FineTuningLlama.ipynb

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        "<a href=\"https://colab.research.google.com/github/DEADLYVISION30/datascience/blob/main/FineTuningLlama.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n",
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            "\u001b[?25hRequirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (0.4.0)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
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            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2023.10.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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            "Collecting tyro>=0.5.11 (from trl)\n",
            "  Downloading tyro-0.5.18-py3-none-any.whl (100 kB)\n",
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            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
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            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Collecting docstring-parser>=0.14.1 (from tyro>=0.5.11->trl)\n",
            "  Downloading docstring_parser-0.15-py3-none-any.whl (36 kB)\n",
            "Requirement already satisfied: rich>=11.1.0 in /usr/local/lib/python3.10/dist-packages (from tyro>=0.5.11->trl) (13.7.0)\n",
            "Collecting shtab>=1.5.6 (from tyro>=0.5.11->trl)\n",
            "  Downloading shtab-1.6.4-py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl) (0.1.2)\n",
            "Installing collected packages: bitsandbytes, shtab, pyarrow-hotfix, docstring-parser, dill, multiprocess, tyro, accelerate, datasets, trl, peft\n",
            "Successfully installed accelerate-0.24.1 bitsandbytes-0.41.2.post2 datasets-2.15.0 dill-0.3.7 docstring-parser-0.15 multiprocess-0.70.15 peft-0.6.2 pyarrow-hotfix-0.6 shtab-1.6.4 trl-0.7.4 tyro-0.5.18\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets peft accelerate bitsandbytes trl safetensors torch --no-cache"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from random import randrange\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(\"medalpaca/medical_meadow_medqa\", split=\"train\")\n",
        "\n",
        "print(f\"Dataset Size: {len(dataset)}\")\n",
        "print(dataset[randrange(len(dataset))])\n",
        "# Dataset Size: 10178"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
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        },
        "id": "XmjDz1vGqzLv",
        "outputId": "fc518333-dc9e-4424-d3a8-008d14d76d1c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.77k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce6f66a60e9c443eaa17333c1ccd436c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b3b65c1151a4a9297991dd6e136b33b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/10.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fc0ea4560df54000aff465b0eb2e38cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50a7bc9675124f379b481c7f68f0a2a1"
            }
          },
          "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": "a14f0a1bf9b148fda477a77eeffc7d23"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Size: 10178\n",
            "{'instruction': 'Please answer with one of the option in the bracket', 'input': \"Q:A 46-year-old man presents to the office complaining of dry, irritated eyes that have gotten worse over the last week. The patient states that he has also developed a red bumpy rash on his arms. On exam, his bilateral cornea and conjunctiva are dry and thickened. There are small ulcerations on the cornea. The skin of the bilateral arms has an erythematous rash characterized by small, white raised lesions. The patient has a history of alcoholism but has no other significant past medical history. What is most likely deficient in this patient?? \\n{'A': 'Folic acid', 'B': 'Vitamin B12', 'C': 'Vitamin K', 'D': 'Thiamine', 'E': 'Vitamin A'},\", 'output': 'E: Vitamin A'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def format_prompt(sample):\n",
        "    return f\"\"\"\n",
        "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",
        "\n",
        "### Instruction:\n",
        "{sample[\"instruction\"]}\n",
        "\n",
        "### Input:\n",
        "{sample[\"input\"]}\n",
        "\n",
        "### Response:\n",
        "{sample[\"output\"]}\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "h1p2W31prDtm"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from random import randrange\n",
        "\n",
        "print(format_prompt(dataset[randrange(len(dataset))]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-lft0IirU36",
        "outputId": "e5b48af0-e623-40fd-b554-afed228e39d6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "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",
            "\n",
            "### Instruction:\n",
            "Please answer with one of the option in the bracket\n",
            "\n",
            "### Input:\n",
            "Q:A 32-year-old African American woman comes to the physician because of fatigue and difficulty swallowing for 6 weeks. She also complains of painful discoloration in her fingers when exposed to cold weather. She has smoked one pack of cigarettes daily for 4 years. She appears younger than her stated age. Physical examination shows smooth, swollen fingers with small white calcifications on her fingertips bilaterally. This patient is at increased risk for which of the following complications?? \n",
            "{'A': 'Liver cirrhosis', 'B': 'Chronic obstructive pulmonary disease', 'C': 'Pulmonary hypertension', 'D': 'Aortic aneurysm', 'E': 'Chondrocalcinosis'},\n",
            "\n",
            "### Response:\n",
            "C: Pulmonary hypertension\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig"
      ],
      "metadata": {
        "id": "p5k5wEZKrjvt"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"TinyPixel/Llama-2-7B-bf16-sharded\"\n",
        "use_flash_attention = False\n",
        "\n",
        "# BitsAndBytesConfig int-4 config\n",
        "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.bfloat16,\n",
        ")\n",
        "\n",
        "# Load model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    use_cache=False,\n",
        "    use_flash_attention_2=use_flash_attention,\n",
        "    device_map=\"auto\",\n",
        "    torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "model.config.pretraining_tp = 1\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753,
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        "id": "mTxbgpqNr_TV",
        "outputId": "0f690909-23cf-4723-9259-359ed38e3742"
      },
      "execution_count": 7,
      "outputs": [
        {
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        {
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            ],
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            ],
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model\n",
        "\n",
        "# LoRA config based on QLoRA paper\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=32,\n",
        "    lora_dropout=0.1,\n",
        "    r=16,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "# Prepare model for training\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "model = get_peft_model(model, peft_config)"
      ],
      "metadata": {
        "id": "2cu8XjgAsk5H"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "args = TrainingArguments(\n",
        "    output_dir=\"finetuned-llama-7b-chat-hf-med\",\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=2,\n",
        "    gradient_checkpointing=True,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    logging_steps=10,\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-4,\n",
        "    fp16=True,\n",
        "    max_grad_norm=0.3,\n",
        "    warmup_ratio=0.03,\n",
        "    lr_scheduler_type=\"constant\",\n",
        "    disable_tqdm=False\n",
        ")"
      ],
      "metadata": {
        "id": "Me8bydtGuJ-A"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "max_seq_length = 1024 # max sequence length for model and packing of the dataset\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    packing=True,\n",
        "    formatting_func=format_prompt,\n",
        "    args=args,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAoFg09GuQAO",
        "outputId": "d0334291-2ba4-4819-af3c-d8da05d8aa44"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train\n",
        "trainer.train()\n",
        "\n",
        "# Save model\n",
        "trainer.save_model()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "WaZXx2mmuWxn",
        "outputId": "27adbe4d-e97b-4e8a-e279-b5ab6e5986b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast 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",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='143' max='3816' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 143/3816 1:05:14 < 28:19:33, 0.04 it/s, Epoch 0.11/3]\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>10</td>\n",
              "      <td>1.355000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.067800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.981800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.954400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.924000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.919800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.914800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.910900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.900400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.890300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.898200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.876600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>0.904300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.881500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}