flan_t5_large_finetuning_v2.ipynb

{
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWeS8SAZNnKi",
        "outputId": "731ce7b6-ce91-41e1-e66b-363dd1033047"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[?25h"
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        }
      ],
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ndS3nXxJNxu7"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoModelForSeq2SeqLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "2AULtP0pN-bC"
      },
      "outputs": [],
      "source": [
        "# The model that you want to train from the Hugging Face hub\n",
        "model_name = \"google/flan-t5-large\"\n",
        "\n",
        "# The instruction dataset to use\n",
        "dataset_name = \"mudata.json\"\n",
        "\n",
        "# Fine-tuned model name\n",
        "new_model = \"flan-t5-large-v2-vetdataset-finetuned\"\n",
        "\n",
        "################################################################################\n",
        "# QLoRA parameters\n",
        "################################################################################\n",
        "\n",
        "# LoRA attention dimension\n",
        "lora_r = 64\n",
        "\n",
        "# Alpha parameter for LoRA scaling\n",
        "lora_alpha = 16\n",
        "\n",
        "# Dropout probability for LoRA layers\n",
        "lora_dropout = 0.1\n",
        "\n",
        "################################################################################\n",
        "# bitsandbytes parameters\n",
        "################################################################################\n",
        "\n",
        "# Activate 4-bit precision base model loading\n",
        "use_4bit = True\n",
        "\n",
        "# Compute dtype for 4-bit base models\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "\n",
        "# Quantization type (fp4 or nf4)\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "\n",
        "# Activate nested quantization for 4-bit base models (double quantization)\n",
        "use_nested_quant = False\n",
        "\n",
        "################################################################################\n",
        "# TrainingArguments parameters\n",
        "################################################################################\n",
        "\n",
        "# Output directory where the model predictions and checkpoints will be stored\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 1\n",
        "\n",
        "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "\n",
        "# Batch size per GPU for training\n",
        "per_device_train_batch_size = 4\n",
        "\n",
        "# Batch size per GPU for evaluation\n",
        "per_device_eval_batch_size = 4\n",
        "\n",
        "# Number of update steps to accumulate the gradients for\n",
        "gradient_accumulation_steps = 1\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Maximum gradient normal (gradient clipping)\n",
        "max_grad_norm = 0.3\n",
        "\n",
        "# Initial learning rate (AdamW optimizer)\n",
        "learning_rate = 2e-4\n",
        "\n",
        "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
        "weight_decay = 0.001\n",
        "\n",
        "# Optimizer to use\n",
        "optim = \"paged_adamw_32bit\"\n",
        "\n",
        "# Learning rate schedule\n",
        "lr_scheduler_type = \"cosine\"\n",
        "\n",
        "# Number of training steps (overrides num_train_epochs)\n",
        "max_steps = -1\n",
        "\n",
        "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length\n",
        "# Saves memory and speeds up training considerably\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 0\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 25\n",
        "\n",
        "################################################################################\n",
        "# SFT parameters\n",
        "################################################################################\n",
        "\n",
        "# Maximum sequence length to use\n",
        "max_seq_length = None\n",
        "\n",
        "# Pack multiple short examples in the same input sequence to increase efficiency\n",
        "packing = False\n",
        "\n",
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhFZlq7TOKJE",
        "outputId": "e50f4ff4-6740-46ae-a9f0-b84fb3ca9bf6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Updated data with IDs written to updated_data.json\n"
          ]
        }
      ],
      "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}')"
      ]
    },
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        {
          "data": {
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              "model_id": "c842279280284275bf62cf814517e485",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
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        {
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            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
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          "metadata": {},
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train dataset size: 3861\n",
            "Test dataset size: 150\n"
          ]
        }
      ],
      "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"
      ]
    },
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            "text/plain": [
              "config.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of T5ForConditionalGeneration were not initialized from the model checkpoint at google/flan-t5-large and are newly initialized: ['decoder.embed_tokens.weight', 'encoder.embed_tokens.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
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          "data": {
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              "model_id": "a773ea6b064a41a7bb7618c0ef2d22a7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a7c93269849444e39b59d80ae04081d1",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d0b064a7dbac45f9a6719972bbc4eb63",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5aed8cfb2ceb4469a69547da9a09d58f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b92baa350ac9412e98ec6b29b145a06e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Load tokenizer and model with QLoRA configuration\n",
        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant,\n",
        ")\n",
        "\n",
        "# Check GPU compatibility with bfloat16\n",
        "if compute_dtype == torch.float16 and use_4bit:\n",
        "    major, _ = torch.cuda.get_device_capability()\n",
        "    if major >= 8:\n",
        "        print(\"=\" * 80)\n",
        "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
        "        print(\"=\" * 80)\n",
        "\n",
        "# Load base model\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "\n",
        "# Load LLaMA tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\n",
        "\n",
        "# Load LoRA configuration\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112,
          "referenced_widgets": [
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            "d4a0bf7157e649e8b1cda57cd814df20"
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        },
        "id": "YMRemthvOZ_l",
        "outputId": "ea95a95a-d2b6-4e87-fedc-a5a89ea46359"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max source length: 66\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9211c6b9a4504757b6d4d6664c3a92f7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/4011 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max target length: 138\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
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        "colab": {
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        "outputId": "919e2902-6c85-4513-fb53-710430086dfe"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4cd956a5b63c4f08a21b890016e2510c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/3861 [00:00<?, ? examples/s]"
            ]
          },
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          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "df78f09087cd471997897016a7512227",
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            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys of tokenized dataset: ['question', 'answer', 'id', 'input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ],
      "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)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
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          ]
        },
        "id": "zPkxsMXsROY3",
        "outputId": "69a39389-0520-4832-8ea7-5cfc274caf3f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db4342ba417c436e98af3456290257eb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/3861 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf01dfae8566472382de156c4db4c635",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "tokenized_dataset = tokenized_dataset.map(lambda example: {'text': example['question'] + example['answer']})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166,
          "referenced_widgets": [
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            "f36513ae193b43f9b3f6221f811ec983"
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        },
        "id": "D8xw6BPhPwxM",
        "outputId": "d07ac151-052d-4d23-e3a2-2c1949ab32ba"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:159: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 512\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6069b58afaf4cffa2266e63997c507e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/3861 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    weight_decay=weight_decay,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "# Set supervised fine-tuning parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=packing,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sKqgADV7QJSg",
        "outputId": "748a8e71-cf01-4b42-958f-6d978d619f3e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "You're using a T5TokenizerFast 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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='966' max='966' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [966/966 11:59, Epoch 1/1]\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>25</td>\n",
              "      <td>0.397000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.288600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.036700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.043200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>0.014100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>0.017400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>0.008300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.025300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>0.008200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>0.008200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>0.007500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.072600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>0.006300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>0.017300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>0.011300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.007500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>425</td>\n",
              "      <td>0.006700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>0.011400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>475</td>\n",
              "      <td>0.004100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.029600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>525</td>\n",
              "      <td>0.004800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>0.009900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>575</td>\n",
              "      <td>0.006700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.094300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>625</td>\n",
              "      <td>0.005200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>0.011800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>675</td>\n",
              "      <td>0.007000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.011300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>725</td>\n",
              "      <td>0.006500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>0.006100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>775</td>\n",
              "      <td>0.002900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.010100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>825</td>\n",
              "      <td>0.005100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>0.009000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>875</td>\n",
              "      <td>0.005800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.005400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>925</td>\n",
              "      <td>0.006000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>0.008100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=966, training_loss=0.0321119126186978, metrics={'train_runtime': 726.3185, 'train_samples_per_second': 5.316, 'train_steps_per_second': 1.33, 'total_flos': 463853122375680.0, 'train_loss': 0.0321119126186978, 'epoch': 1.0})"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train model\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "GXTeaoKQSQac"
      },
      "outputs": [],
      "source": [
        "# Save trained model\n",
        "trainer.model.save_pretrained(new_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5U2UAiTLSyMk",
        "outputId": "65770fb6-d278-463b-f238-d1746562bf64"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>[INST] Please answer the following medical question: What is rabies? [/INST]] str>[INST] s>[INST] str>[INST] Please answer the following medical question: What is rabies? [/INST] s>[INST] str>[INST] str>[INST] str] Please answer the following medical question: What is rabies? [/INST] str>[INST] str] Please answer the following medical question: What is rabies? str>[INST] str] Please answer the following medical question: What is rabies? str>[INST] st\n"
          ]
        }
      ],
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "prompt = \"Please answer the following medical question: What is rabies?\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ar8ep702VFIh",
        "outputId": "eea0098d-567f-4822-8020-ed9b419bab27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Empty VRAM\n",
        "del model\n",
        "del pipe\n",
        "del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "3P7EHmdMVZhw"
      },
      "outputs": [],
      "source": [
        "# Reload model in FP16 and merge it with LoRA weights\n",
        "base_model = AutoModelForSeq2SeqLM.from_pretrained(\n",
        "    model_name,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=device_map,\n",
        ")\n",
        "model = PeftModel.from_pretrained(base_model, new_model)\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "# Reload tokenizer to save it\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "c56e7eb701f4419b88578e0e966b7996",
            "11448e04641d432f8c35a40eee52660e",
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            "4bfb3fd3dabf435295af11eaf9933e44",
            "c9f57c9aca1e4c6294afd9f5e3187ba7",
            "d316437dbaf942fab89b0276ae0383bc",
            "171dc6fbd426463ea69bbce9d5f61498",
            "3f1c6bf1e6d5444d8856f0dc8f5eb83c",
            "afd81a625705485db97053ec6db8bbf7",
            "ffba00505ba04359befe9e6ad030c870",
            "1bf4316a8ee04c33a39dd05c4f9ace3d",
            "2dc6e0bba3484b53bc645b2bf0c79cb1",
            "85aa0b5b2794456d8e8d7b8532c94cd6",
            "f8450df724bb4e8fbfabd3e2fb9f87d7",
            "67159f40e98c4204a3ce4c11c1efc42b",
            "b16a1334df38438d9434930fd3de7263",
            "3c07907f022442168bae7fdc3a1ea9e6",
            "834da0a066074f3fbbad7bde4892691f",
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            "00b3cb5cfd674873a917868e6eb7c002",
            "823b7a30c95c43679ade78cc192b13e5",
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            "67053aeda39741d78267d112ea063266",
            "bb47027cc23a4471979b7e5fcb217b96",
            "3cacbe6784de4e0fad6ff9429627f7f9",
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            "865eed07b6804b7197c61c1e34930d9a",
            "3a1beabbf0e34aebb87552655540128e",
            "3798afd9d0c04146ad6f50c1c9eba5c0",
            "9b1c3dbde20a41b6a322bd8f9d47732e",
            "7e2d2805fdfc412bb8e2e3f9f6704f00",
            "930a6e3971624e548679ff5f72cc4950"
          ]
        },
        "id": "9ICRmbRvVc1x",
        "outputId": "b0a0d229-c087-43f8-cbbe-b68ab1e4b287"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c56e7eb701f4419b88578e0e966b7996",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "77733035f6054fefb9e704d2812597e7",
            "655c0e3f49924c0da8478fa0b6f8508e",
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            "f05eee8ac4e24132ba521aa2a85cc804",
            "088b051991aa456a91a01e40f13781fd",
            "f736b42b08a247bebfb03bc724dd8b1a",
            "3ffb86fe3baa4b2ba73ce9b019cb2505",
            "aa8eb7948f9945d59ed21e4631710f75"
          ]
        },
        "id": "qQK-yZkAVhiR",
        "outputId": "0f475d1a-afc5-4b88-ddac-874a3f45bf93"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "77733035f6054fefb9e704d2812597e7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/1.84G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ahmed807762/flan-t5-large-v2-vetdataset-finetuned/commit/448c01978d334005bc2ed480c4d9c8f347bf54a7', commit_message='Upload tokenizer', commit_description='', oid='448c01978d334005bc2ed480c4d9c8f347bf54a7', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.push_to_hub(new_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(new_model, use_temp_dir=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-8qedJVVkNU",
        "outputId": "e06c8cdf-00e4-4cc0-ce48-11bedae0d33b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>[INST] What is rabies? [/INST]\n"
          ]
        }
      ],
      "source": [
        "# Run text generation pipeline with our next model\n",
        "prompt = \"What is rabies?\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EhNeRloCWFbJ"
      },
      "outputs": [],
      "source": []
    }
  ],
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    "accelerator": "GPU",
    "colab": {
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