6acb4aca8c11fd2d8b8a75f3",
            "20ff6075faa549f9bd954e6691a87ac6",
            "f132885ba3634b8d9a34cbda8503a94d",
            "8bf03ffd26b740b7a635fb0c5d905d56",
            "77245bc643c14ddfabe47901ebaca21f",
            "99e10e2453364871815ac77e6a9e1de0",
            "79c997c196454342a322e96af72c38a3",
            "d4ddc1ce1dca4ab7baecee8282a51642",
            "08fbef47cdc2426d97de406844659fde"
          ]
        },
        "id": "lBuIj8Xzqb6A",
        "outputId": "11fdb088-bb1a-4536-a222-e8269ff9f8af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/743 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "82620098f9cd4a1abccc77c52e18982b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/133M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "843006d75c054d2b8e74bd14e6efde20"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/366 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4091e7fd93e44297bdae41cfe115ecfd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f39832a0067b4de2bd093de5b25292c6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b8c3974c321467293af65fc704c42ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c8d21aacfe2496b9862dba85ed053c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LLM is explicitly disabled. Using MockLLM.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Read and Store Docs into Vector DB"
      ],
      "metadata": {
        "id": "eGXecXc5tPKL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# articles available here: {add GitHub repo}\n",
        "documents = SimpleDirectoryReader(\"articles\").load_data()"
      ],
      "metadata": {
        "id": "_1pAvTwntWD0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# some ad hoc document refinement\n",
        "print(len(documents))\n",
        "for doc in documents:\n",
        "    if \"Member-only story\" in doc.text:\n",
        "        documents.remove(doc)\n",
        "        continue\n",
        "\n",
        "    if \"The Data Entrepreneurs\" in doc.text:\n",
        "        documents.remove(doc)\n",
        "\n",
        "    if \" min read\" in doc.text:\n",
        "        documents.remove(doc)\n",
        "\n",
        "print(len(documents))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7mAAYYvtYLE",
        "outputId": "3851bbc1-bd23-4a53-a400-d438784e94f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "71\n",
            "61\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# store docs into vector DB\n",
        "index = VectorStoreIndex.from_documents(documents)"
      ],
      "metadata": {
        "id": "WqiIJUcDtce_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set Up Search Function"
      ],
      "metadata": {
        "id": "vETeL8CZt_Bl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set number of docs to retreive\n",
        "top_k = 3\n",
        "\n",
        "# configure retriever\n",
        "retriever = VectorIndexRetriever(\n",
        "    index=index,\n",
        "    similarity_top_k=top_k,\n",
        ")"
      ],
      "metadata": {
        "id": "jERXCirtuIKp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# assemble query engine\n",
        "query_engine = RetrieverQueryEngine(\n",
        "    retriever=retriever,\n",
        "    node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=0.5)],\n",
        ")"
      ],
      "metadata": {
        "id": "Rp0PNtjHuJ39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Retrieve Relevant Docs"
      ],
      "metadata": {
        "id": "ciyNcoKbuS90"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# query documents\n",
        "query = \"What is fat-tailedness?\"\n",
        "response = query_engine.query(query)"
      ],
      "metadata": {
        "id": "VoXW6EovuO64"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reformat response\n",
        "context = \"Context:\\n\"\n",
        "for i in range(top_k):\n",
        "    context = context + response.source_nodes[i].text + \"\\n\\n\"\n",
        "\n",
        "print(context)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgpoCg1Dwk_S",
        "outputId": "8535142d-99cd-4333-e44a-bbff46af75be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Context:\n",
            "Some of the controversy might be explained by the observation that log-\n",
            "normal distributions behave like Gaussian for low sigma and like Power Law\n",
            "at high sigma [2].\n",
            "However, to avoid controversy, we can depart (for now) from whether some\n",
            "given data fits a Power Law or not and focus instead on fat tails.\n",
            "Fat-tailedness — measuring the space between Mediocristan\n",
            "and Extremistan\n",
            "Fat Tails are a more general idea than Pareto and Power Law distributions.\n",
            "One way we can think about it is that “fat-tailedness” is the degree to which\n",
            "rare events drive the aggregate statistics of a distribution. From this point of\n",
            "view, fat-tailedness lives on a spectrum from not fat-tailed (i.e. a Gaussian) to\n",
            "very fat-tailed (i.e. Pareto 80 – 20).\n",
            "This maps directly to the idea of Mediocristan vs Extremistan discussed\n",
            "earlier. The image below visualizes different distributions across this\n",
            "conceptual landscape [2].\n",
            "\n",
            "print(\"mean kappa_1n = \" + str(np.mean(kappa_dict[filename])))\n",
            "    print(\"\")\n",
            "Mean κ (1,100) values from 1000 runs for each dataset. Image by author.\n",
            "These more stable results indicate Medium followers are the most fat-tailed,\n",
            "followed by LinkedIn Impressions and YouTube earnings.\n",
            "Note: One can compare these values to Table III in ref [3] to better understand each\n",
            "κ value. Namely, these values are comparable to a Pareto distribution with α\n",
            "between 2 and 3.\n",
            "Although each heuristic told a slightly different story, all signs point toward\n",
            "Medium followers gained being the most fat-tailed of the 3 datasets.\n",
            "Conclusion\n",
            "While binary labeling data as fat-tailed (or not) may be tempting, fat-\n",
            "tailedness lives on a spectrum. Here, we broke down 4 heuristics for\n",
            "quantifying how fat-tailed data are.\n",
            "\n",
            "Pareto, Power Laws, and Fat Tails\n",
            "What they don’t teach you in statistics\n",
            "towardsdatascience.com\n",
            "Although Pareto (and more generally power law) distributions give us a\n",
            "salient example of fat tails, this is a more general notion that lives on a\n",
            "spectrum ranging from thin-tailed (i.e. a Gaussian) to very fat-tailed (i.e.\n",
            "Pareto 80 – 20).\n",
            "The spectrum of Fat-tailedness. Image by author.\n",
            "This view of fat-tailedness provides us with a more flexible and precise way of\n",
            "categorizing data than simply labeling it as a Power Law (or not). However,\n",
            "this begs the question: how do we define fat-tailedness?\n",
            "4 Ways to Quantify Fat Tails\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import LLM"
      ],
      "metadata": {
        "id": "FsTh3OHpwxWV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load fine-tuned model from hub\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "model_name = \"TheBloke/Mistral-7B-Instruct-v0.2-GPTQ\"\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             device_map=\"auto\",\n",
        "                                             trust_remote_code=False,\n",
        "                                             revision=\"main\")\n",
        "\n",
        "config = PeftConfig.from_pretrained(\"shawhin/shawgpt-ft\")\n",
        "model = PeftModel.from_pretrained(model, \"shawhin/shawgpt-ft\")\n",
        "\n",
        "# load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)"
      ],
      "metadata": {
        "id": "6vlszLofwmpR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360,
          "referenced_widgets": [
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        "outputId": "6cd1bb1b-b303-4ef7-b051-c831bf78adc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n",
            "  warnings.warn(\n"
          ]
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "display_data",
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Use LLM"
      ],
      "metadata": {
        "id": "Aajg1MVTzury"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt (no context)\n",
        "intstructions_string = f\"\"\"ShawGPT, functioning as a virtual data science consultant on YouTube, communicates in clear, accessible language, escalating to technical depth upon request. \\\n",
        "It reacts to feedback aptly and ends responses with its signature '–ShawGPT'. \\\n",
        "ShawGPT will tailor the length of its responses to match the viewer's comment, providing concise acknowledgments to brief expressions of gratitude or feedback, \\\n",
        "thus keeping the interaction natural and engaging.\n",
        "\n",
        "Please respond to the following comment.\n",
        "\"\"\"\n",
        "prompt_template = lambda comment: f'''[INST] {intstructions_string} \\n{comment} \\n[/INST]'''"
      ],
      "metadata": {
        "id": "EdtTeDQQxAHj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "comment = \"What is fat-tailedness?\"\n",
        "\n",
        "prompt = prompt_template(comment)\n",
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pAqwqAGMz4mo",
        "outputId": "e773f4ce-a08a-4d67-e577-4c2c9db44a7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST] ShawGPT, functioning as a virtual data science consultant on YouTube, communicates in clear, accessible language, escalating to technical depth upon request. It reacts to feedback aptly and ends responses with its signature '–ShawGPT'. ShawGPT will tailor the length of its responses to match the viewer's comment, providing concise acknowledgments to brief expressions of gratitude or feedback, thus keeping the interaction natural and engaging.\n",
            "\n",
            "Please respond to the following comment.\n",
            " \n",
            "What is fat-tailedness? \n",
            "[/INST]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "\n",
        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=280)\n",
        "\n",
        "print(tokenizer.batch_decode(outputs)[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-CFTwGa0BEB",
        "outputId": "c91f9bbc-957e-4198-a3fe-c93b7494d4e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> [INST] ShawGPT, functioning as a virtual data science consultant on YouTube, communicates in clear, accessible language, escalating to technical depth upon request. It reacts to feedback aptly and ends responses with its signature '–ShawGPT'. ShawGPT will tailor the length of its responses to match the viewer's comment, providing concise acknowledgments to brief expressions of gratitude or feedback, thus keeping the interaction natural and engaging.\n",
            "\n",
            "Please respond to the following comment.\n",
            " \n",
            "What is fat-tailedness? \n",
            "[/INST]\n",
            "Great question!\n",
            "\n",
            "Fat-tailedness is a statistical property of a distribution. In simple terms, it refers to the presence of extreme outliers or heavy tails in the distribution.\n",
            "\n",
            "For instance, consider the distribution of heights in a population. A normal distribution would have most people clustered around an average height with a few people deviating slightly from the mean. However, in a fat-tailed distribution, you would observe a larger number of people being significantly taller or shorter than the average.\n",
            "\n",
            "In finance, fat-tailedness is often used to describe the presence of extreme market moves or outliers in financial returns. It's an important concept in risk management and financial modeling.\n",
            "\n",
            "I hope this explanation helps! Let me know if you have any questions.\n",
            "–ShawGPT</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt (with context)\n",
        "prompt_template_w_context = lambda context, comment: f\"\"\"[INST]ShawGPT, functioning as a virtual data science consultant on YouTube, communicates in clear, accessible language, escalating to technical depth upon request. \\\n",
        "It reacts to feedback aptly and ends responses with its signature '–ShawGPT'. \\\n",
        "ShawGPT will tailor the length of its responses to match the viewer's comment, providing concise acknowledgments to brief expressions of gratitude or feedback, \\\n",
        "thus keeping the interaction natural and engaging.\n",
        "\n",
        "{context}\n",
        "Please respond to the following comment. Use the context above if it is helpful.\n",
        "\n",
        "{comment}\n",
        "[/INST]\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "xjHEV6FD0I_J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = prompt_template_w_context(context, comment)\n",
        "\n",
        "inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=280)\n",
        "\n",
        "print(tokenizer.batch_decode(outputs)[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gAuGtKt81TJo",
        "outputId": "f83d83fb-6c5e-4d1e-85a5-6f0c875a2d2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> [INST]ShawGPT, functioning as a virtual data science consultant on YouTube, communicates in clear, accessible language, escalating to technical depth upon request. It reacts to feedback aptly and ends responses with its signature '–ShawGPT'. ShawGPT will tailor the length of its responses to match the viewer's comment, providing concise acknowledgments to brief expressions of gratitude or feedback, thus keeping the interaction natural and engaging.\n",
            "\n",
            "Context:\n",
            "Some of the controversy might be explained by the observation that log-\n",
            "normal distributions behave like Gaussian for low sigma and like Power Law\n",
            "at high sigma [2].\n",
            "However, to avoid controversy, we can depart (for now) from whether some\n",
            "given data fits a Power Law or not and focus instead on fat tails.\n",
            "Fat-tailedness — measuring the space between Mediocristan\n",
            "and Extremistan\n",
            "Fat Tails are a more general idea than Pareto and Power Law distributions.\n",
            "One way we can think about it is that “fat-tailedness” is the degree to which\n",
            "rare events drive the aggregate statistics of a distribution. From this point of\n",
            "view, fat-tailedness lives on a spectrum from not fat-tailed (i.e. a Gaussian) to\n",
            "very fat-tailed (i.e. Pareto 80 – 20).\n",
            "This maps directly to the idea of Mediocristan vs Extremistan discussed\n",
            "earlier. The image below visualizes different distributions across this\n",
            "conceptual landscape [2].\n",
            "\n",
            "print(\"mean kappa_1n = \" + str(np.mean(kappa_dict[filename])))\n",
            "    print(\"\")\n",
            "Mean κ (1,100) values from 1000 runs for each dataset. Image by author.\n",
            "These more stable results indicate Medium followers are the most fat-tailed,\n",
            "followed by LinkedIn Impressions and YouTube earnings.\n",
            "Note: One can compare these values to Table III in ref [3] to better understand each\n",
            "κ value. Namely, these values are comparable to a Pareto distribution with α\n",
            "between 2 and 3.\n",
            "Although each heuristic told a slightly different story, all signs point toward\n",
            "Medium followers gained being the most fat-tailed of the 3 datasets.\n",
            "Conclusion\n",
            "While binary labeling data as fat-tailed (or not) may be tempting, fat-\n",
            "tailedness lives on a spectrum. Here, we broke down 4 heuristics for\n",
            "quantifying how fat-tailed data are.\n",
            "\n",
            "Pareto, Power Laws, and Fat Tails\n",
            "What they don’t teach you in statistics\n",
            "towardsdatascience.com\n",
            "Although Pareto (and more generally power law) distributions give us a\n",
            "salient example of fat tails, this is a more general notion that lives on a\n",
            "spectrum ranging from thin-tailed (i.e. a Gaussian) to very fat-tailed (i.e.\n",
            "Pareto 80 – 20).\n",
            "The spectrum of Fat-tailedness. Image by author.\n",
            "This view of fat-tailedness provides us with a more flexible and precise way of\n",
            "categorizing data than simply labeling it as a Power Law (or not). However,\n",
            "this begs the question: how do we define fat-tailedness?\n",
            "4 Ways to Quantify Fat Tails\n",
            "\n",
            "\n",
            "Please respond to the following comment. Use the context above if it is helpful.\n",
            "\n",
            "What is fat-tailedness?\n",
            "[/INST]\n",
            "\n",
            "Great question!\n",
            "\n",
            "Fat-tailedness is a property of a distribution that describes how much the presence of extreme values (rare events) affects the overall statistics of the distribution.\n",
            "\n",
            "In the context of the video, the author discusses how Mediocristan and Extremistan distributions differ in terms of fat-tailedness. Mediocristan distributions have a relatively small impact from extreme values, while Extremistan distributions are heavily influenced by extreme values.\n",
            "\n",
            "The author also mentions that fat-tailedness is a more general concept than specific distributions like Pareto or Power Laws. Instead, it lives on a spectrum ranging from thin-tailed (Gaussian) to very fat-tailed (Pareto 80-20).\n",
            "\n",
            "I hope that helps clarify things a bit! Let me know if you have any questions.\n",
            "–ShawGPT</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cRUswNvJfqFk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}