Llama_2_Fine_Tuning_using_QLora.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/shrinidhisuresha/llm/blob/main/Llama_2_Fine_Tuning_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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    {
      "cell_type": "markdown",
      "source": [
        "**Code Credit: Hugging Face**\n",
        "\n",
        "**Dataset Credit: https://twitter.com/Dorialexander/status/1681671177696161794 **"
      ],
      "metadata": {
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      }
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      "source": [
        "## Finetune Llama-2-7b on a Google colab\n",
        "\n",
        "Welcome to this Google Colab notebook that shows how to fine-tune the recent Llama-2-7b model on a single Google colab and turn it into a chatbot\n",
        "\n",
        "We will leverage PEFT library from Hugging Face ecosystem, as well as QLoRA for more memory efficient finetuning"
      ],
      "metadata": {
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        "## Setup\n",
        "\n",
        "Run the cells below to setup and install the required libraries. For our experiment we will need `accelerate`, `peft`, `transformers`, `datasets` and TRL to leverage the recent [`SFTTrainer`](https://huggingface.co/docs/trl/main/en/sft_trainer). We will use `bitsandbytes` to [quantize the base model into 4bit](https://huggingface.co/blog/4bit-transformers-bitsandbytes). We will also install `einops` as it is a requirement to load Falcon models."
      ],
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m76.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m82.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.5/188.5 kB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m214.7/214.7 kB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n",
        "!pip install -q datasets bitsandbytes einops wandb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset\n",
        "\n"
      ],
      "metadata": {
        "id": "Rnqmq7amRrU8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "#dataset_name = \"timdettmers/openassistant-guanaco\" ###Human ,.,,,,,, ###Assistant\n",
        "\n",
        "dataset_name = 'AlexanderDoria/novel17_test' #french novels\n",
        "dataset = load_dataset(dataset_name, split=\"train\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
          "referenced_widgets": [
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            "630598ba078b433886e8d93c3dc5ba12",
            "219d839b70fe4fc5b261dc0912297e6d",
            "de617700d66642ab97e7301de29dfe11",
            "043d6eaeb3134d669db5c38690066373",
            "35e8ab0ef31f4158a8a4200f33478fcb",
            "2a068be971e54943a6ef9c0d279e4905",
            "62e991a7cd7c4fe2834c0fa3123fdf5a",
            "a932bababf4f4d36a235db90f8217630",
            "4acc2939d44a4b70a76084a5ade46084",
            "9a3a07f91ef642d482506b5db2751347",
            "a452aba0c32d4900ba2d3586737b418a",
            "8e484ebae0094ef7b77c16a5d7ddb9b9",
            "517b3758620e40a797e53e19a884a140",
            "5e27bf31874143208bbfc8952a7e4972",
            "14757e984b774164b32d96019895d013",
            "d5b0f9e529974838a966a5a10d9c5302",
            "52fe43846d2443c1b467abb289201d31",
            "021a39c0995b4a84aa1eeacc66eeb8b3",
            "884550f967ba467eaa2d8bf0389bc6f2",
            "76aedc630ee54d7c847b821ccc405333",
            "6a16ccb38bb747eeb8c6af20775d4818",
            "21a0a2b5e1b745a28b0a9371ff3f1e5e",
            "9d20ff582fb145d2b3ab8687fb6068d8",
            "d0220d46f4984d82afd4f58da1af84ba",
            "b773c6fee34c4195bac32a9af3dee63e",
            "f1e40b37767d42c6945101f49aac32d9",
            "479efc3b728c4386997ccd2f61b92cec",
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            "54e6c37184824ceda12ef9fe7bc85c86",
            "06ede41df2414227a8c0bf3f9b8f6d3a",
            "dadc105ec3424ec58d9ff465da7c1e82",
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            "3240a3ef2d294194bed9d3ea237bb6c8",
            "495cdd2967cf485ebecf1d41d8db14f2"
          ]
        },
        "id": "0X3kHnskSWU4",
        "outputId": "e3480871-5aaa-4ccb-c0db-3a3c698d8387"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04ee8989ae864ed88578d0af5d46b500"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/AlexanderDoria--novel17_test to /root/.cache/huggingface/datasets/AlexanderDoria___json/AlexanderDoria--novel17_test-5d434af8006df014/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68fb2a20532a49c3a6d62478f44c8805"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/2.21M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f3b41be56034a2fa608b548a88c8ca9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/119k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65a1405f6b6e4ac2813e5d9f4dd862be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "043d6eaeb3134d669db5c38690066373"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "14757e984b774164b32d96019895d013"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1e40b37767d42c6945101f49aac32d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/AlexanderDoria___json/AlexanderDoria--novel17_test-5d434af8006df014/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the model"
      ],
      "metadata": {
        "id": "rjOMoSbGSxx9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_name = \"TinyPixel/Llama-2-7B-bf16-sharded\"\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",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "id": "ZwXZbQ2dSwzI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625,
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        "outputId": "55eb455f-d1d2-42f0-9cbe-c833b9a7b54e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
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        {
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            ],
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        {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's also load the tokenizer below"
      ],
      "metadata": {
        "id": "xNqIYtQcUBSm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        },
        "id": "XDS2yYmlUAD6",
        "outputId": "f7363b81-c05e-4b7e-b06d-274992dbda65"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/676 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c732e7bd8168436e95f7c1b94084aee8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39a4b1190efb4f2f8e780cd3abf1a9d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eb7476ddbac94025a8a6f31900e1db3f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0306cce88b65470db088c758530d15d5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "\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",
        ")"
      ],
      "metadata": {
        "id": "dQdvjTYTT1vQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the trainer"
      ],
      "metadata": {
        "id": "dzsYHLwIZoLm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we will use the [`SFTTrainer` from TRL library](https://huggingface.co/docs/trl/main/en/sft_trainer) that gives a wrapper around transformers `Trainer` to easily fine-tune models on instruction based datasets using PEFT adapters. Let's first load the training arguments below."
      ],
      "metadata": {
        "id": "aTBJVE4PaJwK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "output_dir = \"./results\"\n",
        "per_device_train_batch_size = 4\n",
        "gradient_accumulation_steps = 4\n",
        "optim = \"paged_adamw_32bit\"\n",
        "save_steps = 100\n",
        "logging_steps = 10\n",
        "learning_rate = 2e-4\n",
        "max_grad_norm = 0.3\n",
        "max_steps = 100\n",
        "warmup_ratio = 0.03\n",
        "lr_scheduler_type = \"constant\"\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\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",
        "    fp16=True,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        ")"
      ],
      "metadata": {
        "id": "OCFTvGW6aspE"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then finally pass everthing to the trainer"
      ],
      "metadata": {
        "id": "I3t6b2TkcJwy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "max_seq_length = 512\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\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",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72,
          "referenced_widgets": [
            "adbda8e270cc40b79a446206fb348776",
            "65c4a785a5b549feaac2861573a08608",
            "e33fb2504b2c414aae294f8cedb8a656",
            "291420b25a694fc2b1432fc3ab05819b",
            "38063ed8de964dea858687e2f8b5fb24",
            "9d8c0e441e7e47afab3306625f350986",
            "7ed35db9eefd4a86af47f21f9fe1bdac",
            "1c73565767d345e1a17a65804f2f47e9",
            "77dd6e3faf314b2f9f5e5739c80356c1",
            "b4c002177e474fde91795c90a27fe34f",
            "b8da98e700834e4e824460a24765e621"
          ]
        },
        "id": "TNeOBgZeTl2H",
        "outputId": "4303f408-bf57-4832-bcda-da03793193fa"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adbda8e270cc40b79a446206fb348776"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will also pre-process the model by upcasting the layer norms in float 32 for more stable training"
      ],
      "metadata": {
        "id": "GWplqqDjb3sS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        "    if \"norm\" in name:\n",
        "        module = module.to(torch.float32)"
      ],
      "metadata": {
        "id": "7OyIvEx7b1GT"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model"
      ],
      "metadata": {
        "id": "1JApkSrCcL3O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's train the model! Simply call `trainer.train()`"
      ],
      "metadata": {
        "id": "JjvisllacNZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "_kbS7nRxcMt7",
        "outputId": "57df5308-0b28-4727-8607-f8d9430691ff"
      },
      "execution_count": 9,
      "outputs": [
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
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            " ··········\n"
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "Tracking run with wandb version 0.15.5"
            ]
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              "Syncing run <strong><a href='https://wandb.ai/shrinidhi10/huggingface/runs/6dgs8mye' target=\"_blank\">crisp-elevator-1</a></strong> to <a href='https://wandb.ai/shrinidhi10/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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              " View project at <a href='https://wandb.ai/shrinidhi10/huggingface' target=\"_blank\">https://wandb.ai/shrinidhi10/huggingface</a>"
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              " View run at <a href='https://wandb.ai/shrinidhi10/huggingface/runs/6dgs8mye' target=\"_blank\">https://wandb.ai/shrinidhi10/huggingface/runs/6dgs8mye</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 02:37, Epoch 100/100]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.871000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.457000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.083300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.014700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.011000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.010300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.009600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.007800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.000900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.000200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=0.14657505290117115, metrics={'train_runtime': 649.9001, 'train_samples_per_second': 2.462, 'train_steps_per_second': 0.154, 'total_flos': 618646376448000.0, 'train_loss': 0.14657505290117115, 'epoch': 100.0})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "During training, the model should converge nicely as follows:\n",
        "\n",
        "![image](https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/loss-falcon-7b.png)\n",
        "\n",
        "The `SFTTrainer` also takes care of properly saving only the adapters during training instead of saving the entire model."
      ],
      "metadata": {
        "id": "H5c0ppfasK29"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model  # Take care of distributed/parallel training\n",
        "model_to_save.save_pretrained(\"outputs\")"
      ],
      "metadata": {
        "id": "v2CEgCF14M0m"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_config = LoraConfig.from_pretrained('outputs')\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "id": "qmA4G6C64dJ4"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['text']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWEM89A48NrU",
        "outputId": "119f33eb-95b4-4607-9284-dd4e1bca8d1d"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"### Human: Écrire un texte dans un style baroque, utilisant le langage et la syntaxe du 17ème siècle, mettant en scène un échange entre un prêtre et un jeune homme confus au sujet de ses péchés.### Assistant: Si j'en luis éton. né ou empêché ce n'eſt pas ſans cauſe vů que ſouvent les liommes ne ſçaventque dire non plus que celui de tantôt qui ne ſçavoit rien faire que des civiéresVALDEN: Jefusbien einpêché confeſſant un jour un jeune Breton Vallonqui enfin de confeſſion me dit qu'il avoit beſongné une civiere . Quoilui dis je mon amice peché n'eſt point écrit au livre Angeli que d'enfernommé la ſommedes pechez ,qui eſt le livre le plus déteſtable qui fut jamais fait& le plus blafphematoire d'autant qu'il eſt dédié à la plus femme de bien je ne ſçai quelle penitence te donner ; mais non mon amiquel goûty prenois-tu ? Mon fieur bon & delectable. Quoi!\"]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Écrire un texte dans un style baroque sur la glace et le feu ### Assistant: Si j'en luis éton\"\n",
        "device = \"cuda:0\"\n",
        "\n",
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(**inputs, max_new_tokens=50)\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgt86z-x4diG",
        "outputId": "91ac7c60-972e-4a84-acb4-a463a8a87bbe"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Écrire un texte dans un style baroque sur la glace et le feu ### Assistant: Si j'en luis étonné, si j'en brûle, si j'en frissonne, si j'en tremble, si j'en frémis, si j'en frissonne, si j'en frissonne, si\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "login()"
      ],
      "metadata": {
        "id": "niyf5_Kc4ugO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "7cbfacc0b23d478aae8c1cff4a7403f8",
            "564473be72b4477aaa0fb018721231f4",
            "29707bf9d96b4d4a88967a723d10e37d",
            "0c02b4b2c424436f96f65f1bed81f90f",
            "4e70413ff8df4fd98da18f285f0267d6",
            "af7589c5fcc849ad843b88de6a850caf",
            "98b52a260a044245b598c66d7ccce2b3",
            "60066219c36f47828aea3a46e5d8d5f6",
            "cc7e59eee9b6489b9482cc2939689fcd",
            "5ae9bd6fefb94d41852b3cd1ba07ecef",
            "075893364c1249ef97155a1ce09acbf8",
            "81ddf8c66f1c49f48233ebe52e238f03",
            "a3013f735e86498a8b6d88e561b2d231",
            "613430056ffc4b03bbe2787920fac075",
            "0669a9c9645c4816b4e1b744feb715db",
            "2335452863274f59b7577d76fdbc92da",
            "739a6a3e96b74c45856bb4ce4ed391d1",
            "2eda04e3520c416b815a0ed378f79ba5",
            "5ec28224813a404d821d5a293e0fced9",
            "1a35e80eb93e4a89acc1a53facf7f617",
            "41b507b4b5a8493d92cbf7bab10253b3",
            "f720f2ef05d7434888dff6e6abd09332",
            "456aa0b4c7dc4f0e8671df328cf14325",
            "938809b2c2c246c7864e2a13842e131d",
            "d520c202ba184e158c1ca4cb5359825d",
            "2ba576a41e9e4594887b6967a65609d3",
            "1c3f6613d5234e48be0e9e6c9ca28ddf",
            "57a8f5d1e8b24d41874a626612f0e7d4",
            "6298616ff14a42058f982d34f97d69b3",
            "a8070e17985d40d18a2d0f489790c93d",
            "cfbc77a45ac54b00a23170667130e21b",
            "481e30aefc264044a332f0e266e20de0"
          ]
        },
        "outputId": "fb6e3006-dce5-4988-d15e-c721418b952a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
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              "model_id": "7cbfacc0b23d478aae8c1cff4a7403f8"
            }
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"llama2-qlora-finetunined-french\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
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        "id": "fjOInz-Q-54k",
        "outputId": "3e4e1301-0c68-45e1-e0a0-fde1bc28c9e6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/134M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "b51994374ee64077a0fa147b3d88a51f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Shrinidhisuresha/llama2-qlora-finetunined-french/commit/08292b5970c03d161cb1555a2473c2d110b3a215', commit_message='Upload model', commit_description='', oid='08292b5970c03d161cb1555a2473c2d110b3a215', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cUmT5MRD_D9k"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}