phi_2_finetuning.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mixOFaozV560",
        "outputId": "2603ff84-560e-40f3-ed25-f79d589aa6d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m536.6/536.6 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.5/8.5 MB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip3 install -q -U bitsandbytes==0.42.0\n",
        "!pip3 install -q -U peft==0.8.2\n",
        "!pip3 install -q -U trl==0.7.10\n",
        "!pip3 install -q -U accelerate==0.27.1\n",
        "!pip3 install -q -U datasets==2.17.0\n",
        "!pip3 install -q -U transformers==4.38.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
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            "027cb8c76e6741c9937d05bc2905c58e"
          ]
        },
        "id": "1NRNqZEEUW1p",
        "outputId": "e31a73fa-f9a9-4e86-a3f3-0a492a954d5f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fed6c08ae5ff42d1bc806a886d612e50",
              "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": 3,
      "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": 4,
      "metadata": {
        "id": "2AULtP0pN-bC"
      },
      "outputs": [],
      "source": [
        "# The model that you want to train from the Hugging Face hub\n",
        "model_name = \"microsoft/phi-2\"\n",
        "\n",
        "# The instruction dataset to use\n",
        "dataset_name = \"mudata.json\"\n",
        "\n",
        "# Fine-tuned model name\n",
        "new_model = \"phi-2-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": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhFZlq7TOKJE",
        "outputId": "60dd59b2-848a-4466-ca7c-4fcc3f3b58fa"
      },
      "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}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
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        },
        "id": "TWCJF2hmONrT",
        "outputId": "bf5193ca-1644-4c58-9c08-dcba8d98d5a8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b5ed7fec60148d2a7df0ba308005aef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train dataset size: 5732\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        },
        "id": "ArR4SH9AOQvZ",
        "outputId": "2f39aaa7-d575-40c4-ae9f-7ac6d06602ee"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5fcc0e953c8e42648602a7559abd979a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/863 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "866b866ec6b04af18cb80cc8722ebfec",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "configuration_phi.py:   0%|          | 0.00/9.26k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3564e81a9234160b82c5aade6352563",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modeling_phi.py:   0%|          | 0.00/62.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\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",
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n",
            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52f7c03137b0402693e6136cef44dcd7",
              "version_major": 2,
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            },
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/35.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c00c7b46a8fa4796a70c73e5f98a9b47",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8d773eb5398044288f4642fd766d3f55",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2a48d966e3b94af381db75563639a696",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/564M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7461dcb8121444e2a46f08a41cfbb9ba",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "efe756e753454ee4b27d6ce7b0fcaf23",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "3931807ad6e1443db1bb7afb900c1b7c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/7.34k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "27558597e10c4bde8b5f37309232c100",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "cbd36d87a9a7485e82acfc40befbae1c",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "528fce9258aa4fb2af50dece033dfc1d",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8302ba7a68ec4a87acd9938ddac7fe8b",
              "version_major": 2,
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "578dde45561a4ad4aeb21dfef02cface",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[]\n"
          ]
        }
      ],
      "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",
        "# ...\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16, load_in_8bit=True)\n",
        "# ...\n",
        "# model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config, device_map=device_map)\n",
        "\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "\n",
        "# Load LLaMA tokenizer\n",
        "# ...\n",
        "tokenizer = AutoTokenizer.from_pretrained(\n",
        "    model_name,\n",
        "    padding_side=\"left\",\n",
        "    add_eos_token=True,\n",
        "    add_bos_token=True,\n",
        "    use_fast=False, # needed for now, should be fixed soon\n",
        ")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "# ...\n",
        "# tokenizer = AutoTokenizer.from_pretrained(model_name, add_eos_token=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",
        "\n",
        "import bitsandbytes as bnb\n",
        "def find_all_linear_names(model):\n",
        "  cls = bnb.nn.Linear4bit #if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear)\n",
        "  lora_module_names = set()\n",
        "  for name, module in model.named_modules():\n",
        "    if isinstance(module, cls):\n",
        "      names = name.split('.')\n",
        "      lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
        "    if 'lm_head' in lora_module_names: # needed for 16-bit\n",
        "      lora_module_names.remove('lm_head')\n",
        "  return list(lora_module_names)\n",
        "\n",
        "modules = find_all_linear_names(model)\n",
        "print(modules)\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    r=32,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\n",
        "        \"Wqkv\",\n",
        "        \"fc1\",\n",
        "        \"fc2\",\n",
        "    ],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "0ed806e33d724011abe8e0e5024696ca",
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        "id": "YMRemthvOZ_l",
        "outputId": "abba2b9e-39f7-4ff8-c2f3-a71e161514e1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0ed806e33d724011abe8e0e5024696ca",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/5882 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max source length: 52\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7d475334ce364b66a5661d40c5d08004",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/5882 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max target length: 126\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",
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        "colab": {
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        },
        "id": "cMGlW0KrPdfo",
        "outputId": "0351914d-80b5-4c6b-92df-2648a7bfe2d7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9dc460625a6a4badb5aa3c3537664ae1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/5732 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "69befb55edb748bdac5022d81c3c641b",
              "version_major": 2,
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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": "889f85c8770d493f9615a58d52cf16b8",
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            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
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          },
          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys of tokenized dataset: ['id', 'answer', 'question', '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": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "zPkxsMXsROY3",
        "outputId": "618ef06c-300e-4be7-e56d-c009eaa69d65"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34faa4e29fd04b04bcd0d6affe55c3f5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/5732 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f1225f11f57c488f86db3fb1b736b257",
              "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": {
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            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "tokenized_dataset = tokenized_dataset.map(lambda example: {'text': example['question'] + example['answer']})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
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          ]
        },
        "id": "D8xw6BPhPwxM",
        "outputId": "ebb690cc-dbad-4464-8750-096b397ae011"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:223: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5b56eba6b19d41d4aa3385360cda05a4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/5732 [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": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sKqgADV7QJSg",
        "outputId": "0e621a10-0e87-49a4-c47e-35f615c4ee06"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1433' max='1433' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1433/1433 23:22, 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>2.410500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>2.379000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>2.127200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>2.036500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>1.984500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.978900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>1.883900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.968000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>1.778100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.661300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>1.703100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.802300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>1.739700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>1.793200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>1.609000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.729800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>425</td>\n",
              "      <td>1.652100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>1.768400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>475</td>\n",
              "      <td>1.640300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.798000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>525</td>\n",
              "      <td>1.496400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>1.651900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>575</td>\n",
              "      <td>1.544800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.596200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>625</td>\n",
              "      <td>1.585000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>1.717400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>675</td>\n",
              "      <td>1.525000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.747000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>725</td>\n",
              "      <td>1.584300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>1.677000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>775</td>\n",
              "      <td>1.562200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.581000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>825</td>\n",
              "      <td>1.487700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>1.605500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>875</td>\n",
              "      <td>1.368900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.561500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>925</td>\n",
              "      <td>1.485300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>1.619200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>975</td>\n",
              "      <td>1.567100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.687500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1025</td>\n",
              "      <td>1.497100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1050</td>\n",
              "      <td>1.675300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1075</td>\n",
              "      <td>1.368300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.571200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1125</td>\n",
              "      <td>1.454100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1150</td>\n",
              "      <td>1.476900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1175</td>\n",
              "      <td>1.412700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.459700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1225</td>\n",
              "      <td>1.385000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>1.567900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1275</td>\n",
              "      <td>1.392200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>1.544500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1325</td>\n",
              "      <td>1.367600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1350</td>\n",
              "      <td>1.515200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1375</td>\n",
              "      <td>1.524400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>1.496300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1425</td>\n",
              "      <td>1.417800</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=1433, training_loss=1.653656442433242, metrics={'train_runtime': 1406.3246, 'train_samples_per_second': 4.076, 'train_steps_per_second': 1.019, 'total_flos': 3963899900190720.0, 'train_loss': 1.653656442433242, 'epoch': 1.0})"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Train model\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "GXTeaoKQSQac"
      },
      "outputs": [],
      "source": [
        "# Save trained model\n",
        "trainer.model.save_pretrained(new_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "UIGNkEhpTMCj"
      },
      "outputs": [],
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5U2UAiTLSyMk",
        "outputId": "1c44aded-cec7-470b-f05d-71a1c261bcf2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>[INST] Please answer the following medical question: What is rabies? [/INST]\n"
          ]
        }
      ],
      "source": [
        "# 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": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ar8ep702VFIh",
        "outputId": "5d40c98b-9cb0-4312-bc92-de98d0a62685"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "22356"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Empty VRAM\n",
        "del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "0a328c7d414b4bcab435cbd26ec4aebe",
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            "7ec0ac3ac5064cd2b157109d8c7c9700",
            "07e7ca81d44a4486ab13140d986daeeb",
            "fcbccf8c351241e6a4aa3346be8419dd",
            "fb65940688004f7895a0eb9b487c7378"
          ]
        },
        "id": "3P7EHmdMVZhw",
        "outputId": "8f5db4a0-bc0f-4638-cc71-1ec8827b730a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a328c7d414b4bcab435cbd26ec4aebe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Reload model in FP16 and merge it with LoRA weights\n",
        "base_model = AutoModelForCausalLM.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",
        "\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(\n",
        "    model_name,\n",
        "    padding_side=\"left\",\n",
        "    add_eos_token=True,\n",
        "    add_bos_token=True,\n",
        "    use_fast=False, # needed for now, should be fixed soon\n",
        ")\n",
        "tokenizer.pad_token = tokenizer.eos_token\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": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
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        },
        "id": "qQK-yZkAVhiR",
        "outputId": "ce0ff7d7-1c45-4d57-a1aa-fef4ac5ad8bf"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "331d9c8577564b26b8326a2377182c07",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2061d519980d4c4881b2403ca6815172",
              "version_major": 2,
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            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/564M [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "048ab2a99bde49b38599fdd1282280d3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
            ]
          },
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        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ahmed807762/phi-2-vetdataset-finetuned/commit/c7ccd023325c0d42acf899fc0f0732e12976f867', commit_message='Upload tokenizer', commit_description='', oid='c7ccd023325c0d42acf899fc0f0732e12976f867', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "execution_count": 19,
          "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": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-8qedJVVkNU",
        "outputId": "a01aa8fb-5df9-40ed-e712-3b3395b63714"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<s>[INST] Question: What is rabies? Answer: ? [/INST] Rabies is a viral disease that affects the nervous system of animals, including dogs, cats, and bats. It is transmitted through the bite of an infected animal. Rabies is a serious and often fatal disease in both animals and humans. Vaccination is essential to prevent rabies. If you suspect your pet has rabies, contact your veterinarian immediately. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vaccination is the only effective treatment for rabies. Vacc\n"
          ]
        }
      ],
      "source": [
        "# Run text generation pipeline with our next model\n",
        "prompt = \"Question: What is rabies? Answer: ?\"\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": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
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    },
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      "display_name": "Python 3.11.0 64-bit",
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