PEFT_Finetuning.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Deep070203/Finetuning-LLM/blob/main/PEFT_Finetuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Installing Requirements and Loading the Model**"
      ],
      "metadata": {
        "id": "Fd9ZvG-a64py"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ajYWc7TxbS52"
      },
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      "source": [
        "!pip install -q bitsandbytes datasets accelerate loralib\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"Qwen/Qwen2.5-1.5B\",\n",
        "    quantization_config=bnb_config,\n",
        "    device_map='auto',\n",
        "    trust_remote_code=True,\n",
        ")\n",
        "\n",
        "model.config.use_cache = False\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen2.5-1.5B\")"
      ],
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        "colab": {
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/682 [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/3.09G [00:00<?, ?B/s]"
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          "output_type": "display_data",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Post-processing the Model**"
      ],
      "metadata": {
        "id": "UDZ4YQwf7L-T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for param in model.parameters():\n",
        "  param.requires_grad = False  # freeze the model - train adapters later\n",
        "  if param.ndim == 1:\n",
        "    # cast the small parameters (e.g. layernorm) to fp32 for stability\n",
        "    param.data = param.data.to(torch.float32)\n",
        "\n",
        "model.gradient_checkpointing_enable()  # reduce number of stored activations\n",
        "model.enable_input_require_grads()\n",
        "\n",
        "class CastOutputToFloat(nn.Sequential):\n",
        "  def forward(self, x): return super().forward(x).to(torch.float32)\n",
        "model.lm_head = CastOutputToFloat(model.lm_head)"
      ],
      "metadata": {
        "id": "GcobcWnQKD86"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ],
      "metadata": {
        "id": "oR6OaNAMLbQC"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Applying LoRA**"
      ],
      "metadata": {
        "id": "wG73W2Ca7Ra1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=16,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q_proj\", \"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlLmujv121Rh",
        "outputId": "70093fa2-ccef-4125-df00-d18a993dd70b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 2179072 || all params: 890795520 || trainable%: 0.24462089795871447\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training on a Dataset**"
      ],
      "metadata": {
        "id": "AznTYW7y7ZZ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from datasets import load_dataset\n",
        "data = load_dataset(\"Abirate/english_quotes\")\n",
        "data = data.map(lambda samples: tokenizer(samples['quote']), batched=True)\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=data['train'],\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=4,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=100,\n",
        "        max_steps=200,\n",
        "        learning_rate=2e-4,\n",
        "        fp16=True,\n",
        "        logging_steps=25,\n",
        "        output_dir='outputs'\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599,
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        "outputId": "7585dcce-0ba7-448a-d175-29ec0d5d4b52"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c600be06c9c0493a94fe47d5a6ed2dd7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "quotes.jsonl:   0%|          | 0.00/647k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9c04f95f19bf4353a765d0a618f921d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e522b372cf84a5c91b2dfcb3cc99e0e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2508 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ea76424ba8f49bfb80ed5d486f6ed58"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n",
            "max_steps is given, it will override any value given in num_train_epochs\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='200' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [200/200 07:05, Epoch 1/2]\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>3.019800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>3.003800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>2.757400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>2.756100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>2.794200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>2.745100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>2.641200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>2.626200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=200, training_loss=2.792966575622559, metrics={'train_runtime': 433.0954, 'train_samples_per_second': 7.389, 'train_steps_per_second': 0.462, 'total_flos': 2214483204096000.0, 'train_loss': 2.792966575622559, 'epoch': 1.2759170653907497})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Saving the Model on my 🤗 account**"
      ],
      "metadata": {
        "id": "TQoHztYR7c-Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "VZm-hSYb4x9m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"Djs07/qwen2.5-1.5b-lora\", use_auth_token=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "7e5d3305845b4de1a3fac90b40ffa60c",
            "4b30e14d1d044d2cbd1f8139b254e565",
            "7d9e6fb9df644ab3bfe3c0d9f7a4e0e1",
            "7294da1ba50b49c3bb651db63ed77f37",
            "4de0d1e4134e4d9d89d2c41caa953af9",
            "5e5b0b51bceb415288909d9fdb989daf",
            "e36f2122329b46b8a04fcd1b1120464d",
            "e06992baa7ba403489ca2b852cb27251",
            "bce84412ad54482189e8578e4753eec3",
            "9d8e7946ba1d4c24abce8afdd7485fe1",
            "62f35a326bf640c2a032ed94732d3b32"
          ]
        },
        "id": "Ip9ennGW5HOl",
        "outputId": "51e35089-6a32-4a74-a0e2-8cf5edad8bae"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/utils/hub.py:894: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/8.73M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e5d3305845b4de1a3fac90b40ffa60c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Djs07/qwen2.5-1.5b-lora/commit/8d7d20b1cbb95e7de29abe404e900c106fa8c8cb', commit_message='Upload model', commit_description='', oid='8d7d20b1cbb95e7de29abe404e900c106fa8c8cb', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading my saved model for Inference**"
      ],
      "metadata": {
        "id": "FEsXLwso7q42"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = \"Djs07/qwen2.5-1.5b-lora\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119,
          "referenced_widgets": [
            "0aeaddf52e3641ea868419969d5f63e4",
            "b1bb3526c5a648038d2b0f45bba96fde",
            "ebd573e35a1748e088612c23c3869840",
            "d9db1e510b1842d8a7b3b87880a23813",
            "9977eedf18324aeb9b468ee71cd80dc8",
            "71ef23663b2143729f7a260360b84da7",
            "afc9102a22b74133be62ca78076d7731",
            "217d81ee492441c2ba48d8e1e9012c56",
            "b900b3752154409bbf10c5eb6aeabc23",
            "9bec0b6322ef4b1183cb1c831b19dc5c",
            "5e0e6102086e4adb815e7939a9aac8ac",
            "2e8085f011ab42ef9fdf590048150c9b",
            "a71e67446ff546d1aea548ec952cc912",
            "f39fe5b0778541e197c507a5b7d83a30",
            "cf7033d274bc42b9917ed74f1eaf278f",
            "a3e78522126f4c40bc68b4fa3d28cbf9",
            "4c0abd538a7649d786d84f24ad154503",
            "ebca6f74fb504f54a86aa08606f0bc0e",
            "67196e95b9664104a75a42dcdf347835",
            "b7eb8103bddf4fc5ab77ebf3739a24d4",
            "b4a36c938ca042a98f3533cc9094563c",
            "1106f77f30c042c4866ec0a6df0c6185"
          ]
        },
        "id": "uRhEhuah5Q14",
        "outputId": "af0d341c-39d6-4285-bca3-c6ed2d212e91"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_config.json:   0%|          | 0.00/643 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0aeaddf52e3641ea868419969d5f63e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/8.73M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e8085f011ab42ef9fdf590048150c9b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch = tokenizer(\"You've gotta dance like there's nobody watching, \", return_tensors='pt')\n",
        "\n",
        "with torch.cuda.amp.autocast():\n",
        "  output_tokens = model.generate(**batch, max_new_tokens=50)\n",
        "\n",
        "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEWl_BQ-5dt0",
        "outputId": "4855b017-e36d-42a8-8b51-593603641b60"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-bab245b3093b>:3: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
            "  with torch.cuda.amp.autocast():\n",
            "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            " You've gotta dance like there's nobody watching,  sing like you're putting your heart on your sleeve,  live like it's heaven on earth,  love like you've never been hurt,  and die like you've never been told you can't.  That's\n"
          ]
        }
      ]
    }
  ]
}