Fine_Tuning_LLaMA_2_using_Qlora.ipynb

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  "cells": [
    {
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Alaaibrahim2/Alaa/blob/main/Fine_Tuning_LLaMA_2_using_Qlora.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
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      "metadata": {
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        },
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        "outputId": "aee6444c-0007-434c-e82f-fd0e35fab06a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: xxhash, shtab, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, docstring-parser, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, tyro, nvidia-cusolver-cu12, datasets, bitsandbytes, accelerate, trl, peft\n",
            "Successfully installed accelerate-0.28.0 bitsandbytes-0.43.0 datasets-2.18.0 dill-0.3.8 docstring-parser-0.16 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 peft-0.9.0 shtab-1.7.1 trl-0.7.11 tyro-0.7.3 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!pip install accelerate peft bitsandbytes transformers trl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "MgtR7uK0IeG9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model from Hugging Face hub\n",
        "base_model = \"NousResearch/Llama-2-7b-chat-hf\"\n",
        "\n",
        "# New instruction dataset\n",
        "guanaco_dataset = \"mlabonne/guanaco-llama2-1k\"\n",
        "\n",
        "# Fine-tuned model\n",
        "new_model = \"llama-2-7b-chat-guanaco\""
      ],
      "metadata": {
        "id": "EKbERdhRLAZn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(guanaco_dataset, split=\"train\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
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            "0aeb417cabd34e7881dc88cfa0bfc947",
            "7fcec632a9254ee3babcb0f4d07bcd3d"
          ]
        },
        "id": "OEV7h4_kLFtI",
        "outputId": "9d8a1465-6715-4e9a-e3e5-162f0381def4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.02k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a9582240a454fb6b5cd84c994b91f9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94b4823d71434d03a9d7574e9be17f20"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compute_dtype = getattr(torch, \"float16\")\n",
        "\n",
        "quant_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=False,\n",
        ")"
      ],
      "metadata": {
        "id": "XXc-_E6GLW_4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model,\n",
        "    quantization_config=quant_config,\n",
        "    device_map={\"\": 0}\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400,
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        },
        "id": "GV42INM-NPVq",
        "outputId": "d62db5d1-a3f9-4e63-91ac-96dd22151ec9"
      },
      "execution_count": 7,
      "outputs": [
        {
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          "metadata": {}
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          "metadata": {}
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          "metadata": {}
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          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:410: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:415: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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        "id": "Slx9_0oYNP5b",
        "outputId": "ba79af9c-716c-421a-dd2c-f0ee2124222c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
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        {
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          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "peft_params = LoraConfig(\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.1,\n",
        "    r=64,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")"
      ],
      "metadata": {
        "id": "RVxUInuSO061"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_params = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=1,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    save_steps=25,\n",
        "    logging_steps=25,\n",
        "    learning_rate=2e-4,\n",
        "    weight_decay=0.001,\n",
        "    fp16=False,\n",
        "    bf16=False,\n",
        "    max_grad_norm=0.3,\n",
        "    max_steps=-1,\n",
        "    warmup_ratio=0.03,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=\"constant\",\n",
        "    report_to=\"tensorboard\"\n",
        ")"
      ],
      "metadata": {
        "id": "rAH_j6xFQ1Ue"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_params,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=None,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_params,\n",
        "    packing=False,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
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        },
        "id": "FV09xGLvQ2EK",
        "outputId": "4c121644-8c2f-4dab-a5da-eae3f9e022ec"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:225: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4cd209dd4d054abfb8b1d71c9bfe1b4a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "IcKe7lJ3Q_ue"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_xG92oHSRI25"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}