{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinGPT/blob/master/FinGPT_Training_LoRA_with_ChatGLM2_6B_for_Beginners.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Getting Started with FinGPT\n",
        "Welcome to this comprehensive guide aimed at beginners diving into the realm of Financial Large Language Models (FinLLMs) with FinGPT. This blog post demystifies the process of training FinGPT using Low-Rank Adaptation (LoRA) with the robust base model ChatGlm2-6b.\n",
        "\n"
      ],
      "metadata": {
        "id": "X8H-Vc6w6WSU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 1: Preparing the Data\n",
        "Data preparation is a crucial step when it comes to training Financial Large Language Models. Here, we’ll guide you on how to get your dataset ready for FinGPT using Python.\n",
        "\n",
        "In this section, you’ve initialized your working directory and loaded a financial sentiment dataset. Let’s break down the steps:\n",
        "\n"
      ],
      "metadata": {
        "id": "4oLjc7bbv0tO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets transformers torch tqdm pandas huggingface_hub\n",
        "!pip install sentencepiece\n",
        "!pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-maUV8CH7JPB",
        "outputId": "dc512a8f-b4e3-44cc-f489-b8f768d82f7e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.1 Initialize Directories:\n",
        "This block checks if certain paths exist; if they do, it deletes them to avoid data conflicts, and then creates a new directory for the upcoming data.\n",
        "\n"
      ],
      "metadata": {
        "id": "hJp_UOiB70o3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "aBE7gRUJ3L8u"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "jsonl_path = \"../data/dataset_new.jsonl\"\n",
        "save_path = '../data/dataset_new'\n",
        "\n",
        "\n",
        "if os.path.exists(jsonl_path):\n",
        "    os.remove(jsonl_path)\n",
        "\n",
        "if os.path.exists(save_path):\n",
        "    shutil.rmtree(save_path)\n",
        "\n",
        "directory = \"../data\"\n",
        "if not os.path.exists(directory):\n",
        "    os.makedirs(directory)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OvIBHhS5pV8o"
      },
      "source": [
        "### 1.2 Load and Prepare Dataset:\n",
        "\n",
        "* Import necessary libraries from the datasets package: https://huggingface.co/docs/datasets/index\n",
        "* Load the Twitter Financial News Sentiment (TFNS) dataset and convert it to a Pandas dataframe. https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment\n",
        "* Map numerical labels to their corresponding sentiments (negative, positive, neutral).\n",
        "* Add instruction for each data entry, which is crucial for Instruction Tuning.\n",
        "* Convert the Pandas dataframe back to a Hugging Face Dataset object.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        },
        "id": "wVVWAq54ohCT",
        "outputId": "6db17b03-b647-4af7-bf3d-aa79ae03cd83"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.57k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6573cca7c7eb4d8cae686699a2239067"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5694f8f68e2248f69e7df724e08a6195"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2dd75ac84881458ea838d2ca0795ebe0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9ac7dcbc7e24f2bb3f32df048a3bfde"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a2421ab197141c78ff4e2b1d68a525b"
            }
          },
          "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": "4c04bb4b1f714bb8b7fa45fe4109533a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea2db2f0c44b461da017f2115123faa5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input', 'output', 'instruction'],\n",
              "    num_rows: 9543\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "import datasets\n",
        "\n",
        "dic = {\n",
        "    0:\"negative\",\n",
        "    1:'positive',\n",
        "    2:'neutral',\n",
        "}\n",
        "\n",
        "tfns = load_dataset('zeroshot/twitter-financial-news-sentiment')\n",
        "tfns = tfns['train']\n",
        "tfns = tfns.to_pandas()\n",
        "tfns['label'] = tfns['label'].apply(lambda x:dic[x])\n",
        "tfns['instruction'] = 'What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.'\n",
        "tfns.columns = ['input', 'output', 'instruction']\n",
        "tfns = datasets.Dataset.from_pandas(tfns)\n",
        "tfns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.3 Concatenate and Shuffle Dataset"
      ],
      "metadata": {
        "id": "BRXfrEc8-yWy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXND6llnpTQj",
        "outputId": "4b088083-f274-4113-8ca8-808f73acc3b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19086\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19086, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "tmp_dataset = datasets.concatenate_datasets([tfns]*2)\n",
        "train_dataset = tmp_dataset\n",
        "print(tmp_dataset.num_rows)\n",
        "\n",
        "all_dataset = train_dataset.shuffle(seed = 42)\n",
        "all_dataset.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that your training data is loaded and prepared."
      ],
      "metadata": {
        "id": "oOVsy4if8_et"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oob5cnSlpi7C"
      },
      "source": [
        "## Part 2: Dataset Formatting and Tokenization\n",
        "Once your data is prepared, the next steps involve formatting the dataset for model ingestion and tokenizing the input data. Below, we provide a step-by-step breakdown of the code snippets shared.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1 Dataset Formatting:\n",
        "You need to structure your data in a specific format that aligns with the training process.\n",
        "\n"
      ],
      "metadata": {
        "id": "1vI4We-r9QXh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "mnZFyhcvpTIv"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "DjmW94OTpjuU"
      },
      "outputs": [],
      "source": [
        "def format_example(example: dict) -> dict:\n",
        "    context = f\"Instruction: {example['instruction']}\\n\"\n",
        "    if example.get(\"input\"):\n",
        "        context += f\"Input: {example['input']}\\n\"\n",
        "    context += \"Answer: \"\n",
        "    target = example[\"output\"]\n",
        "    return {\"context\": context, \"target\": target}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "oomCwuggpjsF"
      },
      "outputs": [],
      "source": [
        "data_list = []\n",
        "for item in all_dataset.to_pandas().itertuples():\n",
        "    tmp = {}\n",
        "    tmp[\"instruction\"] = item.instruction\n",
        "    tmp[\"input\"] = item.input\n",
        "    tmp[\"output\"] = item.output\n",
        "    data_list.append(tmp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "0c47ca540b134cc980002fece7027cd1",
            "55a63e75fb0f4e9b8abcdf97d6396f4c",
            "828b5c4bc5304f2f8d087e6be2aea3de",
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            "eeee958db1ce4d98a8dfc6d64fce44d6"
          ]
        },
        "id": "VuuRhlD1pjqH",
        "outputId": "52b73e43-d23d-43ea-ee00-a94c504527f9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "formatting..:   0%|          | 0/19086 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c47ca540b134cc980002fece7027cd1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# save to a jsonl file\n",
        "with open(\"../data/dataset_new.jsonl\", 'w') as f:\n",
        "    for example in tqdm(data_list, desc=\"formatting..\"):\n",
        "        f.write(json.dumps(format_example(example)) + '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YptAdKh0preG"
      },
      "source": [
        "### 2.2 Tokenization\n",
        "Tokenization is the process of converting input text into tokens that can be fed into the model.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Sjh98Wr_pjmI"
      },
      "outputs": [],
      "source": [
        "import datasets\n",
        "from transformers import AutoTokenizer, AutoConfig\n",
        "\n",
        "model_name = \"THUDM/chatglm2-6b\"\n",
        "jsonl_path = \"../data/dataset_new.jsonl\"  # updated path\n",
        "save_path = '../data/dataset_new'  # updated path\n",
        "max_seq_length = 512\n",
        "skip_overlength = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "_WJtjr5MpjkA"
      },
      "outputs": [],
      "source": [
        "# The preprocess function tokenizes the prompt and target, combines them into input IDs,\n",
        "# and then trims or pads the sequence to the maximum sequence length.\n",
        "def preprocess(tokenizer, config, example, max_seq_length):\n",
        "    prompt = example[\"context\"]\n",
        "    target = example[\"target\"]\n",
        "    prompt_ids = tokenizer.encode(prompt, max_length=max_seq_length, truncation=True)\n",
        "    target_ids = tokenizer.encode(\n",
        "        target,\n",
        "        max_length=max_seq_length,\n",
        "        truncation=True,\n",
        "        add_special_tokens=False)\n",
        "    input_ids = prompt_ids + target_ids + [config.eos_token_id]\n",
        "    return {\"input_ids\": input_ids, \"seq_len\": len(prompt_ids)}\n",
        "\n",
        "# The read_jsonl function reads each line from the JSONL file, preprocesses it using the preprocess function,\n",
        "# and then yields each preprocessed example.\n",
        "def read_jsonl(path, max_seq_length, skip_overlength=False):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\n",
        "        model_name, trust_remote_code=True)\n",
        "    config = AutoConfig.from_pretrained(\n",
        "        model_name, trust_remote_code=True, device_map='auto')\n",
        "    with open(path, \"r\") as f:\n",
        "        for line in tqdm(f.readlines()):\n",
        "            example = json.loads(line)\n",
        "            feature = preprocess(tokenizer, config, example, max_seq_length)\n",
        "            if skip_overlength and len(feature[\"input_ids\"]) > max_seq_length:\n",
        "                continue\n",
        "            feature[\"input_ids\"] = feature[\"input_ids\"][:max_seq_length]\n",
        "            yield feature"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.3 Save the dataset"
      ],
      "metadata": {
        "id": "j9M_NsM2-9Mk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
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        },
        "id": "B5yRJ52HpjiD",
        "outputId": "3b5aa77d-59ff-4a62-b19b-fb283935342e"
      },
      "outputs": [
        {
          "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": "cab66d7e3c104f87b990ae93ccafbe0a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/244 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "069070c8ff784daea79efbf7e61af6be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)enization_chatglm.py:   0%|          | 0.00/10.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7a91951ddf5486187e1cb976fc3c666"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n",
            "- tokenization_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/1.02M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c37604b40abc4680871dfda07fef8994"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eafeedacf9fe43aa8a6305908701f8ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)iguration_chatglm.py:   0%|          | 0.00/2.33k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d653840a8636418eaf0ff15027c4f9a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/THUDM/chatglm2-6b:\n",
            "- configuration_chatglm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19086 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc0acdd6bcb7453c8dc1efce21559562"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Saving the dataset (0/1 shards):   0%|          | 0/19086 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90053e24fb314c4cb8b2314f5f4b454c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# The script then creates a Hugging Face Dataset object from the generator and saves it to disk.\n",
        "save_path = '../data/dataset_new'\n",
        "\n",
        "dataset = datasets.Dataset.from_generator(\n",
        "    lambda: read_jsonl(jsonl_path, max_seq_length, skip_overlength)\n",
        "    )\n",
        "dataset.save_to_disk(save_path)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 3: Setup FinGPT training parameters with LoRA on ChatGlm2-6b\n",
        "\n",
        "Training a model can be resource-intensive. Ensure you have a powerful GPU\n",
        " * Need to purchase Google Colab GPU plans, Colab Pro is sufficient or just buy 100 compute units for $10\n",
        " * NVIDIA A100 is recommended due to its high memory capacity.\n",
        "\n"
      ],
      "metadata": {
        "id": "e4AgPl_t04qV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fu7GJche9RKe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "734f21b2-34c1-4491-f9a6-64f70f0d4868"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install torch torchvision torchaudio\n",
        "!pip install transformers\n",
        "!pip install loguru\n",
        "!pip install datasets\n",
        "!pip install peft\n",
        "!pip install bitsandbytes\n",
        "!pip install tensorboard\n",
        "!pip install sentencepiece\n",
        "!pip install accelerate -U"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Jpn7KAIW9RuP"
      },
      "outputs": [],
      "source": [
        "# Ensure CUDA is accessible in the system path\n",
        "# Only for Windows Subsystem for Linux (WSL)\n",
        "import os\n",
        "os.environ[\"PATH\"] = f\"{os.environ['PATH']}:/usr/local/cuda/bin\"\n",
        "os.environ['LD_LIBRARY_PATH'] = \"/usr/lib/wsl/lib:/usr/local/cuda/lib64\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Training Arguments Setup:\n",
        "Initialize and set training arguments.\n",
        "\n"
      ],
      "metadata": {
        "id": "aLjcEr3FBGzi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "gW5hW4-F9RsX"
      },
      "outputs": [],
      "source": [
        "from typing import List, Dict, Optional\n",
        "import torch\n",
        "from loguru import logger\n",
        "from transformers import (\n",
        "    AutoModel,\n",
        "    AutoTokenizer,\n",
        "    TrainingArguments,\n",
        "    Trainer,\n",
        "    BitsAndBytesConfig\n",
        ")\n",
        "from peft import (\n",
        "    TaskType,\n",
        "    LoraConfig,\n",
        "    get_peft_model,\n",
        "    set_peft_model_state_dict,\n",
        "    prepare_model_for_kbit_training,\n",
        "    prepare_model_for_int8_training,\n",
        ")\n",
        "from peft.utils import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4w2Pe1rT9Rqv"
      },
      "outputs": [],
      "source": [
        "training_args = TrainingArguments(\n",
        "        output_dir='./finetuned_model',    # saved model path\n",
        "        logging_steps = 500,\n",
        "        # max_steps=10000,\n",
        "        num_train_epochs = 2,\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=8,\n",
        "        learning_rate=1e-4,\n",
        "        weight_decay=0.01,\n",
        "        warmup_steps=1000,\n",
        "        save_steps=500,\n",
        "        fp16=True,\n",
        "        # bf16=True,\n",
        "        torch_compile = False,\n",
        "        load_best_model_at_end = True,\n",
        "        evaluation_strategy=\"steps\",\n",
        "        remove_unused_columns=False,\n",
        "\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Quantization Config Setup:\n",
        "Set quantization configuration to reduce model size without losing significant precision.\n",
        "\n"
      ],
      "metadata": {
        "id": "QGabm7cyBM6r"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "OfKTizyP9Rmn"
      },
      "outputs": [],
      "source": [
        " # Quantization\n",
        "q_config = BitsAndBytesConfig(load_in_4bit=True,\n",
        "                                bnb_4bit_quant_type='nf4',\n",
        "                                bnb_4bit_use_double_quant=True,\n",
        "                                bnb_4bit_compute_dtype=torch.float16\n",
        "                                )"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Model Loading & Preparation:\n",
        "Load the base model and tokenizer, and prepare the model for INT8 training.\n",
        "\n",
        "* **Runtime -> Change runtime type -> A100 GPU**\n",
        "* retart runtime and run again if not working\n"
      ],
      "metadata": {
        "id": "WMqFjp_mBVqO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "th_3Rnqy9Rkg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392,
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        },
        "outputId": "1e91c290-beec-416f-f7ec-5f33a2953581"
      },
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {}
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          "metadata": {}
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        {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3eefa6154f1f464493db26cad778cdf1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:122: 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"
          ]
        }
      ],
      "source": [
        "# Load tokenizer & model\n",
        "# need massive space\n",
        "model_name = \"THUDM/chatglm2-6b\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(\n",
        "        model_name,\n",
        "        quantization_config=q_config,\n",
        "        trust_remote_code=True,\n",
        "        device='cuda'\n",
        "    )\n",
        "model = prepare_model_for_int8_training(model, use_gradient_checkpointing=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.4 LoRA Config & Setup:\n",
        "Implement Low-Rank Adaptation (LoRA) and print trainable parameters.\n",
        "\n"
      ],
      "metadata": {
        "id": "Dfw3w9FNBgyF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "IDbX7Ati9RiK"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "RgPzHGUV9RgT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e07ea0b6-5cff-4ee0-b7da-4daf44bb1600"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 1949696 || all params: 3390261248 || trainable%: 0.05750872447219737\n"
          ]
        }
      ],
      "source": [
        "# LoRA\n",
        "target_modules = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING['chatglm']\n",
        "lora_config = LoraConfig(\n",
        "    task_type=TaskType.CAUSAL_LM,\n",
        "    inference_mode=False,\n",
        "    r=8,\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.1,\n",
        "    target_modules=target_modules,\n",
        "    bias='none',\n",
        ")\n",
        "model = get_peft_model(model, lora_config)\n",
        "print_trainable_parameters(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Qt2DjmPn9ReS"
      },
      "outputs": [],
      "source": [
        "resume_from_checkpoint = None\n",
        "if resume_from_checkpoint is not None:\n",
        "    checkpoint_name = os.path.join(resume_from_checkpoint, 'pytorch_model.bin')\n",
        "    if not os.path.exists(checkpoint_name):\n",
        "        checkpoint_name = os.path.join(\n",
        "            resume_from_checkpoint, 'adapter_model.bin'\n",
        "        )\n",
        "        resume_from_checkpoint = False\n",
        "    if os.path.exists(checkpoint_name):\n",
        "        logger.info(f'Restarting from {checkpoint_name}')\n",
        "        adapters_weights = torch.load(checkpoint_name)\n",
        "        set_peft_model_state_dict(model, adapters_weights)\n",
        "    else:\n",
        "        logger.info(f'Checkpoint {checkpoint_name} not found')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "kOT2o7FU9Rcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83c9c2ad-a1b6-4816-819f-63b5a0b3ae5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 1,949,696 || all params: 6,245,533,696 || trainable%: 0.031217444255383614\n"
          ]
        }
      ],
      "source": [
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 4: Loading Data and Training FinGPT\n",
        "In this segment, we'll delve into the loading of your pre-processed data, and finally, launch the training of your FinGPT model. Here's a stepwise breakdown of the script provided:\n",
        "* Need to purchase Google Colab GPU plans, Colab Pro is sufficient or just buy 100 compute units for $10\n"
      ],
      "metadata": {
        "id": "AsRYIMb_CLYR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Loading Your Data:\n"
      ],
      "metadata": {
        "id": "nvmJH99eCR5Z"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "4PbUCglW9RaM"
      },
      "outputs": [],
      "source": [
        "# load data\n",
        "from datasets import load_from_disk\n",
        "import datasets\n",
        "\n",
        "dataset = datasets.load_from_disk(\"../data/dataset_new\")\n",
        "dataset = dataset.train_test_split(0.2, shuffle=True, seed = 42)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 Training Configuration and Launch:\n",
        "* Customize the Trainer class for specific loss computation, prediction step, and model-saving methods.\n",
        "\n",
        "* Define a data collator function to process batches of data during training.\n",
        "\n",
        "* Set up TensorBoard for logging, instantiate your modified trainer, and begin training.\n",
        "\n"
      ],
      "metadata": {
        "id": "Lq87Er6RCZbb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "dUgghgf59RYG"
      },
      "outputs": [],
      "source": [
        "class ModifiedTrainer(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        return model(\n",
        "            input_ids=inputs[\"input_ids\"],\n",
        "            labels=inputs[\"labels\"],\n",
        "        ).loss\n",
        "\n",
        "    def prediction_step(self, model: torch.nn.Module, inputs, prediction_loss_only: bool, ignore_keys = None):\n",
        "        with torch.no_grad():\n",
        "            res = model(\n",
        "                input_ids=inputs[\"input_ids\"].to(model.device),\n",
        "                labels=inputs[\"labels\"].to(model.device),\n",
        "            ).loss\n",
        "        return (res, None, None)\n",
        "\n",
        "    def save_model(self, output_dir=None, _internal_call=False):\n",
        "        from transformers.trainer import TRAINING_ARGS_NAME\n",
        "\n",
        "        os.makedirs(output_dir, exist_ok=True)\n",
        "        torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))\n",
        "        saved_params = {\n",
        "            k: v.to(\"cpu\") for k, v in self.model.named_parameters() if v.requires_grad\n",
        "        }\n",
        "        torch.save(saved_params, os.path.join(output_dir, \"adapter_model.bin\"))\n",
        "\n",
        "def data_collator(features: list) -> dict:\n",
        "    len_ids = [len(feature[\"input_ids\"]) for feature in features]\n",
        "    longest = max(len_ids)\n",
        "    input_ids = []\n",
        "    labels_list = []\n",
        "    for ids_l, feature in sorted(zip(len_ids, features), key=lambda x: -x[0]):\n",
        "        ids = feature[\"input_ids\"]\n",
        "        seq_len = feature[\"seq_len\"]\n",
        "        labels = (\n",
        "            [tokenizer.pad_token_id] * (seq_len - 1) + ids[(seq_len - 1) :] + [tokenizer.pad_token_id] * (longest - ids_l)\n",
        "        )\n",
        "        ids = ids + [tokenizer.pad_token_id] * (longest - ids_l)\n",
        "        _ids = torch.LongTensor(ids)\n",
        "        labels_list.append(torch.LongTensor(labels))\n",
        "        input_ids.append(_ids)\n",
        "    input_ids = torch.stack(input_ids)\n",
        "    labels = torch.stack(labels_list)\n",
        "    return {\n",
        "        \"input_ids\": input_ids,\n",
        "        \"labels\": labels,\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ITubEZSK9RVv"
      },
      "outputs": [],
      "source": [
        "from torch.utils.tensorboard import SummaryWriter\n",
        "from transformers.integrations import TensorBoardCallback"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Cw4Zik6a9RT3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "fcf72d9b-3267-4d72-93fc-0fd09057c338"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are adding a <class 'transformers.integrations.TensorBoardCallback'> to the callbacks of this Trainer, but there is already one. The currentlist of callbacks is\n",
            ":DefaultFlowCallback\n",
            "TensorBoardCallback\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "WARNING:transformers_modules.THUDM.chatglm2-6b.8fd7fba285f7171d3ae7ea3b35c53b6340501ed1.modeling_chatglm:`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='954' max='954' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [954/954 40:18, Epoch 1/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>9.619700</td>\n",
              "      <td>6.023976</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Train\n",
        "# Took about 10 compute units\n",
        "writer = SummaryWriter()\n",
        "trainer = ModifiedTrainer(\n",
        "    model=model,\n",
        "    args=training_args,             # Trainer args\n",
        "    train_dataset=dataset[\"train\"], # Training set\n",
        "    eval_dataset=dataset[\"test\"],   # Testing set\n",
        "    data_collator=data_collator,    # Data Collator\n",
        "    callbacks=[TensorBoardCallback(writer)],\n",
        ")\n",
        "trainer.train()\n",
        "writer.close()\n",
        "# save model\n",
        "model.save_pretrained(training_args.output_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.3 Model Saving and Download:\n",
        "After training, save and download your model. You can also check the model's size.\n",
        "\n"
      ],
      "metadata": {
        "id": "brHTWfnmCn5D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r /content/saved_model.zip /content/{training_args.output_dir}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HUYxzwS_9lMI",
        "outputId": "7840afdf-ab03-4664-fd1b-99aa55f81125"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/./finetuned_model/ (stored 0%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/ (stored 0%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/optimizer.pt (deflated 7%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/trainer_state.json (deflated 53%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/scheduler.pt (deflated 49%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/rng_state.pth (deflated 28%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/adapter_model.bin (deflated 8%)\n",
            "  adding: content/./finetuned_model/checkpoint-500/training_args.bin (deflated 48%)\n",
            "  adding: content/./finetuned_model/runs/ (stored 0%)\n",
            "  adding: content/./finetuned_model/runs/Oct06_08-19-46_81e899208623/ (stored 0%)\n",
            "  adding: content/./finetuned_model/runs/Oct06_08-19-46_81e899208623/events.out.tfevents.1696580727.81e899208623.2594.1 (deflated 59%)\n",
            "  adding: content/./finetuned_model/adapter_model.bin (deflated 8%)\n",
            "  adding: content/./finetuned_model/README.md (deflated 39%)\n",
            "  adding: content/./finetuned_model/adapter_config.json (deflated 42%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# download to local\n",
        "from google.colab import files\n",
        "files.download('/content/saved_model.zip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "s-7s2Cjw9pAM",
        "outputId": "f00c656b-1122-4a81-896b-c8d94a31979c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_0a618fc5-4945-4791-b2cd-8ac358536eeb\", \"saved_model.zip\", 28956881)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save to google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvBgMgO8RADU",
        "outputId": "52bd847d-5bef-4b12-b9ed-d7e9795b3fcb"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save the finetuned model to google drive\n",
        "!cp -r \"/content/finetuned_model\" \"/content/drive/MyDrive\"\n"
      ],
      "metadata": {
        "id": "UUctmjm8RIfQ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "unRoLshR9RQZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e39e6a5-cbc4-4459-9346-7df02f7c5c5d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model size: 29.84746265411377 MB\n"
          ]
        }
      ],
      "source": [
        "def get_folder_size(folder_path):\n",
        "    total_size = 0\n",
        "    for dirpath, _, filenames in os.walk(folder_path):\n",
        "        for f in filenames:\n",
        "            fp = os.path.join(dirpath, f)\n",
        "            total_size += os.path.getsize(fp)\n",
        "    return total_size / 1024 / 1024  # Size in MB\n",
        "\n",
        "model_size = get_folder_size(training_args.output_dir)\n",
        "print(f\"Model size: {model_size} MB\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now your model is trained and saved! You can download it and use it for generating financial insights or any other relevant tasks in the finance domain. The usage of TensorBoard allows you to deeply understand and visualize the training dynamics and performance of your model in real-time.\n",
        "\n",
        "Happy FinGPT Training! 🚀"
      ],
      "metadata": {
        "id": "1LCjYKuoCusU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Part 5: Inference and Benchmarks using FinGPT\n",
        "Now that your model is trained, let’s understand how to use it to infer and run benchmarks.\n",
        "* Took about 10 compute units\n",
        "\n"
      ],
      "metadata": {
        "id": "76g_Qlp8t_Yp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers==4.30.2 peft==0.4.0\n",
        "!pip install sentencepiece\n",
        "!pip install accelerate\n",
        "!pip install torch\n",
        "!pip install peft\n",
        "!pip install datasets\n",
        "!pip install bitsandbytes"
      ],
      "metadata": {
        "id": "ehjG2bpft_OH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1806add3-b383-41eb-f6c9-76f982162b1b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers==4.30.2 in /usr/local/lib/python3.10/dist-packages (4.30.2)\n",
            "Collecting peft==0.4.0\n",
            "  Downloading peft-0.4.0-py3-none-any.whl (72 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Installing collected packages: peft\n",
            "  Attempting uninstall: peft\n",
            "    Found existing installation: peft 0.5.0\n",
            "    Uninstalling peft-0.5.0:\n",
            "      Successfully uninstalled peft-0.5.0\n",
            "Successfully installed peft-0.4.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "peft"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
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            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.41.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.1 Load the model"
      ],
      "metadata": {
        "id": "P91SXTrLS34i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#clone the FinNLP repository\n",
        "!git clone https://github.com/AI4Finance-Foundation/FinNLP.git\n",
        "\n",
        "import sys\n",
        "sys.path.append('/content/FinNLP/')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5jyY7S_uEls",
        "outputId": "739e49ed-0b31-46dd-96e0-162b4ef8073d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'FinNLP'...\n",
            "remote: Enumerating objects: 1316, done.\u001b[K\n",
            "remote: Counting objects: 100% (375/375), done.\u001b[K\n",
            "remote: Compressing objects: 100% (166/166), done.\u001b[K\n",
            "remote: Total 1316 (delta 219), reused 303 (delta 175), pack-reused 941\u001b[K\n",
            "Receiving objects: 100% (1316/1316), 4.21 MiB | 13.55 MiB/s, done.\n",
            "Resolving deltas: 100% (592/592), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "from peft import PeftModel\n",
        "import torch\n",
        "\n",
        "# Load benchmark datasets from FinNLP\n",
        "from finnlp.benchmarks.fpb import test_fpb\n",
        "from finnlp.benchmarks.fiqa import test_fiqa , add_instructions\n",
        "from finnlp.benchmarks.tfns import test_tfns\n",
        "from finnlp.benchmarks.nwgi import test_nwgi"
      ],
      "metadata": {
        "id": "zRsmSTFZuEjt"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade peft"
      ],
      "metadata": {
        "id": "EBqKeUYV9VjF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "outputId": "20f6ad0e-9e52-4e8b-dde8-33509ef2f381"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.4.0)\n",
            "Collecting peft\n",
            "  Using cached peft-0.5.0-py3-none-any.whl (85 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft) (1.23.5)\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) (2.0.1+cu118)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft) (4.30.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.66.1)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from peft) (0.23.0)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft) (0.3.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (4.5.0)\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) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft) (3.27.6)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft) (17.0.2)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate->peft) (0.16.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers->peft) (0.13.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate->peft) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft) (2023.7.22)\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: peft\n",
            "  Attempting uninstall: peft\n",
            "    Found existing installation: peft 0.4.0\n",
            "    Uninstalling peft-0.4.0:\n",
            "      Successfully uninstalled peft-0.4.0\n",
            "Successfully installed peft-0.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "peft"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load model from google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "gRRw9drdA2hv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d75720b1-35da-4305-cd38-309f64d38eb0"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the path you want to check\n",
        "path_to_check = \"/content/drive/My Drive/finetuned_model\"\n",
        "\n",
        "# Check if the specified path exists\n",
        "if os.path.exists(path_to_check):\n",
        "    print(\"Path exists.\")\n",
        "else:\n",
        "    print(\"Path does not exist.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxCAhg9QpkyI",
        "outputId": "81989b85-4abf-4403-c730-ac6e8ebe3488"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Path exists.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## load the chatglm2-6b base model\n",
        "base_model = \"THUDM/chatglm2-6b\"\n",
        "peft_model = training_args.output_dir\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(base_model, trust_remote_code=True, load_in_8bit=True, device_map=\"auto\")\n",
        "\n",
        "model = PeftModel.from_pretrained(model, peft_model)\n",
        "\n",
        "model = model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "424390ff1b0f4c3884e9f0c59fc9a0d7",
            "9a09a691e0064c708c39fee52e5a85cd",
            "111f553446b6427aa8b752c3bb9bf2a0",
            "9275857ca36447b2af2b238aeb64c192",
            "616211b4a10348198d070b35dcfa4ec0",
            "5355d4c77ea54fe3a6f72e7dba2125d0",
            "fa72cf75c46a4a619ce33320573fa9cb",
            "3275141d70f64ae4ae7e79efc792869c",
            "636fd6c2446b47a9a6168ce3634c6b01",
            "3eb06cbb516e4cf28f968ba08a1c7b44",
            "d007d3a04df2494b80d1300f1b8e624a"
          ]
        },
        "id": "bRljPCKC_srt",
        "outputId": "b7ab1104-fae1-4f31-9bfc-06cc53b3fbc9"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "424390ff1b0f4c3884e9f0c59fc9a0d7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## load our finetuned model\n",
        "base_model = \"THUDM/chatglm2-6b\"\n",
        "peft_model = \"/content/drive/My Drive/finetuned_model\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "model = AutoModel.from_pretrained(base_model, trust_remote_code=True, load_in_8bit=True, device_map=\"auto\")\n",
        "\n",
        "model = PeftModel.from_pretrained(model, peft_model)\n",
        "\n",
        "model = model.eval()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "64d59ba30e1b46a4a755e12e1b6e90a4",
            "c5926183d0154195913878c9a56044a9",
            "0ffa8c2a594940eca4a611b4b6e61a03",
            "591a9a19001e4b48b71c200c2ea85cce",
            "c0c26c7360844470b1a8e8dfafbb97ed",
            "e744d81f378644708154cdd8b4c3ea59",
            "67a8bf17536447d0981d78c31472ca3d",
            "262b7b0ee9084161be103876600a22ff",
            "551613ad2d00440281a1813b9fa14edb",
            "2dd6f1461aff4c60b3b95c35b801d873",
            "c25d5f7db3884b909bf567eeffa6dd25"
          ]
        },
        "id": "w37K19SvlwYh",
        "outputId": "5781f009-cccc-4395-eae0-535d768672b6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64d59ba30e1b46a4a755e12e1b6e90a4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5.2 Run Benchmarks:"
      ],
      "metadata": {
        "id": "HZGMT76ITkBY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 8"
      ],
      "metadata": {
        "id": "rv5tja2AuEf3"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TFNS Test Set, len 2388\n",
        "# Available: 84.85 compute units\n",
        "res = test_tfns(model, tokenizer, batch_size = batch_size)\n",
        "# Available: 83.75 compute units\n",
        "# Took about 1 compute unite to inference"
      ],
      "metadata": {
        "id": "0Z4capS_uEeF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b4d4fa3-de93-4f18-92dd-e684d95193b8"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: $ALLY - Ally Financial pulls outlook https://t.co/G9Zdi1boy5\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 2388. Batchsize: 8. Total steps: 299\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 299/299 [01:38<00:00,  3.03it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.8555276381909548. F1 macro: 0.8055229538173897. F1 micro: 0.8555276381909548. F1 weighted (BloombergGPT): 0.8495960118633726. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# FPB, len 1212\n",
        "res = test_fpb(model, tokenizer, batch_size = batch_size)"
      ],
      "metadata": {
        "id": "7we6z7kwuEcO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388,
          "referenced_widgets": [
            "0d517942ce37432196c706a903c4a6a2",
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            "d3631b65f1d542b8946b6661eabf237f",
            "3409be1f51df4b2dade54f4de9d097a4",
            "02ff328987e14c9398b8e5d2ee1ce85b",
            "1cbd0738ee854be6925f4cd3555657fb",
            "99265ab6b07d40c99345b1a45fca0975",
            "0923b481c7144a0394b207d9e3051b38",
            "1965a8d55cd8481ca41beac2ce14cd11",
            "5488578622b048b09bdb37051af6f014",
            "611bf1a5e62d421fbd97ef9d5d5cccf3",
            "e40eea397bb24478b35a670e89978610",
            "cb956c2b5ab24bf0bef4d33c9be30bb1",
            "fb94ac1ae5e34dbc84bc4f484161eb43",
            "0e82191cd8094d9cb51ecf8e3d0f9825",
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            "d3b247b42c5d4b08a903bfd24ccdac95",
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            "14117a816a8d4acba9cbe0a4d37c0ca0",
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            "451d4080b93442f08d25d458712095da",
            "6d32510ea8c2485fb795938f49be296f",
            "260e225b1299410c8c42bf2c6babd59e",
            "d53adf05206d4bddb237ce2a0b7e9198",
            "5d6dcef446e64b05a490c191c9ffcf85",
            "141461619c6c4625b133183f9b5a9925",
            "e145f263e738468f8bec09cd4d020fb4",
            "02fc05a46fef49029c7e42ea4d98e4e7",
            "d5ab71ec0e084b7695d0f00f97b2fef9",
            "0e5d7beb8afa42008e11a9ee321cc3a7",
            "dac395fb97eb47d3b0aca9d335802076",
            "b3a7f6a56f2e4267ab49268eb57e3911",
            "9947b797bc734611b8f8689e5e59399b",
            "3d982f314ba14e86bdfc8c055a095a13",
            "661c15272a1544cd98cbbc3d6622297b",
            "e519c33b2ccf410cb07767f5afdaa695",
            "c0eafbb2c7434d12bfaf0adc50d1017e",
            "a897aed19828421cbb3a88a3dac31e41",
            "1edcdacb970c426ca62ae03e31209c8c",
            "c561df9528194e08b3c650329910b8f7",
            "98c3fd7100364590840b0d11825e48eb",
            "34546cd72e7044b8a88a55b1d0cf1813",
            "7c46d2c0641645cc967e93b9771d545d",
            "8e448afc15234a8bbeecfd2ab0dd7334",
            "87f60c0ff5c3455c8183658229007992",
            "c04e729520ca41ee8cdc205d6bdb3d2b"
          ]
        },
        "outputId": "9ad083c1-5400-4e80-d8d7-93f8f9afcb4d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.04k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d517942ce37432196c706a903c4a6a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/13.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1cbd0738ee854be6925f4cd3555657fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/8.88k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c0a7f19a6a94e69a1c9846d46583f97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d6dcef446e64b05a490c191c9ffcf85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/4846 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e519c33b2ccf410cb07767f5afdaa695"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: L&T has also made a commitment to redeem the remaining shares by the end of 2011 .\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 1212. Batchsize: 8. Total steps: 152\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 152/152 [00:50<00:00,  3.03it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.740924092409241. F1 macro: 0.6550990950335318. F1 micro: 0.740924092409241. F1 weighted (BloombergGPT): 0.6936361630103864. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# FiQA, len 275\n",
        "res = test_fiqa(model, tokenizer, prompt_fun = add_instructions, batch_size = batch_size)"
      ],
      "metadata": {
        "id": "UTpEPwnIuEaN",
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        },
        "outputId": "b2c555dc-6073-42a9-bbdf-4d629f4ded0a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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        {
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          "data": {
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a1d8d6ad979941c88fa12574b044a3a4"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a16976598fc243ad9ce9fa153da98dd4"
            }
          },
          "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": "1170cf976198467c8b24e475bd7b0ad4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a8fe3c786c644f1984d35c1ff8b9675"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15a01bf5c521410bbc0f6c88cb921942"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: This $BBBY stock options trade would have more than doubled your money https://t.co/Oa0loiRIJL via @TheStreet\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 275. Batchsize: 8. Total steps: 35\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 35/35 [00:12<00:00,  2.80it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acc: 0.48. F1 macro: 0.4898448773448773. F1 micro: 0.48. F1 weighted (BloombergGPT): 0.5592298307752852. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NWGI, len 4047\n",
        "res = test_nwgi(model, tokenizer, batch_size = batch_size)"
      ],
      "metadata": {
        "id": "tWl6ILV2uEYA",
        "colab": {
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          "height": 452,
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      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/682 [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53001c4f44e348c6a3a979c8e638439e"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "1a7d561427954b4e8c3efd7039c122f8"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/16184 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "44f4c9a989144fb78045f262c32bd172"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/4047 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29cd8894cc354912ab97bee1f888527a"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Prompt example:\n",
            "Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.\n",
            "Input: In the latest trading session, Adobe Systems (ADBE) closed at $535.98, marking a +0.31% move from the previous day.\n",
            "Answer: \n",
            "\n",
            "\n",
            "Total len: 4047. Batchsize: 8. Total steps: 506\n"
          ]
        },
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          "text": [
            "100%|██████████| 506/506 [02:54<00:00,  2.89it/s]"
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            "Acc: 0.5213738571781567. F1 macro: 0.5002747720191362. F1 micro: 0.5213738571781567. F1 weighted (BloombergGPT): 0.4900144798114501. \n"
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      "source": [
        "### 5.3 Compare it with FinGPT V3.1 results\n",
        "https://github.com/AI4Finance-Foundation/FinGPT/tree/master/fingpt/FinGPT-*v3*"
      ],
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        "#### Comparison\n",
        "\n",
        "\n",
        "***TFNS:***\n",
        "\n",
        "***FinGPT V3.1:***\n",
        "\n",
        "* Acc: 0.876\n",
        "* F1 macro: 0.841\n",
        "* F1 weighted (follow BloombergGPT): 0.875\n",
        "\n",
        "**This notebook**:\n",
        "* Acc: 0.856\n",
        "* F1 macro: 0.806\n",
        "* F1 weighted (follow BloombergGPT): 0.850\n",
        "\n",
        "#### Since we trained on the TFNS dataset, it is expected that the test results would be good.\n",
        "----\n",
        "***FPB:***\n",
        "\n",
        "***FinGPT V3.1:***\n",
        "* Acc: 0.856\n",
        "* F1 macro: 0.841\n",
        "* F1 weighted: 0.855\n",
        "\n",
        "**This notebook**:\n",
        "* Acc: 0.741\n",
        "* F1 macro: 0.655\n",
        "* F1 weighted: 0.694\n",
        "\n",
        "#### Considering the FPB dataset was not included in our training set, the obtained zero-shot results are acceptable.\n",
        "\n",
        "\n",
        "----\n",
        "***FiQA:***\n",
        "\n",
        "***FinGPT V3.1:***\n",
        "* Acc: 0.836\n",
        "* F1 macro: 0.746\n",
        "* F1 weighted: 0.850\n",
        "\n",
        "**This notebook**:\n",
        "* Acc: 0.48\n",
        "* F1 macro: 0.5\n",
        "* F1 weighted: 0.49\n",
        "\n",
        "#### Since the FiQA dataset wasn't part of our training set, our model's zero-shot performance is relatively poor compared to FinGPT V3.1.\n",
        "\n",
        "----\n",
        "***NWGI:***\n",
        "\n",
        "***FinGPT V3.1:***\n",
        "* Acc: 0.642\n",
        "* F1 macro: 0.650\n",
        "* F1 weighted: 0.642\n",
        "\n",
        "**This notebook**:\n",
        "* Acc: 0.521\n",
        "* F1 macro: 0.500\n",
        "* F1 weighted: 0.490\n",
        "\n",
        "#### The results are reasonable"
      ],
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      "source": [
        "## Conclusion:\n",
        "\n",
        "* The training and testing of FinGPT in this exercise demanded **a total of 20 compute units**, broken down into 10 for training and another 10 for inference.\n",
        "* 100 Compute Units for 10 dollars, that **makes 2 dollars to train and test with FinGPT**\n",
        "* This cost-effective approach is primarily attributable to the utilization of the **Low-Rank Adaptation (LoRA) method**, which proves to be economical while ensuring efficient model training and inference.\n",
        "\n",
        "\n",
        "\n",
        "This exercise provided insights into the performance of your trained FinGPT model across various benchmarks. While there are areas where it excels, certain benchmarks highlight opportunities for improvement and tuning. Exploring additional training data and refining the model further will likely lead to enhanced performance across different financial NLP tasks, making it a powerful tool for various applications in the finance sector.\n",
        "\n",
        "\n",
        "\n",
        "Happy Experimenting with FinGPT! 🚀\n",
        "\n",
        "\n",
        "\n"
      ],
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