6.Finetune Falcon-7b with BNB Self Supervised Training.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Finetune Falcon-7b on a Google colab\n",
        "\n",
        "Welcome to this Google Colab notebook that shows how to fine-tune the recent Falcon-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": {
        "id": "C2EgqEPDQ8v6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 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."
      ],
      "metadata": {
        "id": "i-tTvEF1RT3y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        },
        "id": "dhjGKqk7n_RL",
        "outputId": "54303cf8-e0d9-4a5b-8874-606c6b014eaf"
      },
      "execution_count": 5,
      "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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    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mNnkgBq7Q3EU",
        "outputId": "d3897546-c2e2-4afa-e794-f4dddb47fcfc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.0/110.0 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m71.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m21.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 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[31m92.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.6/215.6 kB\u001b[0m \u001b[31m22.7 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[31m8.0 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",
        "For our experiment, we will use the Guanaco dataset, which is a clean subset of the OpenAssistant dataset adapted to train general purpose chatbots.\n",
        "\n",
        "The dataset can be found [here](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)"
      ],
      "metadata": {
        "id": "Rnqmq7amRrU8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset_name = \"timdettmers/openassistant-guanaco\"\n",
        "dataset = load_dataset(dataset_name, split=\"train\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0X3kHnskSWU4",
        "outputId": "ed6ff7fa-d3d0-42f0-d86f-63c023a8d406"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Repo card metadata block was not found. Setting CardData to empty.\n",
            "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the model"
      ],
      "metadata": {
        "id": "rjOMoSbGSxx9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section we will load the [Falcon 7B model](https://huggingface.co/tiiuae/falcon-7b), quantize it in 4bit and attach LoRA adapters on it. Let's get started!"
      ],
      "metadata": {
        "id": "AjB0WAqFSzlD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_name = \"ybelkada/falcon-7b-sharded-bf16\"\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"
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        "outputId": "35eabcbb-4aef-48e6-fab2-0c94396edc27"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
            "- configuration_RW.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"
          ]
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
            "- modelling_RW.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"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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        {
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          "metadata": {}
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        {
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          "metadata": {}
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        {
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          "data": {
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            ],
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              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": 113,
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        },
        "id": "XDS2yYmlUAD6",
        "outputId": "fde3d693-e206-4031-da3c-06860b0dd702"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/180 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7bab272ea53c444397ee154f719874bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b91d9ed82564acf8fea47249c3e4933"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/281 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b6abbbd80694a6f82118c6abf4d249f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below we will load the configuration file in order to create the LoRA model. According to QLoRA paper, it is important to consider all linear layers in the transformer block for maximum performance. Therefore we will add `dense`, `dense_h_to_4_h` and `dense_4h_to_h` layers in the target modules in addition to the mixed query key value layer."
      ],
      "metadata": {
        "id": "NuAx3zBeUL1q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig\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",
        "    target_modules=[\n",
        "        \"query_key_value\",\n",
        "        \"dense\",\n",
        "        \"dense_h_to_4h\",\n",
        "        \"dense_4h_to_h\",\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "dQdvjTYTT1vQ"
      },
      "execution_count": 7,
      "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 = 10\n",
        "logging_steps = 10\n",
        "learning_rate = 2e-4\n",
        "max_grad_norm = 0.3\n",
        "max_steps = 200\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": 15,
      "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 = 200\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": 105,
          "referenced_widgets": [
            "4144c5008bd24e469e1fb7706e136c8e",
            "09b0fc1ce98f4a598971fbd6e6a262c0",
            "c4da7e20c8d640a2a7c3e47835b7b997",
            "ac63a802aff245b393292c1ada9657a3",
            "3fb9025546dc4bfaac3737b26492ea6a",
            "6ca13f9ef1114052865b79a528e1d03f",
            "42b59fb186ba4963934bd7f3b751df4a",
            "a236c743135e4af5a7c94cd7da63f3ad",
            "d6ba2fd54a3144a1b67937f1e74bacff",
            "f6b05bb145e54cdc8f0338bb048b514a",
            "b03067e00aa14876b0c4854d4214299d"
          ]
        },
        "id": "TNeOBgZeTl2H",
        "outputId": "eb3d3d64-3195-485d-80e9-9ad54647b33e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:122: 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%|          | 0/9846 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4144c5008bd24e469e1fb7706e136c8e"
            }
          },
          "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": 17,
      "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": 738
        },
        "id": "_kbS7nRxcMt7",
        "outputId": "5a9814f4-0c68-4550-a6ed-9f014cd1ad39"
      },
      "execution_count": 18,
      "outputs": [
        {
          "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 52:33, Epoch 0/1]\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>10</td>\n",
              "      <td>1.606200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.437600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.442000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.509400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.780900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.450900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.457000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.375200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.505600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.768300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.374500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.439100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.431300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.486400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.739100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>1.463300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>1.392500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.426900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.428000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.796500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=200, training_loss=1.5155159997940064, metrics={'train_runtime': 3168.9705, 'train_samples_per_second': 1.01, 'train_steps_per_second': 0.063, 'total_flos': 1.1841848794073088e+16, 'train_loss': 1.5155159997940064, 'epoch': 0.32})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "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.push_to_hub(\"ashishpatel26/falcon-7b-sharded-bf16\", create_pr=1, use_auth_token=True)"
      ],
      "metadata": {
        "id": "tJIW7UBwoqKP"
      },
      "execution_count": 1,
      "outputs": []
    }
  ]
}