Fine_tuning_mental_health_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xb1aLHZRFrwA"
      },
      "source": [
        "# **Ludwig + DeepLearning.ai: Efficient Fine-Tuning for Llama2-7b on a Single GPU** 🙌\n",
        "\n",
        "Let's explore how to fine-tune an LLM on a single commodity GPU with [Ludwig](https://ludwig.ai/latest/), an open-source package that empowers you to effortlessly build and train machine learning models like LLMs, neural networks and tree based models through declarative config files.\n",
        "\n",
        "In this notebook, we'll show an example of how to fine-tune Llama-2-7b to generate code using the CodeAlpaca dataset.\n",
        "\n",
        "By the end of this example, you will have gained a comprehensive understanding of the following key aspects:\n",
        "\n",
        "1. **Ludwig**: An intuitive toolkit that simplifies fine-tuning for open-source Language Model Models (LLMs).\n",
        "2. **Exploring the base model with prompts**: Dive into the intricacies of prompts and prompt templates, unlocking new dimensions in LLM interaction.\n",
        "3. **Fine-Tuning Large Language Models**: Navigate the world of model fine-tuning optimizations for getting the most out of a single memory-contrained GPU, including: LoRA and 4-bit quantization.\n",
        "\n",
        "<br>\n",
        "\n",
        "👀 If you're interested in a managed solution for training larger LLMs that require distributed training using multi-node multi-GPU setups, or for fast on-demand inference for LLMs, check out [Predibase](https://predibase.com/), the AI platform for engineers built on top of Ludwig! Try Predibase's free 14 day trial by signing up [here](https://pbase.ai/3OD77wQ)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWzGlJcideZQ"
      },
      "source": [
        "# **Goal: Use LLMs For Code-Generation** 💻\n",
        "\n",
        "In this webinar, the goal is to use an LLM for code generation. The model will take natural language as input, and should return code as output. We're first going to iterate on a base Llama-2-7b model with prompting, and finally instruction-fine-tune the model.\n",
        "\n",
        "As an example, if we prompt the model with this instruction:\n",
        "\n",
        "```\n",
        "Instruction: Create an array of length 5 which contains all even numbers between 1 and 10.\n",
        "```\n",
        "\n",
        "We want the model to produce exactly this response:\n",
        "\n",
        "```\n",
        "Response: array = [2, 4, 6, 8, 10]\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAkkNUQMCm38"
      },
      "source": [
        "# **A Quick 2 Minute Introduction To Ludwig** ⏰\n",
        "\n",
        "Every Ludwig model is based on a config, which requires at least input feature and one output feature to be defined. For example,\n",
        "\n",
        "```yaml\n",
        "input_features:\n",
        "  - name: instruction\n",
        "    type: text\n",
        "output_features:\n",
        "  - name: output\n",
        "    type: text\n",
        "```\n",
        "\n",
        "is a simple Ludwig config that tells Ludwig to use the column called `instruction` in our dataset as an input feature and the `output` column in our dataset as an `output` feature. This is the simplest Ludwig config we can define - it's just 6 lines and works out of the box!\n",
        "\n",
        "To make Ludwig compatible with LLMs, **Ludwig 0.8** introduced a new `model_type` called `llm` and a new keyword `base_model` that must be specified:\n",
        "\n",
        "```yaml\n",
        "model_type: llm\n",
        "base_model: meta-llama/Llama-2-7b-hf\n",
        "input_features:\n",
        "  - name: instruction\n",
        "    type: text\n",
        "output_features:\n",
        "  - name: output\n",
        "    type: text\n",
        "```\n",
        "\n",
        "The `model_type` parameter indicates is used to tell Ludwig you want to use the LLM model type (Ludwig supports LLMs, general deep neural networks and trees).\n",
        "The `base_model` parameter is the path to any HuggingFace CausalLM listed [here](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dp9ONuZf2ALw"
      },
      "source": [
        "Once you define your YAML Config, there are two ways you can use it with Ludwig:\n",
        "\n",
        "- Through the Python LudwigModel API: https://ludwig.ai/latest/user_guide/api/LudwigModel/\n",
        "- Through the Ludwig CLI: https://ludwig.ai/latest/user_guide/command_line_interface/\n",
        "\n",
        "For this webinar, we're going to make use of the Python LudwigModel API. This requires just one main object during initialization: a YAML config defining your training pipeline. The initialized LudwigModel object then exposes a variety of methods like `preprocess()`, `train()`, `evaluate()` and `predict()`. We will see this in practice in the next few sections.\n",
        "\n",
        "In general, there is feature parity across both methods so you can use the approach you're most comfortable with. To see what commands are available, create a new cell and run `!ludwig --help`.\n",
        "\n",
        "This is all you need for now! Keeping this in mind, let's dive into the latest features in Ludwig 0.8."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EAIqUUGnM72V"
      },
      "source": [
        "## **Basic Setup** 🧰\n",
        "\n",
        "We're going to install Ludwig, setup our HuggingFace Token and load our dataset that we will be running experiments with."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wXMVoEFkPXJJ"
      },
      "source": [
        "### **Install Ludwig and Ludwig's LLM related dependencies.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiZYaiRufHfh"
      },
      "source": [
        "Install Ludwig from the latest release"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "jvL1cL6wYtWz"
      },
      "outputs": [],
      "source": [
        "# !pip install ludwig\n",
        "# !pip install ludwig[llm]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7eAUs6efGH5"
      },
      "source": [
        "Install Ludwig from Ludwig master"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfYXyUUvM-R6",
        "outputId": "dc1bdb0f-f7bc-48d2-f9f0-cc68c6d4c559"
      },
      "outputs": [],
      "source": [
        "# !pip uninstall -y tensorflow --quiet\n",
        "# !pip install git+https://github.com/ludwig-ai/ludwig.git@master --quiet\n",
        "# !pip install \"git+https://github.com/ludwig-ai/ludwig.git@master#egg=ludwig[llm]\" --quiet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2SRHcKAJYuu"
      },
      "source": [
        "Enable text wrapping so we don't have to scroll horizontally and create a function to flush CUDA cache."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Ht4eVWxB13QL"
      },
      "outputs": [],
      "source": [
        "from IPython.display import HTML, display\n",
        "\n",
        "def set_css():\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
        "    }\n",
        "  </style>\n",
        "  '''))\n",
        "\n",
        "get_ipython().events.register('pre_run_cell', set_css)\n",
        "\n",
        "def clear_cache():\n",
        "  if torch.cuda.is_available():\n",
        "    model = None\n",
        "    torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUkPQ9OLP0Po"
      },
      "source": [
        "### **Setup Your HuggingFace Token** 🤗\n",
        "\n",
        "We'll be exploring Llama-2 today, which a model released by Meta. However, the model is not openly-accessible and requires requesting for access (assigned to your HuggingFace token).\n",
        "\n",
        "Obtain a [HuggingFace API Token](https://huggingface.co/settings/tokens) and request access to [Llama2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) before proceeding. You may need to signup on HuggingFace if you don't aleady have an account: https://huggingface.co/join\n",
        "\n",
        "Incase you haven't been given access to Llama-2-7b, that is alright. We can just use Llama-1 for the rest of this example: [huggyllama/llama-7b](https://huggingface.co/huggyllama/llama-7b)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "nXOIxmfbQHSz",
        "outputId": "022e0a33-99cc-42c5-e38e-9df76a42313c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Created a temporary directory at /tmp/tmplyb5hzh1\n",
            "Writing /tmp/tmplyb5hzh1/_remote_module_non_scriptable.py\n"
          ]
        }
      ],
      "source": [
        "import getpass\n",
        "import locale; locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "import logging\n",
        "import os\n",
        "import torch\n",
        "import yaml\n",
        "\n",
        "from ludwig.api import LudwigModel\n",
        "\n",
        "\n",
        "os.environ[\"HUGGING_FACE_HUB_TOKEN\"] = \"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\"\n",
        "assert os.environ[\"HUGGING_FACE_HUB_TOKEN\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sV0D2rjRbk4z"
      },
      "source": [
        "### **Import The Code Generation Dataset** 📋\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "m8FXsCwYbkHh"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np; np.random.seed(123)\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_json(\"/home/ubuntu/lambda_labs/llama2-finetune/output_summarized.json\")\n",
        "\n",
        "# We're going to create a new column called `split` where:\n",
        "# 90% will be assigned a value of 0 -> train set\n",
        "# 5% will be assigned a value of 1 -> validation set\n",
        "# 5% will be assigned a value of 2 -> test set\n",
        "# Calculate the number of rows for each split value\n",
        "total_rows = len(df)\n",
        "split_0_count = int(total_rows * 0.9)\n",
        "split_1_count = int(total_rows * 0.05)\n",
        "split_2_count = total_rows - split_0_count - split_1_count\n",
        "\n",
        "# Create an array with split values based on the counts\n",
        "split_values = np.concatenate([\n",
        "    np.zeros(split_0_count),\n",
        "    np.ones(split_1_count),\n",
        "    np.full(split_2_count, 2)\n",
        "])\n",
        "\n",
        "# Shuffle the array to ensure randomness\n",
        "np.random.shuffle(split_values)\n",
        "\n",
        "# Add the 'split' column to the DataFrame\n",
        "df['split'] = split_values\n",
        "df['split'] = df['split'].astype(int)\n",
        "\n",
        "# For this webinar, we will just 100 rows of this dataset.\n",
        "df = df.head(n=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ew1H1nbDj9_Q"
      },
      "source": [
        "## **Understanding The Code Alpaca Dataset** 📖\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "dAj8Zdr_F-kQ",
        "outputId": "0b19db68-1c97-45cb-dd74-a8e3823b6113"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
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          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instruction</th>\n",
              "      <th>input</th>\n",
              "      <th>output</th>\n",
              "      <th>split</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>what does it mean to have a mental illness?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mental illnesses are health conditions that di...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>who does mental illness affect?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mental illness affects a significant portion o...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>what causes mental illness?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mental illness affects 1 in 5 adults in Americ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>what are some of the warning signs of mental i...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Symptoms of mental health disorders may includ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>can people with mental illness recover?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Early identification and treatment are crucial...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>what should i do if i know someone who appears...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>While this website cannot replace professional...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>how can i find a mental health professional fo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Feeling comfortable with the professional you ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>what treatment options are available?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Just as there are different types of medicatio...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>if i become involved in treatment, what do i n...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Beginning treatment can be overwhelming, so it...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>what is the difference between mental health p...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>There are many types of mental health professi...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         instruction  input  \\\n",
              "0        what does it mean to have a mental illness?    NaN   \n",
              "1                    who does mental illness affect?    NaN   \n",
              "2                        what causes mental illness?    NaN   \n",
              "3  what are some of the warning signs of mental i...    NaN   \n",
              "4            can people with mental illness recover?    NaN   \n",
              "5  what should i do if i know someone who appears...    NaN   \n",
              "6  how can i find a mental health professional fo...    NaN   \n",
              "7              what treatment options are available?    NaN   \n",
              "8  if i become involved in treatment, what do i n...    NaN   \n",
              "9  what is the difference between mental health p...    NaN   \n",
              "\n",
              "                                              output  split  \n",
              "0  Mental illnesses are health conditions that di...      0  \n",
              "1  Mental illness affects a significant portion o...      0  \n",
              "2  Mental illness affects 1 in 5 adults in Americ...      0  \n",
              "3  Symptoms of mental health disorders may includ...      0  \n",
              "4  Early identification and treatment are crucial...      0  \n",
              "5  While this website cannot replace professional...      0  \n",
              "6  Feeling comfortable with the professional you ...      0  \n",
              "7  Just as there are different types of medicatio...      0  \n",
              "8  Beginning treatment can be overwhelming, so it...      0  \n",
              "9  There are many types of mental health professi...      0  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Empty DataFrame\n",
            "Columns: [instruction, input, output, split]\n",
            "Index: []\n"
          ]
        }
      ],
      "source": [
        "# Check for None (NaN) values in 'Column1'\n",
        "none_values = df['output'].isna()\n",
        "\n",
        "# Print rows where 'Column1' has None values\n",
        "rows_with_none_values = df[none_values]\n",
        "print(rows_with_none_values)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gOW5fIpGGKf3"
      },
      "source": [
        "This dataset is meant to train a large language model to following instructions to produce code from natural language. Each row in the dataset consists of an:\n",
        "- `instruction` that describes a task\n",
        "- `input` when additional context is required for the instruction, and\n",
        "- the expected `output`.\n",
        "\n",
        "There are two types of instructions:\n",
        "\n",
        "#### **Type 1: Self-Sufficient**\n",
        "Instructions that are self-sufficient and require no additional context.\n",
        "```\n",
        "Instruction: \"Create an array of length 5 which contains all even numbers between 1 and 10.\"\n",
        "Input: None (instruction is self-sufficient)\n",
        "Output: \"arr = [2, 4, 6, 8, 10]\"\n",
        "```\n",
        "\n",
        "These examples are useful for training models to understand and follow instructions independently. They can be applied in various scenarios, including:\n",
        "\n",
        "- **Coding Tasks**: Teaching a model to generate code based on a clear instruction. For instance, generating a function to sort an array in a specific way.\n",
        "- **Mathematical Calculations**: Instructing a model to perform calculations or equations, such as finding the area of a shape given its dimensions.\n",
        "- **Data Manipulation**: Showing how to process data, like filtering, transforming, or aggregating data based on given criteria.\n",
        "- **Writing Instructions**: Training models to generate instructional content, like recipes, guides, or tutorials.\n",
        "\n",
        "#### **Type 2: Need Context**\n",
        "The instruction needs additional context provided in the `input` to complete the task.\n",
        "```\n",
        "Instruction: \"Write a replace method for a string class which replaces the given string with a given set of characters.\"\n",
        "Input: \"string = 'Hello World!' replace_with = 'Greetings!'\"\n",
        "Output:\n",
        "def replace(string, replace_with):\n",
        "    new_string = \"\"\n",
        "    for char in string:\n",
        "        if char == \" \":\n",
        "            new_string += replace_with\n",
        "        else:\n",
        "            new_string += char\n",
        "    return new_string\n",
        "```\n",
        "\n",
        "These examples are useful for training models to understand both the instruction and relevant context provided by the input. They find application in scenarios such as:\n",
        "\n",
        "- **Customized Code Generation**: Teaching a model to generate code that depends on specific inputs. For instance, creating a function to calculate a mathematical result given input parameters.\n",
        "- **Conditional Responses**: Guiding models to produce outputs that change based on different input conditions (think of a instruction that has different criteria for different letter grades, and then a conditional input like score = 85/100)\n",
        "- **Dynamic Content Creation**: Training models to generate content based on variable input, such as dynamic forms, letters, or responses to user queries.\n",
        "- **Configurable Processes**: Demonstrating how to perform tasks with customizable settings, like configuring a software component using provided parameters.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5eIyMPoSGFAN"
      },
      "source": [
        "As you can see below, the dataset is pretty balanced in terms of the number of examples of each type of instruction (also true for the full dataset with 20,000 rows)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "REqFr-ngx0et",
        "outputId": "5bf3b924-ab95-4c8f-f920-c010e6f62035"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of examples in the dataset: 98\n",
            "% of examples that are self-sufficient: 0.0\n",
            "% of examples that are need additional context: 100.0\n"
          ]
        }
      ],
      "source": [
        "num_self_sufficient = (df['input'] == '').sum()\n",
        "num_need_contex = df.shape[0] - num_self_sufficient\n",
        "\n",
        "# We are only using 100 rows of this dataset for this webinar\n",
        "print(f\"Total number of examples in the dataset: {df.shape[0]}\")\n",
        "\n",
        "print(f\"% of examples that are self-sufficient: {round(num_self_sufficient/df.shape[0] * 100, 2)}\")\n",
        "print(f\"% of examples that are need additional context: {round(num_need_contex/df.shape[0] * 100, 2)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcM8DVrIKXWj"
      },
      "source": [
        "The other aspect worth noting is the average number of characters in each of the three columns `instruction`, `input` and `output` in the dataset. Typically, every 3-4 characters maps to a *token* (the basic building blocks that language models use to understand and analyze text data), and large language models have a limit on the number of tokens they can take as input.\n",
        "\n",
        "The maximum context length for the base LLaMA-2 model is 4096 tokens. Ludwig automatically truncates texts that are too long for the model, but looking at these sequence lengths, we should be able to fine-tune on full length examples without needing any truncation.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "_3x3stdDKoCT",
        "outputId": "83fc8ec0-3698-4e10-89e3-d57a038e5b85"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average number of tokens in the instruction column: 17\n",
            "Average number of tokens in the output column: 234\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Calculating the length of each cell in each column\n",
        "df['num_characters_instruction'] = df['instruction'].apply(lambda x: len(x))\n",
        "# df['num_characters_input'] = df['input'].apply(lambda x: len(x))\n",
        "df['num_characters_output'] = df['output'].apply(lambda x: len(x))\n",
        "\n",
        "# Show Distribution\n",
        "# df.hist(column=['num_characters_instruction', 'num_characters_input', 'num_characters_output'])\n",
        "df.hist(column=['num_characters_instruction', 'num_characters_output'])\n",
        "\n",
        "# Calculating the average\n",
        "average_chars_instruction = df['num_characters_instruction'].mean()\n",
        "# average_chars_input = df['num_characters_input'].mean()\n",
        "average_chars_output = df['num_characters_output'].mean()\n",
        "\n",
        "print(f'Average number of tokens in the instruction column: {(average_chars_instruction / 3):.0f}')\n",
        "# print(f'Average number of tokens in the input column: {(average_chars_input / 3):.0f}')\n",
        "print(f'Average number of tokens in the output column: {(average_chars_output / 3):.0f}', end=\"\\n\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyAqJgqTGP0M"
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      "source": [
        "## **Prompt Templating and Zero Shot Inference**\n",
        "\n",
        "**Zero-shot learning is a capability enabled by Large Language Models, allowing them to generalize to tasks or domains they have never been explicitly trained on.** This approach leverages the inherent knowledge and linguistic understanding encoded within the model during its pretraining phase.\n",
        "\n",
        "Zero-shot learning involves presenting the model with a task description or prompt, along with some context, and expecting it to generate a relevant response or output. The key idea is that the model can understand and generate coherent content even for tasks it hasn't been explicitly fine-tuned for.\n",
        "\n",
        "<img src=\"https://ludwig.ai/latest/images/icl_zero_shot_learning.png\" alt=\"ICL\" width=\"650\"/>\n",
        "\n",
        "In Ludwig, there are two parameters we can use to control prompting and the quality of generation when trying to use the LLM for zero-shot inference:\n",
        "- `prompt`: the prompt parameter can be used to\n",
        "  - Provide necessary boilerplate needed to make the LLM respond in the correct way (for example, with a response to a question rather than a continuation of the input sequence).\n",
        "  - Combine multiple columns from a dataset into a single text input feature.\n",
        "  - Provide additional context to the model that can help it understand the task, or provide restrictions to prevent hallucinations (producing false information confidently). <br>\n",
        "\n",
        "  All of this can be configured through the nested keyword called `template`. The template allows regular text to describe the task, but also allows two special group of keywords that can be used:\n",
        "  - **Reserved Keywords**: `{__sample__}`, `{__task__}` and `{__context__}`\n",
        "  - **Feature Names**: If you have additional feature names you want to combine into your prompt, you can add them using the same `{}` template without the `__`. For e.g., in our case, we have three columns: `instruction`, `input` and `output`. We can refer to the `instruction` and `input` data via the `{instruction}` and `{input}`. We will see this in our example below.\n",
        "\n",
        "  See the full docs for prompt [here](https://ludwig.ai/latest/configuration/large_language_model/#prompt).\n",
        "\n",
        "- `generation`: You may often want to control the generation process, such as what token decoding strategy to use, how many new tokens to produce, which tokens to exclude, or how diverse you want the generated text to be. See the full docs [here](https://ludwig.ai/latest/configuration/large_language_model/#generation).\n",
        "\n",
        "Let's try and perform some zero-shot prompting with Ludwig to see how the model does on our task out of the box."
      ]
    },
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        {
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            "\n",
            "╒════════════════════════╕\n",
            "│ EXPERIMENT DESCRIPTION │\n",
            "╘════════════════════════╛\n",
            "\n",
            "╒══════════════════╤══════════════════════════════════════════════════════════════════════════════════════════════════╕\n",
            "│ Experiment name  │ api_experiment                                                                                   │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ Model name       │ run                                                                                              │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ Output directory │ /home/ubuntu/lambda_labs/llama2-finetune/results/api_experiment_run_19                           │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ ludwig_version   │ '0.8'                                                                                            │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ command          │ ('/home/ubuntu/miniconda/envs/env_langchain/lib/python3.10/site-packages/ipykernel_launcher.py ' │\n",
            "│                  │  '--ip=127.0.0.1 --stdin=9003 --control=9001 --hb=9000 '                                         │\n",
            "│                  │  '--Session.signature_scheme=\"hmac-sha256\" '                                                     │\n",
            "│                  │  '--Session.key=b\"a5c8c993-efaa-4141-b6a2-d8cfa048803b\" --shell=9002 '                           │\n",
            "│                  │  '--transport=\"tcp\" --iopub=9004 '                                                               │\n",
            "│                  │  '--f=/home/ubuntu/.local/share/jupyter/runtime/kernel-v2-1504734War51guZBLem.json')             │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ random_seed      │ 42                                                                                               │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ data_format      │ \"<class 'pandas.core.frame.DataFrame'>\"                                                          │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ torch_version    │ '2.0.1+cu118'                                                                                    │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ compute          │ {'gpu_type': 'NVIDIA H100 PCIe', 'gpus_per_node': 1, 'num_nodes': 1}                             │\n",
            "╘══════════════════╧══════════════════════════════════════════════════════════════════════════════════════════════════╛\n",
            "\n",
            "╒═══════════════╕\n",
            "│ LUDWIG CONFIG │\n",
            "╘═══════════════╛\n",
            "\n",
            "User-specified config (with upgrades):\n",
            "\n",
            "{   'base_model': 'meta-llama/Llama-2-7b-hf',\n",
            "    'generation': {'max_new_tokens': 512, 'temperature': 0.1},\n",
            "    'input_features': [{'name': 'instruction', 'type': 'text'}],\n",
            "    'ludwig_version': '0.8',\n",
            "    'model_type': 'llm',\n",
            "    'output_features': [{'name': 'output', 'type': 'text'}],\n",
            "    'preprocessing': {'split': {'type': 'fixed'}},\n",
            "    'prompt': {   'template': 'Below is an instruction that describes a task, '\n",
            "                              'paired with an input that may provide further '\n",
            "                              'context. Write a response that appropriately '\n",
            "                              'completes the request.\\n'\n",
            "                              '### Instruction: {instruction}\\n'\n",
            "                              '### Input: {input}\\n'\n",
            "                              '### Response:'},\n",
            "    'quantization': {'bits': 4}}\n",
            "\n",
            "Full config saved to:\n",
            "/home/ubuntu/lambda_labs/llama2-finetune/results/api_experiment_run_19/api_experiment/model/model_hyperparameters.json\n",
            "\n",
            "╒═══════════════╕\n",
            "│ PREPROCESSING │\n",
            "╘═══════════════╛\n",
            "\n",
            "No cached dataset found at /home/ubuntu/lambda_labs/llama2-finetune/6267993c4b8411eeaf79de647818c81d.training.hdf5. Preprocessing the dataset.\n",
            "Using full dataframe\n",
            "Building dataset (it may take a while)\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-7b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max length of feature 'None': 109 (without start and stop symbols)\n",
            "Setting max length using dataset: 111 (including start and stop symbols)\n",
            "max sequence length is 111 for feature 'None'\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-7b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max length of feature 'output': 371 (without start and stop symbols)\n",
            "Setting max length using dataset: 373 (including start and stop symbols)\n",
            "max sequence length is 373 for feature 'output'\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-7b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded HuggingFace implementation of meta-llama/Llama-2-7b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Building dataset: DONE\n",
            "Writing preprocessed training set cache to /home/ubuntu/lambda_labs/llama2-finetune/6267993c4b8411eeaf79de647818c81d.training.hdf5\n",
            "Writing preprocessed validation set cache to /home/ubuntu/lambda_labs/llama2-finetune/6267993c4b8411eeaf79de647818c81d.validation.hdf5\n",
            "Writing preprocessed test set cache to /home/ubuntu/lambda_labs/llama2-finetune/6267993c4b8411eeaf79de647818c81d.test.hdf5\n",
            "Writing train set metadata to /home/ubuntu/lambda_labs/llama2-finetune/6267993c4b8411eeaf79de647818c81d.meta.json\n",
            "\n",
            "Dataset Statistics\n",
            "╒════════════╤═══════════════╤════════════════════╕\n",
            "│ Dataset    │   Size (Rows) │ Size (In Memory)   │\n",
            "╞════════════╪═══════════════╪════════════════════╡\n",
            "│ Training   │            88 │ 20.75 Kb           │\n",
            "├────────────┼───────────────┼────────────────────┤\n",
            "│ Validation │             4 │ 1.06 Kb            │\n",
            "├────────────┼───────────────┼────────────────────┤\n",
            "│ Test       │             6 │ 1.53 Kb            │\n",
            "╘════════════╧═══════════════╧════════════════════╛\n",
            "\n",
            "╒═══════╕\n",
            "│ MODEL │\n",
            "╘═══════╛\n",
            "\n",
            "Warnings and other logs:\n",
            "Loading large language model...\n",
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /home/ubuntu/miniconda/envs/env_langchain/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA SETUP: CUDA runtime path found: /usr/lib/x86_64-linux-gnu/libcudart.so.11.0\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 9.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /home/ubuntu/miniconda/envs/env_langchain/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n",
            "We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e9241a22c6b4190aeed2b0215401ae1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Done.\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-7b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n",
            "\n",
            "╒══════════╕\n",
            "│ TRAINING │\n",
            "╘══════════╛\n",
            "\n",
            "\n",
            "Running evaluation for step: 0, epoch: 0\n",
            "Evaluation valid:   0%|          | 0/4 [00:00<?, ?it/s]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: where can i go to find therapy?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: where can i go to find therapy?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: nan\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input: i am looking for a way to get to the airport\n",
            "### response:\n",
            "### instruction: what is the best way to get to the airport?\n",
            "### input\n",
            "Evaluation valid:  25%|██▌       | 1/4 [00:29<01:27, 29.05s/it]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: where can i go to find other local services?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: where can i go to find other local services?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other local services?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: where can i go to find other\n",
            "Evaluation valid:  50%|█████     | 2/4 [00:53<00:52, 26.30s/it]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what do i do if the support doesn't help?\n",
            "### input:\n",
            "### response\n",
            "Evaluation valid:  75%|███████▌  | 3/4 [01:17<00:25, 25.43s/it]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what's the difference between anxiety and an anxiety disorder?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what's the difference between anxiety and an anxiety disorder?\n",
            "### input: nan\n",
            "### response:\n",
            "### anxiety is a normal human emotion that is experienced by everyone at some point in their lives. Anxiety disorders, on the other hand, are mental health conditions that cause people to feel excessive worry, fear, or dread for no apparent reason. These disorders can be debilitating and can interfere with a person's ability to function normally in their daily life.\n",
            "### anxiety is a normal human emotion that is experienced by everyone at some point in their lives. Anxiety disorders, on the other hand, are mental health conditions that cause people to feel excessive worry, fear, or dread for no apparent reason. These disorders can be debilitating and can interfere with a person's ability to function normally in their daily life.\n",
            "### anxiety is a normal human emotion that is experienced by everyone at some point in their lives. Anxiety disorders, on the other hand, are mental health conditions that cause people to feel excessive worry, fear, or dread for no apparent reason. These disorders can be debilitating and can interfere with a person's ability to function normally in their daily life.\n",
            "### anxiety is a normal human emotion that is experienced by everyone at some point in their lives. Anxiety disorders, on the other hand, are mental health conditions that cause people to feel excessive worry, fear, or dread for no apparent reason. These disorders can be debilitating and can interfere with a person's ability to function normally in their daily life.\n",
            "### anxiety is a normal human emotion that is experienced by everyone at some point in their lives. Anxiety disorders, on the other hand, are mental health conditions that cause people to feel excessive worry, fear, or dread for no apparent reason. These disorders can be debilitating and can interfere with a person's ability to function normally in their daily life.\n",
            "### anxiety is a normal human emotion that is experienced by everyone at some point in their lives. Anxiety disorders, on the other hand, are mental health conditions that cause people to feel excessive worry, fear, or dread for no apparent reason. These disorders can be debilitating and can interfere with a person's ability to function normally in their daily life.\n",
            "### anxiety is a normal\n",
            "Evaluation valid: 100%|██████████| 4/4 [01:42<00:00, 25.54s/it]\n",
            "Evaluation test :   0%|          | 0/6 [00:00<?, ?it/s]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what causes mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what causes mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to treat mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to prevent mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the best way to support people with mental health problems?\n",
            "### input:\n",
            "Evaluation test :  17%|█▋        | 1/6 [00:24<02:01, 24.27s/it]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i maintain social connections?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: how can i maintain social connections? what if i feel lonely?\n",
            "### input:\n",
            "### response:\n",
            "### instruction\n",
            "Evaluation test :  33%|███▎      | 2/6 [00:48<01:37, 24.28s/it]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: how can i take care of my physical health?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: how can i take care of my physical health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my mental health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my spiritual health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my social health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my financial health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my emotional health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my intellectual health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my environmental health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my occupational health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my physical health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my mental health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my spiritual health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my social health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my financial health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my emotional health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my intellectual health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my environmental health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my occupational health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my physical health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my mental health?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: how can i take care of my spiritual health\n",
            "Evaluation test :  50%|█████     | 3/6 [01:12<01:12, 24.28s/it]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what's the difference between anxiety and stress?\n",
            "##\n",
            "Evaluation test :  67%|██████▋   | 4/6 [01:37<00:48, 24.30s/it]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what's the difference between dissociative identity disorder (multiple personality disorder) and schizophrenia?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what's the difference between dissociative identity disorder (multiple personality disorder) and schizophrenia?\n",
            "### input: nan\n",
            "### response:\n",
            "### schizophrenia is a mental disorder that is characterized by a breakdown in the relation between thought, emotion, and behavior.\n",
            "### multiple personality disorder is a mental disorder that is characterized by the presence of two or more distinct identities or personality states.\n",
            "### schizophrenia is a mental disorder that is characterized by a breakdown in the relation between thought, emotion, and behavior.\n",
            "### multiple personality disorder is a mental disorder that is characterized by the presence of two or more distinct identities or personality states.\n",
            "### schizophrenia is a mental disorder that is characterized by a breakdown in the relation between thought, emotion, and behavior.\n",
            "### multiple personality disorder is a mental disorder that is characterized by the presence of two or more distinct identities or personality states.\n",
            "### schizophrenia is a mental disorder that is characterized by a breakdown in the relation between thought, emotion, and behavior.\n",
            "### multiple personality disorder is a mental disorder that is characterized by the presence of two or more distinct identities or personality states.\n",
            "### schizophrenia is a mental disorder that is characterized by a breakdown in the relation between thought, emotion, and behavior.\n",
            "### multiple personality disorder is a mental disorder that is characterized by the presence of two or more distinct identities or personality states.\n",
            "### schizophrenia is a mental disorder that is characterized by a breakdown in the relation between thought, emotion, and behavior.\n",
            "### multiple personality disorder is a mental disorder that is characterized by the presence of two or more distinct identities or personality states.\n",
            "### schizophrenia is a mental disorder that is characterized by a breakdown in the relation between thought, emotion, and behavior.\n",
            "### multiple personality disorder is a mental disorder that is characterized by the presence of two or more distinct identities or personality states.\n",
            "### schizophrenia is a mental disorder that is characterized by a breakdown in the relation between thought, emotion, and behavior.\n",
            "### multiple personality disorder is a mental disorder that is characterized by the presence of two or more\n",
            "Evaluation test :  83%|████████▎ | 5/6 [02:01<00:24, 24.32s/it]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input: nan\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that may provide further context. write a response that appropriately completes the request.\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input: nan\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "### input:\n",
            "### response:\n",
            "### instruction: what is the legal status (and evidence) of cbd oil?\n",
            "Evaluation test : 100%|██████████| 6/6 [02:26<00:00, 24.46s/it]\n",
            "Evaluation took 4m 8.9528s\n",
            "\n",
            "╒═══════════════════════╤═════════╤══════════════╤════════════╕\n",
            "│                       │ train   │   validation │       test │\n",
            "╞═══════════════════════╪═════════╪══════════════╪════════════╡\n",
            "│ bleu                  │         │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │         │       0.9956 │     0.9990 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ loss                  │         │      10.3735 │    10.3735 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │         │   32000.0645 │ 32000.0645 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ perplexity            │         │   32000.0039 │ 31999.9727 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │         │       0.0884 │     0.0708 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │         │       0.0607 │     0.0516 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │         │       0.1858 │     0.1854 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │         │       0.0093 │     0.0048 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │         │       0.0068 │     0.0030 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │         │       0.0149 │     0.0297 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │         │       0.0662 │     0.0699 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │         │       0.0445 │     0.0509 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │         │       0.1503 │     0.1840 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │         │       0.0658 │     0.0465 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │         │       0.0453 │     0.0338 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │         │       0.1371 │     0.1328 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │         │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │         │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │         │       4.0900 │     3.3509 │\n",
            "├───────────────────────┼─────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │         │      10.3735 │    10.3735 │\n",
            "╘═══════════════════════╧═════════╧══════════════╧════════════╛\n",
            "Skipped saving LLM without weight adjustments.\n",
            "\n",
            "╒═════════════════╕\n",
            "│ TRAINING REPORT │\n",
            "╘═════════════════╛\n",
            "\n",
            "╒══════════════════════════════╤════════════════════╕\n",
            "│ Validation feature           │ output             │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Validation metric            │ loss               │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model step              │ 0                  │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model epoch             │ 1                  │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model's validation loss │ 10.373493194580078 │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model's test loss       │ 10.373493194580078 │\n",
            "╘══════════════════════════════╧════════════════════╛\n",
            "\n",
            "Finished: api_experiment_run\n",
            "Saved to: /home/ubuntu/lambda_labs/llama2-finetune/results/api_experiment_run_19\n",
            "Skipped saving LLM without weight adjustments.\n",
            "\n",
            "╒══════════╕\n",
            "│ FINISHED │\n",
            "╘══════════╛\n",
            "\n"
          ]
        }
      ],
      "source": [
        "zero_shot_config = yaml.safe_load(\n",
        "  \"\"\"\n",
        "  model_type: llm\n",
        "  base_model: meta-llama/Llama-2-7b-hf\n",
        "\n",
        "  input_features:\n",
        "    - name: instruction\n",
        "      type: text\n",
        "\n",
        "  output_features:\n",
        "    - name: output\n",
        "      type: text\n",
        "\n",
        "  prompt:\n",
        "    template: >-\n",
        "      Below is an instruction that describes a task, paired with an input\n",
        "      that may provide further context. Write a response that appropriately\n",
        "      completes the request.\n",
        "\n",
        "      ### Instruction: {instruction}\n",
        "\n",
        "      ### Input: {input}\n",
        "\n",
        "      ### Response:\n",
        "\n",
        "  generation:\n",
        "    temperature: 0.1 # Temperature is used to control the randomness of predictions.\n",
        "    max_new_tokens: 512\n",
        "\n",
        "  preprocessing:\n",
        "    split:\n",
        "      type: fixed\n",
        "\n",
        "  quantization:\n",
        "    bits: 4\n",
        "  \"\"\"\n",
        ")\n",
        "\n",
        "# Just run on 10 examples for now\n",
        "model = LudwigModel(config=zero_shot_config, logging_level=logging.INFO)\n",
        "results = model.train(dataset=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_w0u2WbymJk"
      },
      "source": [
        "#### **Observations From Zero Shot Prompting Results** 🔍\n",
        "1. The base Llama-2 model is generally good at producing coherent English text as responses.\n",
        "2. When it doesn't know how to respond (which seems to be true most of the times we want it to follow an instruction), it just returns the input over and over again until the token limit is reached. Sometimes it even modifies the original instruction in the process.\n",
        "3. It doesn't know when to stop producing a response, i.e., it gets confused and just produces till we set a hard stop through the number of maximum allowed tokens. In an ideal scenario, the probability distribution being used to generate the next token predicts a stop token at the right point in time.\n",
        "4. It doesn't get even one of the input prompts correct.\n",
        "\n",
        "⭐ **None of these results are surprising, but it goes to show that while a model like Llama-2 can be good on general tasks, it can often be very poor at performing domain specific tasks out of the box just through zero shot/regular prompting (pretrained knowledge without any additional context)** ⭐"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Vr_dXNLGn9o"
      },
      "source": [
        "# **Declaratively Fine-Tune Large Language Models**\n",
        "\n",
        "Fine-tuning a large language model **refers to the process of further training the pre-trained model on a specific task or domain using a smaller dataset.** The initial pre-training phase involves training a language model on a massive corpus of text data to learn general language patterns and representations. Fine-tuning, on the other hand, customizes the model to a specific task or domain by exposing it to task-specific data. By fine-tuning a large language model on a specific task, you leverage the pre-trained knowledge of the model while tailoring it to the nuances and requirements of your target task. This typically allows the model to perform better and achieve higher accuracy on the specific task compared to using the pretrained model by itself for your specific task.\n",
        "\n",
        "#### ▶ **What is instruction following/tuning? Why should I do it?**\n",
        "\n",
        "Pre-trained language models are often great at giving general answers, but they struggle when asked to follow specific instructions, especially for tasks in certain domains. To make a pre-trained model do better at these specific tasks, we can train it on examples of those tasks. This is called instruction fine-tuning. We use a dataset with pairs of `{instructions, outputs}` in the domain, and this helps the model learn to give the right response when given those types of instructions. This training process typically changes the underlying model weights, but there are also other ways to train it without doing this. When done correctly, this process teaches the model to understand and follow instructions it couldn't handle well before.\n",
        "\n",
        "#### ▶ **What will this teach the model?**\n",
        "\n",
        "Here's an example of prompting the base model and an example prompting the fine-tuned model. The model was given all of the text until `Response:`, and it was supposed to continue generating an appropriate response.\n",
        "\n",
        "**Using the base model (no fine-tuning):** 👎\n",
        "```\n",
        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction: create an array of length 5 which contains all even numbers between 1 and 10.\n",
        "\n",
        "### Response:\\n\\n### Instruction: create an array of length 5 which contains all even numbers between 1 and 10.\\n\\n### Input: \\n\\n### Response:\\n\\n### Instruction: create an array of length 5 which contains all even numbers between 1 and 10.\\n\\n### Input: \\n\\n### Response:\\n\\n### Instruction: create an array of length 5 which contains all even numbers between 1 and 10.\\n\\n### Input: \\n\\n### Response:\\n\\n### Instruction: create an\n",
        "```\n",
        "\n",
        "**After instruction-fine-tuning:** 👍\n",
        "```\n",
        "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction: create an array of length 5 which contains all even numbers between 1 and 10.\n",
        "\n",
        "### Response: array = [2, 4, 6, 8, 10]\n",
        "```\n",
        "\n",
        "The base model does not know how to follow-instructions and answer the question for our task, so just repeats the inputs we passed in until the token limit is hit. Our fine-tuned model should be able to respond back correctly (these are actual outputs from a model we fine-tuned).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oU8iKtqwCc1b"
      },
      "source": [
        "There are three different fine-tuning approaches in Ludwig:\n",
        "\n",
        "1. **Full Fine-Tuning**:\n",
        "\n",
        "- Involves training the entire pre-trained model on new data from scratch.\n",
        "- All model layers and parameters are updated during fine-tuning.\n",
        "- Can lead to high accuracy but requires a significant amount of computational resources and time.\n",
        "- Runs the risk of catastrophic forgetting: occasionally, since we are updating all of the weights in the model, this process can lead to the algorithm inadvertently losing knowledge of its past tasks, i.e., the knowledge it gained during pretraining. The outcome may vary, with the algorithm experiencing heightened error margins in some cases, while in others, it might completely erase the memory of a specific task leading to terrible performance.\n",
        "- Best suited when the target task is significantly different from the original pre-training task.\n",
        "\n",
        "2. **Parameter Efficient Fine-Tuning (PEFT), e.g. LoRA**:\n",
        "\n",
        "- Focuses on updating only a subset of the model's parameters.\n",
        "- Often involves freezing certain layers or parts of the model to avoid catastrophic forgetting, or inserting additional layers that are trainable while keeping the original model's weights frozen.\n",
        "- Can result in faster fine-tuning with fewer computational resources, but might sacrifice some accuracy compared to full fine-tuning.\n",
        "- Includes methods like LoRA, AdaLoRA and Adaption Prompt (LLaMA Adapter)\n",
        "- Suitable when the new task shares similarities with the original pre-training task.\n",
        "\n",
        "3. **Quantization-Based Fine-Tuning (QLoRA)**:\n",
        "\n",
        "- Involves reducing the precision of model parameters (e.g., converting 32-bit floating-point values to 8-bit or 4-bit integers). This reduces the amount of CPU and GPU memory required by either 4x if using 8-bit integers, or 8x if using 4-bit integers.\n",
        "- Typically, since we're changing the weights to 8 or 4 bit integers, we will lose some precision/performance.\n",
        "- This can lead to reduced memory usage and faster inference on hardware with reduced precision support.\n",
        "- Particularly useful when deploying models on resource-constrained devices, such as mobile phones or edge devices.\n",
        "\n",
        "\n",
        "**Today, we're going to fine-tune using method 3 since we only have access to a single T4 GPU with 16GiB of GPU VRAM on Colab.** If you have more compute available, give LoRA based fine-tuning or full fine-tuning a try! Typically this requires 4 GPUs with 24GiB of GPU VRAM on a single node multi-GPU cluster and fine-tuning Deepspeeed.\n",
        "\n",
        "\n",
        "To do this, the new parameters we're introducing are:\n",
        "\n",
        "- `adapter`: The PEFT method we want to use\n",
        "- `quantization`: Load the weights in int4 or int8 to reduce memory overhead.\n",
        "- `trainer`: We enable the `finetune` trainer and can configure a variety of training parameters such as epochs and learning rate."
      ]
    },
    {
      "cell_type": "code",
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            "d527f4f6775a4da4b1df9affa4b8eb33"
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        "outputId": "37bc626f-09d4-4849-bf03-e9e2028b059a"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <style>\n",
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            "\n",
            "╒════════════════════════╕\n",
            "│ EXPERIMENT DESCRIPTION │\n",
            "╘════════════════════════╛\n",
            "\n",
            "╒══════════════════╤══════════════════════════════════════════════════════════════════════════════════════════════════╕\n",
            "│ Experiment name  │ api_experiment                                                                                   │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ Model name       │ run                                                                                              │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ Output directory │ /home/ubuntu/lambda_labs/llama2-finetune/results/api_experiment_run_20                           │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ ludwig_version   │ '0.8'                                                                                            │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ command          │ ('/home/ubuntu/miniconda/envs/env_langchain/lib/python3.10/site-packages/ipykernel_launcher.py ' │\n",
            "│                  │  '--ip=127.0.0.1 --stdin=9003 --control=9001 --hb=9000 '                                         │\n",
            "│                  │  '--Session.signature_scheme=\"hmac-sha256\" '                                                     │\n",
            "│                  │  '--Session.key=b\"a5c8c993-efaa-4141-b6a2-d8cfa048803b\" --shell=9002 '                           │\n",
            "│                  │  '--transport=\"tcp\" --iopub=9004 '                                                               │\n",
            "│                  │  '--f=/home/ubuntu/.local/share/jupyter/runtime/kernel-v2-1504734War51guZBLem.json')             │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ random_seed      │ 42                                                                                               │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ data_format      │ \"<class 'pandas.core.frame.DataFrame'>\"                                                          │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ torch_version    │ '2.0.1+cu118'                                                                                    │\n",
            "├──────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ compute          │ {'gpu_type': 'NVIDIA H100 PCIe', 'gpus_per_node': 1, 'num_nodes': 1}                             │\n",
            "╘══════════════════╧══════════════════════════════════════════════════════════════════════════════════════════════════╛\n",
            "\n",
            "╒═══════════════╕\n",
            "│ LUDWIG CONFIG │\n",
            "╘═══════════════╛\n",
            "\n",
            "User-specified config (with upgrades):\n",
            "\n",
            "{   'adapter': {'type': 'lora'},\n",
            "    'base_model': 'meta-llama/Llama-2-13b-hf',\n",
            "    'generation': {'max_new_tokens': 2048, 'temperature': 0.1},\n",
            "    'input_features': [{'name': 'instruction', 'type': 'text'}],\n",
            "    'ludwig_version': '0.8',\n",
            "    'model_type': 'llm',\n",
            "    'output_features': [{'name': 'output', 'type': 'text'}],\n",
            "    'prompt': {   'template': 'Below is an instruction that describes a task, '\n",
            "                              'paired with an input that provides further '\n",
            "                              'context. Write a response that appropriately '\n",
            "                              'completes the request.\\n'\n",
            "                              '### Instruction: {instruction}\\n'\n",
            "                              '### Input: {input}\\n'\n",
            "                              '### Response:'},\n",
            "    'quantization': {'bits': 4},\n",
            "    'trainer': {   'batch_size': 1,\n",
            "                   'epochs': 50,\n",
            "                   'eval_batch_size': 2,\n",
            "                   'gradient_accumulation_steps': 16,\n",
            "                   'learning_rate': 0.0001,\n",
            "                   'learning_rate_scheduler': {   'reduce_on_plateau': 0,\n",
            "                                                  'warmup_fraction': 0.03},\n",
            "                   'optimizer': {   'params': {   'betas': [0.9, 0.999],\n",
            "                                                  'eps': 1e-08,\n",
            "                                                  'weight_decay': 0},\n",
            "                                    'type': 'adam'},\n",
            "                   'type': 'finetune'}}\n",
            "\n",
            "Full config saved to:\n",
            "/home/ubuntu/lambda_labs/llama2-finetune/results/api_experiment_run_20/api_experiment/model/model_hyperparameters.json\n",
            "\n",
            "╒═══════════════╕\n",
            "│ PREPROCESSING │\n",
            "╘═══════════════╛\n",
            "\n",
            "No cached dataset found at /home/ubuntu/lambda_labs/llama2-finetune/0d8897624b8511eeaf79de647818c81d.training.hdf5. Preprocessing the dataset.\n",
            "Using full dataframe\n",
            "Building dataset (it may take a while)\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-13b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max length of feature 'None': 108 (without start and stop symbols)\n",
            "Setting max length using dataset: 110 (including start and stop symbols)\n",
            "max sequence length is 110 for feature 'None'\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-13b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max length of feature 'output': 371 (without start and stop symbols)\n",
            "Setting max length using dataset: 373 (including start and stop symbols)\n",
            "max sequence length is 373 for feature 'output'\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-13b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded HuggingFace implementation of meta-llama/Llama-2-13b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Building dataset: DONE\n",
            "Writing preprocessed training set cache to /home/ubuntu/lambda_labs/llama2-finetune/0d8897624b8511eeaf79de647818c81d.training.hdf5\n",
            "Writing preprocessed validation set cache to /home/ubuntu/lambda_labs/llama2-finetune/0d8897624b8511eeaf79de647818c81d.validation.hdf5\n",
            "Writing preprocessed test set cache to /home/ubuntu/lambda_labs/llama2-finetune/0d8897624b8511eeaf79de647818c81d.test.hdf5\n",
            "Writing train set metadata to /home/ubuntu/lambda_labs/llama2-finetune/0d8897624b8511eeaf79de647818c81d.meta.json\n",
            "\n",
            "Dataset Statistics\n",
            "╒════════════╤═══════════════╤════════════════════╕\n",
            "│ Dataset    │   Size (Rows) │ Size (In Memory)   │\n",
            "╞════════════╪═══════════════╪════════════════════╡\n",
            "│ Training   │            69 │ 16.30 Kb           │\n",
            "├────────────┼───────────────┼────────────────────┤\n",
            "│ Validation │             9 │ 2.23 Kb            │\n",
            "├────────────┼───────────────┼────────────────────┤\n",
            "│ Test       │            20 │ 4.81 Kb            │\n",
            "╘════════════╧═══════════════╧════════════════════╛\n",
            "\n",
            "╒═══════╕\n",
            "│ MODEL │\n",
            "╘═══════╛\n",
            "\n",
            "Warnings and other logs:\n",
            "Loading large language model...\n",
            "We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "67f29555a2094b6ba3e107fafb5c2de7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Done.\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-13b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n",
            "==================================================\n",
            "Trainable Parameter Summary For Fine-Tuning\n",
            "Fine-tuning with adapter: lora\n",
            "trainable params: 6,553,600 || all params: 13,022,417,920 || trainable%: 0.05032552357220002\n",
            "==================================================\n",
            "\n",
            "╒══════════╕\n",
            "│ TRAINING │\n",
            "╘══════════╛\n",
            "\n",
            "Creating fresh model training run.\n",
            "Training for 3450 step(s), approximately 50 epoch(s).\n",
            "Early stopping policy: 5 round(s) of evaluation, or 345 step(s), approximately 5 epoch(s).\n",
            "\n",
            "Starting with step 0, epoch: 0\n",
            "Training:   2%|▏         | 68/3450 [00:28<19:09,  2.94it/s, loss=0.126]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:   2%|▏         | 69/3450 [00:28<24:25,  2.31it/s, loss=0.154]\n",
            "Running evaluation for step: 69, epoch: 1\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.32s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.76s/it]\n",
            "Evaluation took 29.2279s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0000 │       0.1375 │     0.0739 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.7745 │       0.6873 │     0.7367 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     2.4565 │       2.3974 │     2.6852 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 21991.7480 │   21646.3984 │ 22609.8574 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 32000.0645 │   31974.0723 │ 31920.6934 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.4299 │       0.4609 │     0.4242 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.3433 │       0.4426 │     0.3985 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.5750 │       0.4857 │     0.4570 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1905 │       0.1492 │     0.1151 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1515 │       0.1407 │     0.1065 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.2564 │       0.1594 │     0.1258 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.3738 │       0.3386 │     0.3095 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.2985 │       0.3229 │     0.2888 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.5000 │       0.3588 │     0.3359 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.3925 │       0.4306 │     0.3962 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.3134 │       0.4139 │     0.3719 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.5250 │       0.4529 │     0.4271 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0006 │     0.0009 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     1.3500 │       0.8526 │     0.9346 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     2.4565 │       2.3974 │     2.6852 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Evaluation validation metric: 'output' 'loss' improved.\n",
            "'output' 'loss' decreased by inf.\n",
            "New best model saved.\n",
            "\n",
            "Training:   4%|▍         | 136/3450 [01:19<17:14,  3.20it/s, loss=0.117]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:   4%|▍         | 138/3450 [01:21<37:52,  1.46it/s, loss=0.144]\n",
            "Running evaluation for step: 138, epoch: 2\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.26s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.74s/it]\n",
            "Evaluation took 28.7619s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0628 │       0.1363 │     0.0801 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.6307 │       0.6841 │     0.7357 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     2.2993 │       2.3762 │     2.6604 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 21847.1367 │   21581.5820 │ 22527.7461 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 32000.7363 │   31973.8906 │ 31920.7852 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.4339 │       0.4684 │     0.4262 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.3975 │       0.4498 │     0.4004 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.4776 │       0.4933 │     0.4590 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1502 │       0.1553 │     0.1175 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1375 │       0.1465 │     0.1087 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.1654 │       0.1659 │     0.1284 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.3525 │       0.3434 │     0.3116 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.3230 │       0.3276 │     0.2907 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.3881 │       0.3638 │     0.3381 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.4271 │       0.4397 │     0.4001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.3913 │       0.4229 │     0.3757 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.4701 │       0.4623 │     0.4312 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0006 │     0.0009 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.8864 │       0.8509 │     0.9283 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     2.2993 │       2.3762 │     2.6604 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Evaluation validation metric: 'output' 'loss' improved.\n",
            "'output' 'loss' decreased by 0.021.\n",
            "New best model saved.\n",
            "\n",
            "Training:   6%|▌         | 204/3450 [02:13<18:15,  2.96it/s, loss=0.103]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:   6%|▌         | 207/3450 [02:15<33:27,  1.62it/s, loss=0.09]  \n",
            "Running evaluation for step: 207, epoch: 3\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.37s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.75s/it]\n",
            "Evaluation took 29.3559s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1837 │       0.1356 │     0.0871 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.5114 │       0.6813 │     0.7334 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.4400 │       2.3397 │     2.6181 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 18545.1719 │   21452.1445 │ 22386.3008 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31803.9805 │   31973.9824 │ 31920.8164 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.5846 │       0.4719 │     0.4282 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.5315 │       0.4525 │     0.4008 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.6496 │       0.4976 │     0.4632 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.3023 │       0.1537 │     0.1179 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.2746 │       0.1446 │     0.1088 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.3362 │       0.1648 │     0.1294 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.5154 │       0.3428 │     0.3124 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.4685 │       0.3264 │     0.2904 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.5726 │       0.3637 │     0.3404 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.5692 │       0.4531 │     0.4005 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.5175 │       0.4342 │     0.3746 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.6325 │       0.4780 │     0.4334 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0046 │       0.0006 │     0.0009 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.7798 │       0.8457 │     0.9247 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.4400 │       2.3397 │     2.6181 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Evaluation validation metric: 'output' 'loss' improved.\n",
            "'output' 'loss' decreased by 0.037.\n",
            "New best model saved.\n",
            "\n",
            "Training:   8%|▊         | 272/3450 [03:07<20:12,  2.62it/s, loss=0.163]   Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:   8%|▊         | 276/3450 [03:10<42:20,  1.25it/s, loss=0.103]\n",
            "Running evaluation for step: 276, epoch: 4\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.33s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.74s/it]\n",
            "Evaluation took 29.1055s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1119 │       0.1296 │     0.0916 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.5487 │       0.6762 │     0.7273 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.6552 │       2.2881 │     2.5576 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 19841.7402 │   21246.8438 │ 22185.5977 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 32000.0645 │   31974.2266 │ 31920.7559 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.5944 │       0.4726 │     0.4337 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.5661 │       0.4507 │     0.4039 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.6257 │       0.5015 │     0.4717 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.2067 │       0.1480 │     0.1272 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1968 │       0.1384 │     0.1169 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.2176 │       0.1599 │     0.1402 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.4389 │       0.3405 │     0.3197 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.4180 │       0.3224 │     0.2958 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.4620 │       0.3638 │     0.3500 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.5722 │       0.4510 │     0.4091 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.5450 │       0.4296 │     0.3810 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.6023 │       0.4792 │     0.4448 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0006 │     0.0009 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.7661 │       0.8422 │     0.9211 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.6552 │       2.2881 │     2.5576 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Evaluation validation metric: 'output' 'loss' improved.\n",
            "'output' 'loss' decreased by 0.052.\n",
            "New best model saved.\n",
            "\n",
            "Training:  10%|▉         | 340/3450 [04:04<21:20,  2.43it/s, loss=0.12]   Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  10%|█         | 345/3450 [04:06<20:44,  2.49it/s, loss=0.117]\n",
            "Running evaluation for step: 345, epoch: 5\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.30s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.75s/it]\n",
            "Evaluation took 29.2017s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1201 │       0.1542 │     0.0911 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.7080 │       0.6667 │     0.7193 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.8785 │       2.2092 │     2.4756 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 20607.8203 │   20966.1211 │ 21928.9512 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 32000.0039 │   31976.4531 │ 31921.8516 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.5625 │       0.4767 │     0.4318 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.4444 │       0.4536 │     0.4002 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.7660 │       0.5068 │     0.4725 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.2540 │       0.1630 │     0.1317 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.2000 │       0.1516 │     0.1203 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.3478 │       0.1769 │     0.1462 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.4531 │       0.3457 │     0.3226 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.3580 │       0.3256 │     0.2970 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.6170 │       0.3711 │     0.3555 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.5469 │       0.4534 │     0.4082 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.4321 │       0.4308 │     0.3781 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7447 │       0.4827 │     0.4467 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0006 │     0.0009 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     1.2391 │       0.8371 │     0.9174 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.8785 │       2.2092 │     2.4756 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Evaluation validation metric: 'output' 'loss' improved.\n",
            "'output' 'loss' decreased by 0.079.\n",
            "New best model saved.\n",
            "\n",
            "Training:  12%|█▏        | 408/3450 [04:59<18:51,  2.69it/s, loss=0.0919]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  12%|█▏        | 414/3450 [05:04<56:04,  1.11s/it, loss=0.0912]\n",
            "Running evaluation for step: 414, epoch: 6\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.28s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.74s/it]\n",
            "Evaluation took 28.8160s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1964 │       0.1583 │     0.1004 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.4617 │       0.6635 │     0.7157 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.4594 │       2.1352 │     2.4007 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 18727.3789 │   20879.6992 │ 21820.6172 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 32000.8574 │   31981.2402 │ 31923.1602 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.6048 │       0.4794 │     0.4305 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.5761 │       0.4567 │     0.3993 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.6364 │       0.5091 │     0.4703 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.3080 │       0.1666 │     0.1365 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.2934 │       0.1551 │     0.1247 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.3242 │       0.1805 │     0.1515 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.4968 │       0.3494 │     0.3267 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.4733 │       0.3296 │     0.3010 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.5227 │       0.3743 │     0.3597 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.5745 │       0.4544 │     0.4080 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.5473 │       0.4326 │     0.3779 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.6045 │       0.4827 │     0.4466 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0006 │     0.0010 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.6835 │       0.8397 │     0.9206 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.4594 │       2.1352 │     2.4007 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Evaluation validation metric: 'output' 'loss' improved.\n",
            "'output' 'loss' decreased by 0.074.\n",
            "New best model saved.\n",
            "\n",
            "Training:  14%|█▍        | 476/3450 [05:53<15:28,  3.20it/s, loss=0.104]   Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  14%|█▍        | 483/3450 [05:59<1:05:30,  1.32s/it, loss=0.102]\n",
            "Running evaluation for step: 483, epoch: 7\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:10<00:00,  2.09s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.74s/it]\n",
            "Evaluation took 27.9193s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1396 │       0.1704 │     0.1130 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.5457 │       0.6571 │     0.7110 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.6302 │       2.0703 │     2.3414 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 19572.4043 │   20828.2930 │ 21776.7656 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 32000.8574 │   31983.8945 │ 31924.4395 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.5688 │       0.4859 │     0.4299 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.5116 │       0.4605 │     0.3967 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.6405 │       0.5180 │     0.4723 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.2615 │       0.1717 │     0.1393 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.2351 │       0.1595 │     0.1265 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.2946 │       0.1862 │     0.1557 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.4771 │       0.3523 │     0.3273 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.4290 │       0.3312 │     0.3001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.5372 │       0.3784 │     0.3620 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.5578 │       0.4565 │     0.4072 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.5017 │       0.4325 │     0.3752 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.6281 │       0.4865 │     0.4481 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0006 │     0.0010 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.8058 │       0.8284 │     0.9107 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.6302 │       2.0703 │     2.3414 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Evaluation validation metric: 'output' 'loss' improved.\n",
            "'output' 'loss' decreased by 0.065.\n",
            "New best model saved.\n",
            "\n",
            "Training:  16%|█▌        | 544/3450 [06:47<18:06,  2.67it/s, loss=0.0502]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  16%|█▌        | 552/3450 [06:51<26:57,  1.79it/s, loss=0.0672]\n",
            "Running evaluation for step: 552, epoch: 8\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.36s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.75s/it]\n",
            "Evaluation took 29.3857s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1784 │       0.1633 │     0.1063 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.4932 │       0.6579 │     0.7142 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.0749 │       2.0510 │     2.3300 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 17661.9375 │   20867.5977 │ 21842.5898 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31804.6797 │   31985.7227 │ 31925.5645 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.6312 │       0.4788 │     0.4207 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.5685 │       0.4514 │     0.3866 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.7094 │       0.5140 │     0.4644 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.3218 │       0.1672 │     0.1348 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.2897 │       0.1545 │     0.1221 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.3621 │       0.1826 │     0.1512 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.5171 │       0.3531 │     0.3220 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.4658 │       0.3302 │     0.2943 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.5812 │       0.3819 │     0.3575 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.6084 │       0.4505 │     0.3965 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.5479 │       0.4249 │     0.3640 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.6838 │       0.4832 │     0.4383 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0046 │       0.0010 │     0.0010 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.7523 │       0.8190 │     0.9055 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.0749 │       2.0510 │     2.3300 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Evaluation validation metric: 'output' 'loss' improved.\n",
            "'output' 'loss' decreased by 0.019.\n",
            "New best model saved.\n",
            "\n",
            "Training:  18%|█▊        | 612/3450 [07:36<14:21,  3.30it/s, loss=0.0953]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  18%|█▊        | 621/3450 [07:39<20:00,  2.36it/s, loss=0.095] \n",
            "Running evaluation for step: 621, epoch: 9\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:10<00:00,  2.02s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.75s/it]\n",
            "Evaluation took 27.6391s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1080 │       0.1568 │     0.0933 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.5920 │       0.6631 │     0.7189 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.5194 │       2.0562 │     2.3449 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 18720.5039 │   20871.3633 │ 21881.9922 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 32001.0078 │   31987.3691 │ 31927.1484 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.4251 │       0.4717 │     0.4098 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.3636 │       0.4436 │     0.3774 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.5116 │       0.5064 │     0.4506 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.2049 │       0.1639 │     0.1302 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1750 │       0.1519 │     0.1184 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.2471 │       0.1783 │     0.1452 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.3478 │       0.3493 │     0.3146 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.2975 │       0.3268 │     0.2887 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.4186 │       0.3768 │     0.3472 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.3961 │       0.4437 │     0.3939 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.3388 │       0.4169 │     0.3625 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.4767 │       0.4768 │     0.4335 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0004 │     0.0010 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     1.0233 │       0.8207 │     0.9102 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.5194 │       2.0562 │     2.3449 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Last improvement of output validation loss happened 69 step(s) ago.\n",
            "\n",
            "Training:  20%|█▉        | 680/3450 [08:27<16:30,  2.80it/s, loss=0.0803]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  20%|██        | 690/3450 [08:33<1:00:23,  1.31s/it, loss=0.131]\n",
            "Running evaluation for step: 690, epoch: 10\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:10<00:00,  2.07s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.75s/it]\n",
            "Evaluation took 27.8791s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1319 │       0.1658 │     0.0977 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.5417 │       0.6663 │     0.7222 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     2.0891 │       2.0992 │     2.4053 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 20758.7969 │   20869.4473 │ 21932.6250 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31740.9570 │   31990.3301 │ 31929.1582 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.5709 │       0.4610 │     0.4025 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.5523 │       0.4308 │     0.3676 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.5909 │       0.4987 │     0.4469 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.2203 │       0.1633 │     0.1287 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.2131 │       0.1508 │     0.1164 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.2281 │       0.1784 │     0.1443 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.4392 │       0.3451 │     0.3086 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.4248 │       0.3210 │     0.2810 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.4545 │       0.3749 │     0.3436 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.5439 │       0.4402 │     0.3822 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.5261 │       0.4110 │     0.3493 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.5629 │       0.4764 │     0.4239 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0070 │       0.0000 │     0.0008 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.7348 │       0.8284 │     0.9221 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     2.0891 │       2.0992 │     2.4053 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Last improvement of output validation loss happened 138 step(s) ago.\n",
            "\n",
            "Training:  22%|██▏       | 748/3450 [09:17<10:46,  4.18it/s, loss=0.093]   Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  22%|██▏       | 759/3450 [09:20<19:21,  2.32it/s, loss=0.0618]\n",
            "Running evaluation for step: 759, epoch: 11\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:10<00:00,  2.03s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.76s/it]\n",
            "Evaluation took 27.7934s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.2294 │       0.1723 │     0.0993 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.4840 │       0.6694 │     0.7275 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.9889 │       2.1561 │     2.4896 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 17125.2656 │   20882.0000 │ 21965.0820 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31804.5859 │   31993.6250 │ 31932.7812 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.6466 │       0.4523 │     0.3906 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.5772 │       0.4226 │     0.3576 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.7350 │       0.4905 │     0.4330 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.3485 │       0.1646 │     0.1230 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.3108 │       0.1516 │     0.1111 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.3966 │       0.1803 │     0.1382 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.5338 │       0.3449 │     0.3022 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.4765 │       0.3210 │     0.2749 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.6068 │       0.3751 │     0.3373 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.6241 │       0.4377 │     0.3736 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.5570 │       0.4089 │     0.3417 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7094 │       0.4744 │     0.4145 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0046 │       0.0000 │     0.0008 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.7431 │       0.8198 │     0.9164 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.9889 │       2.1561 │     2.4896 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Last improvement of output validation loss happened 207 step(s) ago.\n",
            "\n",
            "Training:  24%|██▎       | 816/3450 [10:06<14:40,  2.99it/s, loss=0.0836]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  24%|██▍       | 828/3450 [10:11<23:13,  1.88it/s, loss=0.0584]\n",
            "Running evaluation for step: 828, epoch: 12\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.29s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.75s/it]\n",
            "Evaluation took 28.9830s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.2650 │       0.1697 │     0.0959 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.4612 │       0.6754 │     0.7320 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.9336 │       2.2348 │     2.6058 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 16859.1816 │   21018.3027 │ 22097.3633 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31804.5254 │   31994.5410 │ 31935.5547 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.6820 │       0.4473 │     0.3796 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.6181 │       0.4199 │     0.3487 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.7607 │       0.4818 │     0.4193 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.3861 │       0.1665 │     0.1236 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.3497 │       0.1536 │     0.1120 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.4310 │       0.1822 │     0.1383 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.5747 │       0.3458 │     0.2965 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.5208 │       0.3231 │     0.2708 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.6410 │       0.3743 │     0.3294 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.6743 │       0.4313 │     0.3615 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.6111 │       0.4041 │     0.3319 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7521 │       0.4656 │     0.3994 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0046 │       0.0000 │     0.0007 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.6789 │       0.8147 │     0.9102 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.9336 │       2.2348 │     2.6058 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Last improvement of output validation loss happened 276 step(s) ago.\n",
            "\n",
            "Training:  26%|██▌       | 884/3450 [10:57<14:57,  2.86it/s, loss=0.0646]  Last batch in epoch only has 1 sample and will be dropped.\n",
            "Training:  26%|██▌       | 897/3450 [11:02<22:26,  1.90it/s, loss=0.0864]\n",
            "Running evaluation for step: 897, epoch: 13\n",
            "Evaluation valid: 100%|██████████| 5/5 [00:11<00:00,  2.37s/it]\n",
            "Evaluation test : 100%|██████████| 10/10 [00:17<00:00,  1.76s/it]\n",
            "Evaluation took 29.4699s\n",
            "\n",
            "╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.1148 │       0.1611 │     0.1015 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     0.5640 │       0.6849 │     0.7402 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.3830 │       2.3761 │     2.7920 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 19012.4805 │   21228.2695 │ 22266.3711 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31895.8926 │   31994.9375 │ 31941.7383 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.4810 │       0.4265 │     0.3656 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.4222 │       0.4055 │     0.3420 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.5588 │       0.4541 │     0.3972 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.2128 │       0.1594 │     0.1237 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1866 │       0.1477 │     0.1126 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.2475 │       0.1735 │     0.1379 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.4135 │       0.3341 │     0.2928 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.3630 │       0.3157 │     0.2721 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.4804 │       0.3578 │     0.3200 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.4810 │       0.4120 │     0.3501 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.4222 │       0.3917 │     0.3275 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.5588 │       0.4387 │     0.3804 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0048 │       0.0000 │     0.0007 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     0.9010 │       0.8216 │     0.8889 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.3830 │       2.3761 │     2.7920 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "Last improvement of output validation loss happened 345 step(s) ago.\n",
            "\n",
            "\n",
            "EARLY STOPPING due to lack of validation improvement. It has been 345 step(s) since last validation improvement.\n",
            "Training:  26%|██▌       | 897/3450 [11:32<32:49,  1.30it/s, loss=0.0864]\n",
            "\n",
            "╒═════════════════╕\n",
            "│ TRAINING REPORT │\n",
            "╘═════════════════╛\n",
            "\n",
            "╒══════════════════════════════╤════════════════════╕\n",
            "│ Validation feature           │ output             │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Validation metric            │ loss               │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model step              │ 552                │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model epoch             │ 9                  │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model's validation loss │ 2.0510141849517822 │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model's test loss       │ 2.3300318717956543 │\n",
            "╘══════════════════════════════╧════════════════════╛\n",
            "\n",
            "Finished: api_experiment_run\n",
            "Saved to: /home/ubuntu/lambda_labs/llama2-finetune/results/api_experiment_run_20\n",
            "\n",
            "╒══════════╕\n",
            "│ FINISHED │\n",
            "╘══════════╛\n",
            "\n"
          ]
        }
      ],
      "source": [
        "model = None\n",
        "clear_cache()\n",
        "\n",
        "qlora_fine_tuning_config = yaml.safe_load(\n",
        "\"\"\"\n",
        "model_type: llm\n",
        "base_model: meta-llama/Llama-2-13b-hf\n",
        "\n",
        "input_features:\n",
        "  - name: instruction\n",
        "    type: text\n",
        "\n",
        "output_features:\n",
        "  - name: output\n",
        "    type: text\n",
        "\n",
        "prompt:\n",
        "  template: >-\n",
        "    Below is an instruction that describes a task, paired with an input\n",
        "    that provides further context. Write a response that appropriately\n",
        "    completes the request.\n",
        "\n",
        "    ### Instruction: {instruction}\n",
        "\n",
        "    ### Input: {input}\n",
        "\n",
        "    ### Response:\n",
        "\n",
        "generation:\n",
        "  temperature: 0.1\n",
        "  max_new_tokens: 2048\n",
        "\n",
        "adapter:\n",
        "  type: lora\n",
        "\n",
        "quantization:\n",
        "  bits: 4\n",
        "\n",
        "trainer:\n",
        "  type: finetune\n",
        "  epochs: 50\n",
        "  batch_size: 1\n",
        "  eval_batch_size: 2\n",
        "  gradient_accumulation_steps: 16\n",
        "  learning_rate: 0.0001\n",
        "  optimizer:\n",
        "    type: adam\n",
        "    params:\n",
        "      eps: 1.e-8\n",
        "      betas:\n",
        "        - 0.9\n",
        "        - 0.999\n",
        "      weight_decay: 0\n",
        "  learning_rate_scheduler:\n",
        "    warmup_fraction: 0.03\n",
        "    reduce_on_plateau: 0\n",
        "\"\"\"\n",
        ")\n",
        "\n",
        "model = LudwigModel(config=qlora_fine_tuning_config, logging_level=logging.INFO)\n",
        "results = model.train(dataset=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YkjcqMFMkwT"
      },
      "source": [
        "#### Perform Inference\n",
        "\n",
        "We can now use the model we fine-tuned above to make predictions on some test examples to see whether fine-tuning the large language model improve its ability to follow instructions/the tasks we're asking it to perform."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ql8BdQm7Mora",
        "outputId": "0d187e14-f7db-47b4-e840-74e7c1eeaa2e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded HuggingFace implementation of meta-llama/Llama-2-13b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Prediction:   0%|          | 0/1 [00:00<?, ?it/s]For generating text, using: GenerationConfig {\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 2048,\n",
            "  \"temperature\": 0.1,\n",
            "  \"transformers_version\": \"4.31.0\"\n",
            "}\n",
            "\n",
            "Decoded text inputs for the first example in batch: below is an instruction that describes a task, paired with an input that provides further context. write a response that appropriately completes the request.\n",
            "### instruction: how do i know if i'm drinking too much?\n",
            "### input: how do i know if i'm drinking too much?\n",
            "### response:\n",
            "Decoded generated output for the first example in batch: below is an instruction that describes a task, paired with an input that provides further context. write a response that appropriately completes the request.\n",
            "### instruction: how do i know if i'm drinking too much?\n",
            "### input: how do i know if i'm drinking too much?\n",
            "### response: there is no set amount of alcohol that is safe for everyone. it depends on your age, gender, and health. however, drinking more than the recommended daily limit can increase your risk of developing health problems.\n",
            "Prediction: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]\n",
            "Loaded HuggingFace implementation of meta-llama/Llama-2-13b-hf tokenizer\n",
            "No padding token id found. Using eos_token as pad_token.\n",
            "Instruction: how do i know if i'm drinking too much?\n",
            "Input: how do i know if i'm drinking too much?\n",
            "Generated Output: there is no set amount of alcohol that is safe for everyone. it depends on your age, gender, and health. however, drinking more than the recommended daily limit can increase your risk of developing health problems.\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/ubuntu/miniconda/envs/env_langchain/lib/python3.10/site-packages/ludwig/features/feature_utils.py:102: RuntimeWarning: divide by zero encountered in log\n",
            "  return np.sum(np.log(sequence_probabilities))\n"
          ]
        }
      ],
      "source": [
        "test_examples = pd.DataFrame([\n",
        "      # {\n",
        "      #       \"instruction\": \"what is mental health illness?\",\n",
        "      #       \"input\": 'describe mental illness'\n",
        "      # },      \n",
        "      # {\n",
        "      #       \"instruction\": \"which type of therapy is required for better mental health?\",\n",
        "      #       \"input\": \"show all the therapy options\"\n",
        "      # },\n",
        "      {\n",
        "            \"instruction\": \"how do i know if i'm drinking too much?\",\n",
        "            \"input\": \"how do i know if i'm drinking too much?\",\n",
        "      }      \n",
        "])\n",
        "\n",
        "predictions = model.predict(test_examples)[0]\n",
        "for input_with_prediction in zip(test_examples['instruction'], test_examples['input'], predictions['output_response']):\n",
        "  print(f\"Instruction: {input_with_prediction[0]}\")\n",
        "  print(f\"Input: {input_with_prediction[1]}\")\n",
        "  print(f\"Generated Output: {input_with_prediction[2][0]}\")\n",
        "  print(\"\\n\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alN-aOHQBLhH"
      },
      "source": [
        "#### **Observations From QLoRA Fine-Tuning** 🔎\n",
        "- Even when we just fine-tune the model on 100 examples from our dataset (which only takes about 4 minutes), it significantly improves the model on our task 🔥\n",
        "- The answers are not perfect when we just use 100 examples, but if we inspect the *logic* in the response, we can see that it is 95% of the way there. This is SIGNIFICANTLY better than before - there is no repetition and the actual code aspects of the answers are all correct.\n",
        "- The partial errors such as `sierp` instead of `arrray` etc indicates that we need to train on a larger amount of data for the model to better learn how to follow instructions and not make these kinds of mistakes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVOUrJTBD2Io"
      },
      "source": [
        "If you're looking for a managed solution to handle all of the hassle of figuring out the right compute for your fine-tuning task, ensuring that they always succeed without CPU or GPU out-of-memory errors, and be able to rapidly deploy them for fast real-time inference, check out [Predibase](https://www.predibase.com/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWxotc5kGcBD"
      },
      "source": [
        "# **Upload Trained Model Artifacts To HuggingFace** 🤗\n",
        "\n",
        "Now that we have a fine-tuned model, we can export the model weights to HuggingFace hub so we can use them in downstream tasks or in production. Ludwig supports uploading model weights directly to HuggingFace Hub via the `upload` Ludwig command.\n",
        "\n",
        "```\n",
        "!ludwig upload hf_hub --repo_id <hf_user_name>/<repo_name> --model_path <top_level_model_directory>\n",
        "```\n",
        "\n",
        "The `model-path` can be seen at the end of training/fine-tuning, as seen in this screenshot.\n",
        "\n",
        "<br>\n",
        "\n",
        "![Screenshot 2023-08-15 at 2.20.00 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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "J5YTm1UgMp27",
        "outputId": "0d37ef95-3085-4b5a-ab65-afd25d0fd05d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Upload 1 LFS files:   0%|                                 | 0/1 [00:00<?, ?it/s]\n",
            "adapter_model.bin:   0%|                            | 0.00/26.3M [00:00<?, ?B/s]\u001b[A\n",
            "adapter_model.bin:   0%|                   | 8.19k/26.3M [00:00<13:23, 32.7kB/s]\u001b[A\n",
            "adapter_model.bin:   1%|▏                    | 180k/26.3M [00:00<00:42, 616kB/s]\u001b[A\n",
            "adapter_model.bin:   2%|▍                   | 508k/26.3M [00:00<00:17, 1.43MB/s]\u001b[A\n",
            "adapter_model.bin:   7%|█▎                 | 1.85M/26.3M [00:00<00:04, 4.98MB/s]\u001b[A\n",
            "adapter_model.bin:  21%|███▉               | 5.51M/26.3M [00:00<00:01, 13.3MB/s]\u001b[A\n",
            "adapter_model.bin:  30%|█████▋             | 7.87M/26.3M [00:00<00:01, 16.2MB/s]\u001b[A\n",
            "adapter_model.bin:  47%|████████▉          | 12.4M/26.3M [00:00<00:00, 23.8MB/s]\u001b[A\n",
            "adapter_model.bin:  61%|███████████▌       | 16.0M/26.3M [00:01<00:00, 21.6MB/s]\u001b[A\n",
            "adapter_model.bin:  76%|██████████████▍    | 20.0M/26.3M [00:01<00:00, 24.9MB/s]\u001b[A\n",
            "adapter_model.bin:  88%|████████████████▊  | 23.2M/26.3M [00:01<00:00, 25.9MB/s]\u001b[A\n",
            "adapter_model.bin: 100%|███████████████████| 26.3M/26.3M [00:01<00:00, 13.6MB/s]\u001b[A\n",
            "Upload 1 LFS files: 100%|█████████████████████████| 1/1 [00:02<00:00,  2.22s/it]\n",
            "Model uploaded to `https://huggingface.co/rewisdomai/llama-finetune/tree/main/` with repository name `rewisdomai/llama-finetune`\n"
          ]
        }
      ],
      "source": [
        "!ludwig upload hf_hub --repo_id rewisdomai/llama-finetune --model_path /home/ubuntu/lambda_labs/llama2-finetune/results/api_experiment_run_20"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_lcu4QuHy63"
      },
      "source": [
        "#### Want to try out our QLoRA fine-tuned weights that were fine-tuned on the entire dataset over 3 epochs? Check it out here on HuggingFace 🤗: https://huggingface.co/arnavgrg/codealpaca-qlora\n",
        "\n",
        "```\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM\n",
        "\n",
        "config = PeftConfig.from_pretrained(\"arnavgrg/codealpaca-qlora\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"meta-llama/Llama-2-7b-hf\")\n",
        "model = PeftModel.from_pretrained(model, \"arnavgrg/codealpaca-qlora\")\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHjj-C1gETuz"
      },
      "source": [
        "# **Resources** 🧺\n",
        "\n",
        "- If you're new to LLMs, check out this webinar where Daliana Liu discusses the 10 things to know about LLMs: https://www.youtube.com/watch?v=fezMHMk7u5o&t=2027s&ab_channel=Predibase\n",
        "- Ludwig 0.8 Release Blogpost for the full set of new features: https://predibase.com/blog/ludwig-v0-8-open-source-toolkit-to-build-and-fine-tune-custom-llms-on-your-data\n",
        "- Ludwig Documentation: https://ludwig.ai/latest/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4wOQWvITcouD"
      },
      "source": [
        "# **Try Predibase: The first low-code AI platform for developers**\n",
        "\n",
        "If you're interested in a managed solution for training larger LLMs that require distributed training using multi-node multi-GPU setups, or for fast on-demand inference for LLMs, check out [Predibase](https://predibase.com/), the AI platform for engineers built on top of Ludwig! Try our free 14 day trial here by signing up [here](https://pbase.ai/3OD77wQ)\n",
        "\n",
        "## How can Predibase help me with fine-tuning?\n",
        "Here's a 6 minute read through of the challenges that fine-tuning poses, and how Predibase solves all of them for you. You can fine-tune Llama-2 with Scalable LLM infrastructure through Predibase: https://predibase.com/blog/how-to-fine-tune-llama-2-on-your-data-with-scalable-llm-infrastructure\n",
        "\n",
        "### Check out these great blog posts and webinars on LLMs\n",
        "1. Beyond Chat Use Cases for LLMs in Production: https://predibase.com/blog/beyond-chat-use-cases-for-llms-in-production\n",
        "2. From Unstructured to Structured Data with Predibase: https://www.youtube.com/watch?v=KMZ_OSJ6bOE&t=523s&ab_channel=Predibase\n",
        "3. Build your own LLM in less than 10 lines of code: https://www.youtube.com/watch?v=efMQgwM9dFY&ab_channel=Predibase"
      ]
    }
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