zephyr_7b_Autotrain_finetuning.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "JvMRbVLEJlZT",
        "outputId": "e0873cb7-f96d-4bb9-863b-d281f9590f86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires pandas==2.0.3, but you have pandas 2.2.2 which is incompatible.\n",
            "tensorflow 2.15.0 requires tensorboard<2.16,>=2.15, but you have tensorboard 2.16.2 which is incompatible.\n",
            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 4.23.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "#@title 🤗 AutoTrain LLM\n",
        "#@markdown In order to use this colab\n",
        "#@markdown - upload train.csv to a folder named `data/`\n",
        "#@markdown - train.csv must contain a `text` column\n",
        "#@markdown - choose a project name if you wish\n",
        "#@markdown - change model if you wish, you can use most of the text-generation models from Hugging Face Hub\n",
        "#@markdown - add huggingface information (token and repo_id) if you wish to push trained model to huggingface hub\n",
        "#@markdown - update hyperparameters if you wish\n",
        "#@markdown - click `Runtime > Run all` or run each cell individually\n",
        "#@markdown - report issues / feature requests here: https://github.com/huggingface/autotrain-advanced/issues\n",
        "\n",
        "import os\n",
        "!pip install -U autotrain-advanced > install_logs.txt\n",
        "!autotrain setup --colab > setup_logs.txt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "S8c4OkfK8XBv",
        "outputId": "cc649c47-9e44-4b37-fe00-0068071144ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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            "463df4b2b0784bda82fbdde16a0dbba6"
          ]
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "a6bc34834e1a41ebb708a345a918f985"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Read the original JSON file\n",
        "with open('mudata.json', 'r') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "# Add IDs to each row\n",
        "for idx, item in enumerate(data, start=1):\n",
        "    item['id'] = idx\n",
        "\n",
        "# Write the updated data to a new file\n",
        "new_file_path = 'updated_data.json'\n",
        "with open(new_file_path, 'w') as new_file:\n",
        "    json.dump(data, new_file, indent=2)\n",
        "\n",
        "print(f'Updated data with IDs written to {new_file_path}')"
      ],
      "metadata": {
        "id": "Oy4LSADP7ll3",
        "outputId": "e6c2844b-745a-4f82-dd5b-e32caaab4c27",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated data with IDs written to updated_data.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(\"json\", data_files=\"updated_data.json\")\n",
        "\n",
        "datasets_train_test = dataset[\"train\"].train_test_split(test_size=150)\n",
        "datasets_train_validation = datasets_train_test[\"train\"].train_test_split(test_size=150)\n",
        "\n",
        "dataset[\"train\"] = datasets_train_validation[\"train\"]\n",
        "dataset[\"validation\"] = datasets_train_validation[\"test\"]\n",
        "dataset[\"test\"] = datasets_train_test[\"test\"]\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset['train'])}\")\n",
        "print(f\"Test dataset size: {len(dataset['test'])}\")\n",
        "\n",
        "# Train dataset size: 14732\n",
        "# Test dataset size: 819"
      ],
      "metadata": {
        "id": "ueuowcLh71dy",
        "outputId": "5fef4fa3-4d23-408d-8e8a-5a2641f2fe4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
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            "30c61e5ca1d5427a903d66bd246020e7"
          ]
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1cebbab86ed841f9b2a526942c03e2b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: 7444\n",
            "Test dataset size: 150\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "def get_model_and_tokenizer(mode_id):\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(mode_id)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "    return tokenizer\n",
        "\n",
        "tokenizer = get_model_and_tokenizer(\"HuggingFaceH4/zephyr-7b-beta\")"
      ],
      "metadata": {
        "id": "lOcjW0PN75Tp",
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "9e5f2c32-1091-4841-e99d-20d65f0c9fbb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.43k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
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          "metadata": {}
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/168 [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "# The maximum total input sequence length after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"question\"], truncation=True), batched=True)\n",
        "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"answer\"], truncation=True), batched=True)\n",
        "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
        "print(f\"Max target length: {max_target_length}\")\n"
      ],
      "metadata": {
        "id": "5GVer8eY8Rb1",
        "outputId": "6078ed83-9ce7-43a1-a656-553bbfb55eb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133,
          "referenced_widgets": [
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        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7594 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ceb5d9d15a9488fa6ee0ab073f0ec10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max source length: 55\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7594 [00:00<?, ? examples/s]"
            ],
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              "model_id": "7289d2663f194e1d854f23df381bc57a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max target length: 177\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "    # add prefix to the input for t5\n",
        "    inputs = [\"answer the following question: \" + item for item in sample[\"question\"]]\n",
        "\n",
        "    # tokenize inputs\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    # Tokenize targets with the `text_target` keyword argument\n",
        "    labels = tokenizer(text_target=sample[\"answer\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "\n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")"
      ],
      "metadata": {
        "id": "1DqJbIqb8i05",
        "outputId": "5e61f98d-7d96-4c83-f3e6-0f8546727aeb",
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            "4f21245e2ac04fa7a400cf21be504b7f",
            "b964a4683c2b413f9715571891253e4e",
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            "b9b5a12582c14725bc46f9d2632eaf7c",
            "6b411652485c4e6dbd4157f733c26bad"
          ]
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7444 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7478eaacd214d749783cba4869daad2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "9167401e6d054ae4b002c98db1c60d99"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "b1238fcb15b04a8f9330fcb41c9e18c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys of tokenized dataset: ['answer', 'id', 'question', 'input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = tokenized_dataset.map(lambda example: {'text': example['question'] + example['answer']})"
      ],
      "metadata": {
        "id": "tYbZa1zo8l2x",
        "outputId": "19111132-decd-4c54-fc3f-0648568f855f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "b91dddb215fc487bb9529b235bf5262a",
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            "3d9fe01ceff148399a3fc401459f096c",
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            "7513c52ea1fe43b59fc735efb8d659f6"
          ]
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7444 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b91dddb215fc487bb9529b235bf5262a"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71166fb538b9486fbe691b95c1455567"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ddcafee27b1e4757b1056618ca58b0ca"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Assuming tokenized_dataset is a DatasetDict object\n",
        "# Accessing each dataset\n",
        "train_df = tokenized_dataset['train'].to_pandas()\n",
        "val_df = tokenized_dataset['validation'].to_pandas()\n",
        "test_df = tokenized_dataset['test'].to_pandas()\n",
        "\n",
        "# Concatenating all three DataFrames\n",
        "concatenated_df = pd.concat([train_df, val_df, test_df], ignore_index=True)\n",
        "\n",
        "# Saving the concatenated DataFrame to a CSV file\n",
        "concatenated_df.to_csv('data/train.csv', index=False)\n",
        "\n",
        "print(\"Concatenated DataFrame saved to train.csv successfully.\")\n"
      ],
      "metadata": {
        "id": "xFLDmnZL8rdq",
        "outputId": "6d2722ff-6b9d-4e5a-dabd-cf2df5b60cbf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Concatenated DataFrame saved to train.csv successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "cellView": "form",
        "id": "A2-_lkBS1WKA"
      },
      "outputs": [],
      "source": [
        "#@markdown ---\n",
        "#@markdown #### Project Config\n",
        "#@markdown Note: if you are using a restricted/private model, you need to enter your Hugging Face token in the next step.\n",
        "project_name = 'zephyr-7b-vetdataset-finetuned' # @param {type:\"string\"}\n",
        "model_name = 'HuggingFaceH4/zephyr-7b-beta' # @param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### Push to Hub?\n",
        "#@markdown Use these only if you want to push your trained model to a private repo in your Hugging Face Account\n",
        "#@markdown If you dont use these, the model will be saved in Google Colab and you are required to download it manually.\n",
        "#@markdown Please enter your Hugging Face write token. The trained model will be saved to your Hugging Face account.\n",
        "#@markdown You can find your token here: https://huggingface.co/settings/tokens\n",
        "push_to_hub = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "hf_token = \"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\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown #### Hyperparameters\n",
        "learning_rate = 2e-4 # @param {type:\"number\"}\n",
        "num_epochs = 1 #@param {type:\"number\"}\n",
        "batch_size = 1 # @param {type:\"slider\", min:1, max:32, step:1}\n",
        "block_size = 1024 # @param {type:\"number\"}\n",
        "trainer = \"sft\" # @param [\"default\", \"sft\"] {type:\"raw\"}\n",
        "warmup_ratio = 0.1 # @param {type:\"number\"}\n",
        "weight_decay = 0.01 # @param {type:\"number\"}\n",
        "gradient_accumulation = 4 # @param {type:\"number\"}\n",
        "mixed_precision = \"fp16\" # @param [\"fp16\", \"bf16\", \"none\"] {type:\"raw\"}\n",
        "peft = True # @param [\"False\", \"True\"] {type:\"raw\"}\n",
        "quantization = \"int4\" # @param [\"int4\", \"int8\", \"none\"] {type:\"raw\"}\n",
        "lora_r = 16 #@param {type:\"number\"}\n",
        "lora_alpha = 32 #@param {type:\"number\"}\n",
        "lora_dropout = 0.05 #@param {type:\"number\"}\n",
        "\n",
        "os.environ[\"PROJECT_NAME\"] = project_name\n",
        "os.environ[\"MODEL_NAME\"] = model_name\n",
        "os.environ[\"PUSH_TO_HUB\"] = str(push_to_hub)\n",
        "os.environ[\"HF_TOKEN\"] = hf_token\n",
        "os.environ[\"LEARNING_RATE\"] = str(learning_rate)\n",
        "os.environ[\"NUM_EPOCHS\"] = str(num_epochs)\n",
        "os.environ[\"BATCH_SIZE\"] = str(batch_size)\n",
        "os.environ[\"BLOCK_SIZE\"] = str(block_size)\n",
        "os.environ[\"WARMUP_RATIO\"] = str(warmup_ratio)\n",
        "os.environ[\"WEIGHT_DECAY\"] = str(weight_decay)\n",
        "os.environ[\"GRADIENT_ACCUMULATION\"] = str(gradient_accumulation)\n",
        "os.environ[\"MIXED_PRECISION\"] = str(mixed_precision)\n",
        "os.environ[\"PEFT\"] = str(peft)\n",
        "os.environ[\"QUANTIZATION\"] = str(quantization)\n",
        "os.environ[\"LORA_R\"] = str(lora_r)\n",
        "os.environ[\"LORA_ALPHA\"] = str(lora_alpha)\n",
        "os.environ[\"LORA_DROPOUT\"] = str(lora_dropout)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "id": "g3cd_ED_yXXt",
        "outputId": "f8a7fde6-161c-4dd1-af77-3525afa7b068",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:44\u001b[0m | \u001b[36mautotrain.cli.run_llm\u001b[0m:\u001b[36mrun\u001b[0m:\u001b[36m343\u001b[0m - \u001b[1mRunning LLM\u001b[0m\n",
            "\u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[32m2024-05-05 21:00:44\u001b[0m | \u001b[36mautotrain.trainers.common\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m180\u001b[0m - \u001b[33m\u001b[1mParameters supplied but not used: train, deploy, backend, inference, version, func, config\u001b[0m\n",
            "Saving the dataset (1/1 shards): 100% 7744/7744 [00:00<00:00, 256679.13 examples/s]\n",
            "Saving the dataset (1/1 shards): 100% 7744/7744 [00:00<00:00, 246056.51 examples/s]\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:45\u001b[0m | \u001b[36mautotrain.backend\u001b[0m:\u001b[36mcreate\u001b[0m:\u001b[36m300\u001b[0m - \u001b[1mStarting local training...\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:45\u001b[0m | \u001b[36mautotrain.commands\u001b[0m:\u001b[36mlaunch_command\u001b[0m:\u001b[36m327\u001b[0m - \u001b[1m['accelerate', 'launch', '--num_machines', '1', '--num_processes', '1', '--mixed_precision', 'fp16', '-m', 'autotrain.trainers.clm', '--training_config', 'zephyr-7b-vetdataset-finetuned/training_params.json']\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:45\u001b[0m | \u001b[36mautotrain.commands\u001b[0m:\u001b[36mlaunch_command\u001b[0m:\u001b[36m328\u001b[0m - \u001b[1m{'model': 'HuggingFaceH4/zephyr-7b-beta', 'project_name': 'zephyr-7b-vetdataset-finetuned', 'data_path': 'zephyr-7b-vetdataset-finetuned/autotrain-data', 'train_split': 'train', 'valid_split': None, 'add_eos_token': False, 'block_size': 1024, 'model_max_length': 1024, 'padding': None, 'trainer': 'default', 'use_flash_attention_2': False, 'log': 'none', 'disable_gradient_checkpointing': False, 'logging_steps': -1, 'evaluation_strategy': 'epoch', 'save_total_limit': 1, 'auto_find_batch_size': False, 'mixed_precision': 'fp16', 'lr': 0.0002, 'epochs': 1, 'batch_size': 1, 'warmup_ratio': 0.1, 'gradient_accumulation': 4, 'optimizer': 'adamw_torch', 'scheduler': 'linear', 'weight_decay': 0.01, 'max_grad_norm': 1.0, 'seed': 42, 'chat_template': None, 'quantization': 'int4', 'target_modules': 'all-linear', 'merge_adapter': False, 'peft': True, 'lora_r': 16, 'lora_alpha': 32, 'lora_dropout': 0.05, 'model_ref': None, 'dpo_beta': 0.1, 'max_prompt_length': 128, 'max_completion_length': None, 'prompt_text_column': 'autotrain_prompt', 'text_column': 'autotrain_text', 'rejected_text_column': 'autotrain_rejected_text', 'push_to_hub': True, 'username': 'ahmed807762', 'token': '*****'}\u001b[0m\n",
            "The following values were not passed to `accelerate launch` and had defaults used instead:\n",
            "\t`--dynamo_backend` was set to a value of `'no'`\n",
            "To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:57\u001b[0m | \u001b[36mautotrain.trainers.clm.train_clm_default\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mStarting default/generic CLM training...\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:57\u001b[0m | \u001b[36mautotrain.trainers.clm.utils\u001b[0m:\u001b[36mprocess_input_data\u001b[0m:\u001b[36m311\u001b[0m - \u001b[1mloading dataset from disk\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:57\u001b[0m | \u001b[36mautotrain.trainers.clm.utils\u001b[0m:\u001b[36mprocess_input_data\u001b[0m:\u001b[36m352\u001b[0m - \u001b[1mTrain data: Dataset({\n",
            "    features: ['answer', 'id', 'question', 'input_ids', 'attention_mask', 'labels', 'autotrain_text'],\n",
            "    num_rows: 7744\n",
            "})\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:57\u001b[0m | \u001b[36mautotrain.trainers.clm.utils\u001b[0m:\u001b[36mprocess_input_data\u001b[0m:\u001b[36m353\u001b[0m - \u001b[1mValid data: None\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:58\u001b[0m | \u001b[36mautotrain.trainers.clm.utils\u001b[0m:\u001b[36mconfigure_logging_steps\u001b[0m:\u001b[36m423\u001b[0m - \u001b[1mconfiguring logging steps\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:58\u001b[0m | \u001b[36mautotrain.trainers.clm.utils\u001b[0m:\u001b[36mconfigure_logging_steps\u001b[0m:\u001b[36m436\u001b[0m - \u001b[1mLogging steps: 25\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:58\u001b[0m | \u001b[36mautotrain.trainers.clm.utils\u001b[0m:\u001b[36mconfigure_training_args\u001b[0m:\u001b[36m441\u001b[0m - \u001b[1mconfiguring training args\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:58\u001b[0m | \u001b[36mautotrain.trainers.clm.utils\u001b[0m:\u001b[36mconfigure_block_size\u001b[0m:\u001b[36m504\u001b[0m - \u001b[1mUsing block size 1024\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:58\u001b[0m | \u001b[36mautotrain.trainers.clm.train_clm_default\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m59\u001b[0m - \u001b[1mloading model config...\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:00:58\u001b[0m | \u001b[36mautotrain.trainers.clm.train_clm_default\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m67\u001b[0m - \u001b[1mloading model...\u001b[0m\n",
            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n",
            "Loading checkpoint shards: 100% 8/8 [01:09<00:00,  8.65s/it]\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:02:10\u001b[0m | \u001b[36mautotrain.trainers.clm.train_clm_default\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m98\u001b[0m - \u001b[1mmodel dtype: torch.float16\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:02:10\u001b[0m | \u001b[36mautotrain.trainers.clm.train_clm_default\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m101\u001b[0m - \u001b[1mpreparing peft model...\u001b[0m\n",
            "Running tokenizer on train dataset: 100% 7744/7744 [00:01<00:00, 7651.74 examples/s]\n",
            "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "Grouping texts in chunks of 1024 (num_proc=4): 100% 7744/7744 [00:00<00:00, 7866.94 examples/s] \n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:02:14\u001b[0m | \u001b[36mautotrain.trainers.clm.train_clm_default\u001b[0m:\u001b[36mtrain\u001b[0m:\u001b[36m162\u001b[0m - \u001b[1mcreating trainer\u001b[0m\n",
            "\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:02:14\u001b[0m | \u001b[36mautotrain.trainers.common\u001b[0m:\u001b[36mon_train_begin\u001b[0m:\u001b[36m231\u001b[0m - \u001b[1mStarting to train...\u001b[0m\n",
            " 27% 25/93 [08:22<23:00, 20.30s/it]\u001b[1mINFO    \u001b[0m | \u001b[32m2024-05-05 21:10:36\u001b[0m | \u001b[36mautotrain.trainers.common\u001b[0m:\u001b[36mon_log\u001b[0m:\u001b[36m226\u001b[0m - \u001b[1m{'loss': 1.7125, 'grad_norm': 1.2242318391799927, 'learning_rate': 0.00016626506024096388, 'epoch': 0.26881720430107525}\u001b[0m\n",
            "{'loss': 1.7125, 'grad_norm': 1.2242318391799927, 'learning_rate': 0.00016626506024096388, 'epoch': 0.27}\n",
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        }
      ],
      "source": [
        "!autotrain llm \\\n",
        "--train \\\n",
        "--model ${MODEL_NAME} \\\n",
        "--project-name ${PROJECT_NAME} \\\n",
        "--username \"ahmed807762\" \\\n",
        "--data-path data/ \\\n",
        "--text-column text \\\n",
        "--lr ${LEARNING_RATE} \\\n",
        "--batch-size ${BATCH_SIZE} \\\n",
        "--epochs ${NUM_EPOCHS} \\\n",
        "--block-size ${BLOCK_SIZE} \\\n",
        "--warmup-ratio ${WARMUP_RATIO} \\\n",
        "--lora-r ${LORA_R} \\\n",
        "--lora-alpha ${LORA_ALPHA} \\\n",
        "--lora-dropout ${LORA_DROPOUT} \\\n",
        "--weight-decay ${WEIGHT_DECAY} \\\n",
        "--gradient-accumulation ${GRADIENT_ACCUMULATION} \\\n",
        "--quantization ${QUANTIZATION} \\\n",
        "--mixed-precision ${MIXED_PRECISION} \\\n",
        "$( [[ \"$PEFT\" == \"True\" ]] && echo \"--peft\" ) \\\n",
        "$( [[ \"$PUSH_TO_HUB\" == \"True\" ]] && echo \"--push-to-hub --token ${HF_TOKEN}\" )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip3 install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6fMqOACrdqr",
        "outputId": "a788c414-a05f-4c65-d8f8-89b7eda25fc0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.16.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx>=0.24.1->gradio) (1.2.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->gradio) (3.3.2)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n",
            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=f3c97862484b7294cb1fafcab723995f372be97928ee2cc36badc6e9d2097abb\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, tomlkit, shellingham, semantic-version, ruff, orjson, aiofiles, typer, gradio-client, gradio\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.9.4\n",
            "    Uninstalling typer-0.9.4:\n",
            "      Successfully uninstalled typer-0.9.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
            "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiofiles-23.2.1 ffmpy-0.3.2 gradio-4.29.0 gradio-client-0.16.1 orjson-3.10.3 pydub-0.25.1 ruff-0.4.3 semantic-version-2.10.0 shellingham-1.5.4 tomlkit-0.12.0 typer-0.12.3 websockets-11.0.3\n"
          ]
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      "cell_type": "code",
      "source": [
        "# Empty VRAM\n",
        "del model\n",
        "# del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "8wcmKqpUuoc2"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "from peft import PeftModel\n",
        "import torch\n",
        "\n",
        "\n",
        "def merge(base_model, trained_adapter, token):\n",
        "    base = AutoModelForCausalLM.from_pretrained(\n",
        "        base_model, torch_dtype=torch.float16, low_cpu_mem_usage=True, token=token\n",
        "    )\n",
        "    model = PeftModel.from_pretrained(base, trained_adapter, token=token)\n",
        "    try:\n",
        "        tokenizer = AutoTokenizer.from_pretrained(base_model, token=token)\n",
        "    except RecursionError:\n",
        "        tokenizer = AutoTokenizer.from_pretrained(\n",
        "            base_model, unk_token=\"<unk>\", token=token\n",
        "        )\n",
        "\n",
        "    model = model.merge_and_unload()\n",
        "\n",
        "    print(\"Saving target model\")\n",
        "    model.push_to_hub(trained_adapter, token=token)\n",
        "    tokenizer.push_to_hub(trained_adapter, token=token)\n",
        "    return gr.Markdown.update(\n",
        "        value=\"Model successfully merged and pushed! Please shutdown/pause this space\"\n",
        "    )"
      ],
      "metadata": {
        "id": "4uyqXPs-TaEv"
      },
      "execution_count": 2,
      "outputs": []
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      "source": [
        "merge(\"HuggingFaceH4/zephyr-7b-beta\", \"ahmed807762/zephyr-7b-vetdataset-finetuned\", \"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\")"
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              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
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        "headers = {\"Authorization\": \"Bearer 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\"}\n",
        "\n",
        "def query(payload):\n",
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        "\treturn response.json()\n",
        "\n",
        "output = query({\n",
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        "  \"options\": {\"wait_for_model\": True}\n",
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              "{'error': \"ahmed807762/DialoGPT-large-vetdataset-finetuned does not appear to have a file named config.json. Checkout 'https://huggingface.co/ahmed807762/DialoGPT-large-vetdataset-finetuned/f26f7e24002841f4e5b4c9bdb525a562a34fadd7' for available files.\"}"
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