FineTuning_phi-1_5_with_PRFT_LoRA_runned.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onFu1QCyBp5d"
      },
      "source": [
        "### Checkout my [Twitter(@rohanpaul_ai)](https://twitter.com/rohanpaul_ai) for daily LLM bits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9C0uT9BjBp5h"
      },
      "source": [
        "##  Fine Tuning Phi 1_5 with PEFT and QLoRA\n",
        "\n",
        "# [Link to my Youtube Video Explaining this whole Notebook](https://www.youtube.com/watch?v=J0RbOtLrJhQ&list=PLxqBkZuBynVTzqUQCQFgetR97y1X_1uCI&index=10&ab_channel=Rohan-Paul-AI)\n",
        "\n",
        "[![Imgur](https://imgur.com/ensYOlt.png)](https://www.youtube.com/watch?v=J0RbOtLrJhQ&list=PLxqBkZuBynVTzqUQCQFgetR97y1X_1uCI&index=10&ab_channel=Rohan-Paul-AI)\n",
        "\n"
      ]
    },
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          "base_uri": "https://localhost:8080/"
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          "iopub.status.idle": "2023-10-05T16:07:52.741784Z",
          "shell.execute_reply": "2023-10-05T16:07:52.740692Z",
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        "id": "Z-jiAReD2env",
        "outputId": "50cd238d-c5c7-4eb7-8ee3-dfc18c3de0db",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: bitsandbytes, pyarrow-hotfix, einops, dill, multiprocess, accelerate, transformers, datasets, peft\n",
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            "    Found existing installation: transformers 4.35.2\n",
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            "      Successfully uninstalled transformers-4.35.2\n",
            "Successfully installed accelerate-0.25.0 bitsandbytes-0.41.3.post2 datasets-2.16.1 dill-0.3.7 einops-0.7.0 multiprocess-0.70.15 peft-0.7.1 pyarrow-hotfix-0.6 transformers-4.36.2\n"
          ]
        }
      ],
      "source": [
        "!pip install accelerate transformers einops datasets peft bitsandbytes --upgrade"
      ]
    },
    {
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      "source": [
        "# from huggingface_hub import notebook_login\n",
        "\n",
        "# notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "execution": {
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      "source": [
        "from peft import PeftModel\n",
        "from transformers import AutoModelForCausalLM\n",
        "import torch\n",
        "from datasets import load_dataset, Dataset\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling, BitsAndBytesConfig\n",
        "from peft import LoraConfig, get_peft_model\n",
        "import os"
      ]
    },
    {
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      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True)\n",
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:16.575290Z",
          "iopub.status.busy": "2023-10-05T16:08:16.574632Z",
          "iopub.status.idle": "2023-10-05T16:08:42.343596Z",
          "shell.execute_reply": "2023-10-05T16:08:42.342759Z",
          "shell.execute_reply.started": "2023-10-05T16:08:16.575258Z"
        },
        "id": "q29CmvRzA996",
        "trusted": true,
        "outputId": "49cdb7c9-3a64-4e3f-b0c3-71b8bd2c1de1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284,
          "referenced_widgets": [
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        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/732 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "257627f25d5f4e3e8d2f21b0fa66cc37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "configuration_phi.py:   0%|          | 0.00/2.03k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7f8e592a2f248ad8aebb38c52f748d5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-1_5:\n",
            "- configuration_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modeling_phi.py:   0%|          | 0.00/33.5k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2660e02c6fa04598a58fecb8b1cbf7c6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-1_5:\n",
            "- modeling_phi.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/2.84G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a4f1d01bb8944dcb5931dd5177c6b5f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/69.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "990cf61133984148b159e5ecdbb37026"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"microsoft/phi-1_5\",\n",
        "    device_map={\"\":0},\n",
        "    trust_remote_code=True,\n",
        "    quantization_config=bnb_config\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:42.345373Z",
          "iopub.status.busy": "2023-10-05T16:08:42.345038Z",
          "iopub.status.idle": "2023-10-05T16:08:42.349369Z",
          "shell.execute_reply": "2023-10-05T16:08:42.348495Z",
          "shell.execute_reply.started": "2023-10-05T16:08:42.345342Z"
        },
        "id": "M75zCiGwBM1q",
        "outputId": "c7829079-3970-4fd1-fc0d-214c4a02e7aa",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PhiForCausalLM(\n",
              "  (transformer): PhiModel(\n",
              "    (embd): Embedding(\n",
              "      (wte): Embedding(51200, 2048)\n",
              "      (drop): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (h): ModuleList(\n",
              "      (0-23): 24 x ParallelBlock(\n",
              "        (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (resid_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (mixer): MHA(\n",
              "          (rotary_emb): RotaryEmbedding()\n",
              "          (Wqkv): Linear4bit(in_features=2048, out_features=6144, bias=True)\n",
              "          (out_proj): Linear4bit(in_features=2048, out_features=2048, bias=True)\n",
              "          (inner_attn): SelfAttention(\n",
              "            (drop): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "          (inner_cross_attn): CrossAttention(\n",
              "            (drop): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (mlp): MLP(\n",
              "          (fc1): Linear4bit(in_features=2048, out_features=8192, bias=True)\n",
              "          (fc2): Linear4bit(in_features=8192, out_features=2048, bias=True)\n",
              "          (act): NewGELUActivation()\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (lm_head): CausalLMHead(\n",
              "    (ln): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "    (linear): Linear(in_features=2048, out_features=51200, bias=True)\n",
              "  )\n",
              "  (loss): CausalLMLoss(\n",
              "    (loss_fct): CrossEntropyLoss()\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NgdhY3thBp5m"
      },
      "source": [
        "## A little hack to make `get_peft_model()` method faster by avoiding `kaiming_uniform` initialization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "NoFMmaW_Bp5m"
      },
      "outputs": [],
      "source": [
        "from bitsandbytes.nn.modules import Linear8bitLt, Linear4bit\n",
        "import torch.nn.init as init  # Importing the init module from PyTorch\n",
        "import bitsandbytes as bnb\n",
        "from contextlib import contextmanager\n",
        "\n",
        "def noop (x=None, *args, **kwargs):\n",
        "    \"Do nothing\"\n",
        "    return x\n",
        "\n",
        "@contextmanager\n",
        "def no_kaiming():\n",
        "    old_iku = init.kaiming_uniform_\n",
        "    init.kaiming_uniform_ = noop\n",
        "    try: yield\n",
        "    finally: init.kaiming_uniform_ = old_iku\n",
        "\n",
        "_old_8init = Linear8bitLt.__init__\n",
        "_old_4init = Linear4bit.__init__\n",
        "\n",
        "def _new_4init(self, input_features, output_features, bias=True,\n",
        "               device=None, **kwargs):\n",
        "    with no_kaiming():\n",
        "        return _old_4init(self, input_features, output_features, bias=bias,\n",
        "                          device=device, **kwargs)\n",
        "\n",
        "\n",
        "\n",
        "def _new_8init(self, input_features, output_features, bias=True, has_fp16_weights=True,\n",
        "              memory_efficient_backward=False, threshold=0.0, index=None, device=None):\n",
        "    with no_kaiming():\n",
        "        return _old_8init(self, input_features, output_features, bias=bias, has_fp16_weights=has_fp16_weights,\n",
        "                          memory_efficient_backward=memory_efficient_backward, threshold=threshold, index=index, device=device)\n",
        "\n",
        "Linear8bitLt.__init__ = _new_8init\n",
        "Linear4bit.__init__ = _new_4init"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:42.351464Z",
          "iopub.status.busy": "2023-10-05T16:08:42.350623Z",
          "iopub.status.idle": "2023-10-05T16:08:45.749460Z",
          "shell.execute_reply": "2023-10-05T16:08:45.748537Z",
          "shell.execute_reply.started": "2023-10-05T16:08:42.351434Z"
        },
        "id": "MVSrZLrUBegg",
        "outputId": "3f4a3c68-608b-47ff-9eae-5a51abd31bef",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 4,718,592 || all params: 1,422,989,312 || trainable%: 0.3315971497613047\n"
          ]
        }
      ],
      "source": [
        "lora_config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=16,\n",
        "    target_modules=[\"Wqkv\", \"out_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, lora_config)\n",
        "\n",
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgTdMcQ2Bp5n"
      },
      "source": [
        "![](https://github.com/yuanlei6616/LLM-FineTuning-Large-Language-Models/blob/main/assets/lora-4.png?raw=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:45.751607Z",
          "iopub.status.busy": "2023-10-05T16:08:45.750980Z",
          "iopub.status.idle": "2023-10-05T16:08:45.755790Z",
          "shell.execute_reply": "2023-10-05T16:08:45.754945Z",
          "shell.execute_reply.started": "2023-10-05T16:08:45.751574Z"
        },
        "id": "QYNq0WmSKQ0Q",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:45.760071Z",
          "iopub.status.busy": "2023-10-05T16:08:45.759211Z",
          "iopub.status.idle": "2023-10-05T16:08:45.768975Z",
          "shell.execute_reply": "2023-10-05T16:08:45.768104Z",
          "shell.execute_reply.started": "2023-10-05T16:08:45.759984Z"
        },
        "id": "mnbKMOIO9pWr",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def tokenize(sample):\n",
        "    tokenized_text =  tokenizer(sample[\"text\"], padding=True, truncation=True, max_length=512)\n",
        "    return tokenized_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:45.784548Z",
          "iopub.status.busy": "2023-10-05T16:08:45.783733Z",
          "iopub.status.idle": "2023-10-05T16:08:47.790140Z",
          "shell.execute_reply": "2023-10-05T16:08:47.789080Z",
          "shell.execute_reply.started": "2023-10-05T16:08:45.784516Z"
        },
        "trusted": true,
        "id": "tRA6LqrgBp5n",
        "outputId": "07e3e002-ac68-41e4-c216-647791987810",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "5f6350a774a641b0835474401aba7633",
            "398c4779902d4dad91cd9b997c10153c",
            "d2c28ddf062f4d52b30e131e5d7bb89c",
            "7b52cdfb272c4e03805e4fc9348cfb14",
            "e49795cb428e41cbae3c988a6dd020a6",
            "680e98d4877b47988e64b20c96b9eda4",
            "25a7af712b934ad098765a3eb9af152e",
            "c9fc7089fe344f3c9deec022a38bb5b1",
            "a684bc011aef428299cc9810d133cb42",
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            "65d42c66227d4eceb0bc31b552a656c9",
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            "4f50c0b4cac84726a8da9c5c13987a0a",
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            "d677a298dc9e42d59320e86f56719203",
            "ab3dfa2f03694dfd9ef56b5140f94be8",
            "bcc2ddc649ea40f6a8d92112bec89414",
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            "e22603cb54584c6c949aa9cd57d25385",
            "fef76a294c224969b1bad20973024025"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/940k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f6350a774a641b0835474401aba7633"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6839de7a2ff14fde87e270e4f174fcdc"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "data = load_dataset(\"BI55/MedText\", \"default\", split=\"train\")\n",
        "\n",
        "data_df = data.to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:47.791843Z",
          "iopub.status.busy": "2023-10-05T16:08:47.791453Z",
          "iopub.status.idle": "2023-10-05T16:08:47.811861Z",
          "shell.execute_reply": "2023-10-05T16:08:47.811336Z",
          "shell.execute_reply.started": "2023-10-05T16:08:47.791790Z"
        },
        "trusted": true,
        "id": "DIPwTbyuBp5o"
      },
      "outputs": [],
      "source": [
        "data_df[\"text\"] = data_df[[\"Prompt\", \"Completion\"]].apply(lambda x: \"Prompt: \" + x[\"Prompt\"] + \" Completion: \" + x[\"Completion\"], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120,
          "referenced_widgets": [
            "a6bc9229071046c891b68c1dd8fc368a",
            "b778e0d301be4d988e4f9f304d9dfe2c",
            "f03dc930e7404676989a2c1a09f483fd",
            "dafa74800c7b45618923a8331ed1d00b",
            "45c87f619cdc4c748a51ad32c22e44bc",
            "a738d39a8ad240d1a7e828db30da676b",
            "6253397475694293b03caf10d634b4b5",
            "d589e93accf6404f82029d0663618a50",
            "0467d05a93f74b0d9f45bff63ffb987b",
            "35383be1bc784171bbbe15a0a76ecc88",
            "bf87128e6a154238a9705f7d49b4cde6"
          ]
        },
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:47.813797Z",
          "iopub.status.busy": "2023-10-05T16:08:47.813047Z",
          "iopub.status.idle": "2023-10-05T16:08:48.446310Z",
          "shell.execute_reply": "2023-10-05T16:08:48.445389Z",
          "shell.execute_reply.started": "2023-10-05T16:08:47.813765Z"
        },
        "id": "fjszkbOx9YnY",
        "outputId": "64515811-e376-4877-957e-09b25e58d16f",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing data:   0%|          | 0/1412 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6bc9229071046c891b68c1dd8fc368a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input_ids', 'attention_mask'],\n",
              "    num_rows: 1412\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "data = Dataset.from_pandas(data_df)\n",
        "\n",
        "tokenized_data = data.map(tokenize, batched=True, desc=\"Tokenizing data\", remove_columns=data.column_names)\n",
        "\n",
        "tokenized_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:48.449569Z",
          "iopub.status.busy": "2023-10-05T16:08:48.449313Z",
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          "shell.execute_reply": "2023-10-05T16:08:48.455977Z",
          "shell.execute_reply.started": "2023-10-05T16:08:48.449546Z"
        },
        "id": "5ZVmWzSn-BO2",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "training_arguments = TrainingArguments(\n",
        "        output_dir=\"phi-1_5-finetuned-med-text\",\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=1,\n",
        "        learning_rate=2e-4,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        logging_steps=100,\n",
        "        max_steps=1000,\n",
        "        num_train_epochs=1\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 772
        },
        "execution": {
          "iopub.execute_input": "2023-10-05T16:08:48.458863Z",
          "iopub.status.busy": "2023-10-05T16:08:48.458159Z",
          "iopub.status.idle": "2023-10-05T16:11:02.764033Z",
          "shell.execute_reply": "2023-10-05T16:11:02.763200Z",
          "shell.execute_reply.started": "2023-10-05T16:08:48.458832Z"
        },
        "id": "hzggVLwo24vi",
        "outputId": "a3430319-df9c-4f53-971e-753db33b284d",
        "trusted": true
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a CodeGenTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='834' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 834/1000 08:47 < 01:45, 1.58 it/s, Epoch 2.36/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.571600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.453000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.429000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.363700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.351200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.334300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.341000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.274200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1000/1000 10:34, Epoch 2/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.571600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.453000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.429000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.363700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.351200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.334300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.341000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.274200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.257900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.269500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1000, training_loss=1.364548599243164, metrics={'train_runtime': 637.3394, 'train_samples_per_second': 6.276, 'train_steps_per_second': 1.569, 'total_flos': 7368619896422400.0, 'train_loss': 1.364548599243164, 'epoch': 2.83})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_data,\n",
        "    args=training_arguments,\n",
        "    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "trainer.train()\n",
        "\n",
        "# trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDgGcSbwBp5o"
      },
      "source": [
        "## Saving"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "trusted": true,
        "id": "W7rv2vHqBp5o"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"phi-1_5-finetuned-med-text\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-05T16:11:02.855190Z",
          "iopub.status.busy": "2023-10-05T16:11:02.853182Z",
          "iopub.status.idle": "2023-10-05T16:11:29.082578Z",
          "shell.execute_reply": "2023-10-05T16:11:29.081380Z",
          "shell.execute_reply.started": "2023-10-05T16:11:02.855158Z"
        },
        "id": "gztQI9_vH06k",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True, torch_dtype=torch.float32)\n",
        "\n",
        "peft_model = PeftModel.from_pretrained(model, \"phi-1_5-finetuned-med-text\", from_transformers=True)\n",
        "\n",
        "model = peft_model.merge_and_unload()\n",
        "\n",
        "# model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-10-05T16:11:29.085158Z",
          "iopub.status.busy": "2023-10-05T16:11:29.083840Z",
          "iopub.status.idle": "2023-10-05T16:11:54.426258Z",
          "shell.execute_reply": "2023-10-05T16:11:54.424992Z",
          "shell.execute_reply.started": "2023-10-05T16:11:29.085125Z"
        },
        "id": "_RD3fKuGI5pY",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"phi-1_5-finetuned-med-text\")\n",
        "\n",
        "# model.push_to_hub(\"llm-exp/phi-1_5-finetuned-med-text\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRw7KSuVBp5o"
      },
      "source": [
        "## The overall flow\n",
        "\n",
        "1. load the base model\n",
        "\n",
        "2. train the base model\n",
        "\n",
        "3. save the LoRA adapter\n",
        "\n",
        "4. reload the base model at full precision\n",
        "\n",
        "5. merge the LoRA weights with the base model\n",
        "\n",
        "6. save\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xkYdH7rOJI7S"
      },
      "source": [
        "# Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2023-10-05T16:11:54.428265Z",
          "iopub.status.busy": "2023-10-05T16:11:54.427898Z"
        },
        "id": "zZ5SXmwX3_w9",
        "outputId": "f134caa1-3a00-43e1-ddeb-dedce5a2ea45",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I am having a headache with but no fever - what could be the cause?\n",
            "\n",
            "Headaches can be caused by a variety of factors, including stress, dehydration, lack of sleep, or even certain foods. Without more information, it's difficult to say for sure what's causing your headache. However, it's possible that you're dehydrated, so drinking more water could help. You could also try taking a break from screens or other activities that might be causing stress. If the headache persists, it's a good idea to consult a healthcare professional.\n",
            "\n",
            "I hope this helps! Let me know if you have any other questions.\n",
            "\n",
            "Best regards,\n",
            "[Your Name]\n",
            "\n",
            "Reply 2:\n",
            "Subject: Re: Headache with no fever - what could be the cause?\n",
            "\n",
            "Dear [Your Name],\n",
            "\n",
            "Thank you for your email. I appreciate your suggestions for managing my headache. I'll definitely try drinking more water and taking a break from screens. If the headache persists, I'll make an appointment with my doctor.\n",
            "\n",
            "Thanks again for your help!\n",
            "\n",
            "Best regards,\n",
            "[Reader's Name]\n",
            "\n",
            "Real-world use case:\n",
            "\n",
            "Scenario: A group of middle school students are discussing the importance of staying hydrated during the summer months. One student, Alex, mentions that they often forget to drink water and end up feeling tired and sluggish. Another student, Maya, suggests setting reminders on their phone to drink water throughout the day.\n",
            "\n",
            "Conversation:\n",
            "Alex: \"I always forget to drink water during the summer. I feel so tired and sluggish.\"\n",
            "Maya: \"That's because you're dehydrated! You should try setting reminders on your phone to drink water throughout the day.\"\n",
            "Alex: \"That's a good idea. I'll try that.\"\n",
            "Another student, Jake, chimes in: \"I always carry a water bottle with me wherever I go. It's a good way to make sure I'm drinking enough water.\"\n",
            "Maya: \"That's a great idea, Jake! I should start doing that too.\"\n",
            "\n",
            "Debate:\n",
            "The group then starts discussing the pros and cons of drinking water versus other beverages, such as soda or juice. Some students argue that soda is more enjoyable to drink and tastes better, while others point out that it's not as hydrating and can actually dehydrate you.\n",
            "\n",
            "Opinions:\n",
            "- \"I think soda is fine in moderation, but I try to\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"phi-1_5-finetuned-med-text\", trust_remote_code=True, torch_dtype=torch.float32)\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/phi-1_5\", trust_remote_code=True)\n",
        "\n",
        "inputs = tokenizer('I am having a headache with but no fever - what could be the cause', return_tensors=\"pt\", return_attention_mask=False)\n",
        "\n",
        "outputs = model.generate(**inputs, max_length=512)\n",
        "\n",
        "text = tokenizer.batch_decode(outputs)[0]\n",
        "\n",
        "print(text)\n"
      ]
    }
  ],
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