Fine_Tune_OPL_in_Llama2.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chrishayuk/opl-train/blob/main/Fine_Tune_OPL_in_Llama2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine Tuning a programming language in Llama2\n",
        "In this notebook, we will teach llama2 an old programming language that it has no knowledge of.  OPL is a programming language from the 1980's created Psion in the UK.  It powered their Psion Organisers.\n",
        "\n",
        "This Notebook runs in Google Colab and is designed to run on a T4"
      ],
      "metadata": {
        "id": "Nr_QOM2Yd4_l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup the Model\n",
        "The following section performs all the setup of the model.\n",
        "This includes\n",
        "\n",
        "- Installing any dependencies\n",
        "- Setting any configuration\n",
        "- Downloading the Base Model"
      ],
      "metadata": {
        "id": "_EieT2YujHbW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install dependencies\n",
        "In order to get started we need to install the appropriate dependencies"
      ],
      "metadata": {
        "id": "H9iaMHIRfNlH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GLXwJqbjtPho",
        "outputId": "4eda67a0-3fda-4427-e31d-f930817ed831",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/244.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.7/244.2 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.9/244.2 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m235.5/244.2 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# install dependencies\n",
        "\n",
        "# we use the latest version of transformers, peft, and accelerate\n",
        "!pip install -q accelerate peft transformers\n",
        "\n",
        "# install bitsandbytes for quantization\n",
        "!pip install -q bitsandbytes\n",
        "\n",
        "# install trl for the SFT library\n",
        "!pip install -q trl\n",
        "\n",
        "# we need sentencepiece for the llama2 slow tokenizer\n",
        "!pip install sentencepiece\n",
        "\n",
        "# we need einops, used by falcon-7b, llama-2 etc\n",
        "# einops (einsteinops) is used to simplify tensorops by making them readable\n",
        "!pip install -q -U einops\n",
        "\n",
        "# we need to install datasets for our training dataset\n",
        "!pip install -q datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Settings\n",
        "The following configures our settings for finetuning our model"
      ],
      "metadata": {
        "id": "Dv-EmERXgmYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# The model that you want to train from the Hugging Face hub\n",
        "model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
        "\n",
        "# The instruction dataset to use\n",
        "dataset_name = \"chrishayuk/test\"\n",
        "\n",
        "# Fine-tuned model name\n",
        "new_model = \"llama-2-7b-chuk-test\"\n",
        "\n",
        "# Output directory where the model predictions and checkpoints will be stored\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 20"
      ],
      "metadata": {
        "id": "loKUs7YRgzKJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download the base model\n",
        "The following will download the base model, in this case the llama-2-7b-chat-hf model."
      ],
      "metadata": {
        "id": "e2zWMgQ2fUYR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import (\n",
        "    AutoTokenizer,\n",
        "    AutoModelForCausalLM,\n",
        "    BitsAndBytesConfig,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "\n",
        "# load the quantized settings, we're doing 4 bit quantization\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    bnb_4bit_use_double_quant=False,\n",
        ")\n",
        "\n",
        "# Load base model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    # use the gpu\n",
        "    device_map={\"\": 0}\n",
        ")\n",
        "\n",
        "# don't use the cache\n",
        "model.config.use_cache = False\n",
        "\n",
        "# Load the tokenizer from the model (llama2)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run the Model\n",
        "The following tests the capabilities of the language model prior to fine tuning."
      ],
      "metadata": {
        "id": "E5pqSD7xiwlK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "#prompt = \"What does the REM keyword stand for in OPL?\"\n",
        "#prompt = \"What was the first implementation of the OPL programming language?\"\n",
        "#prompt = \"Who were involved in the creation of OPL for Psion?\"\n",
        "#prompt = \"Did Colly Myers create OPL?\"\n",
        "prompt = \"Write a hello world program in the OPL programming language. \"\n",
        "#prompt = \"Write a limerick as comments in the OPL programming language. \"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "id": "frlSLPin4IJ4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06a2fb4e-c551-4106-cf24-d42e65a58168"
      },
      "execution_count": null,
      "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] Write a hello world program in the OPL programming language.  [/INST]  The OPL (Optimizing Programming Language) is a fictional programming language created for the purpose of this exercise. Here is a \"Hello World\" program in OPL:\n",
            "```\n",
            "BEGIN\n",
            "  PRINT \"Hello, World!\";\n",
            "END\n",
            "```\n",
            "Explanation:\n",
            "\n",
            "* `BEGIN` is the keyword that marks the beginning of a program in OPL.\n",
            "* `PRINT` is a keyword that indicates that the following expression should be printed to the screen.\n",
            "* `\"Hello, World!\"` is a string literal that contains the message to be printed.\n",
            "* `END` is the keyword that marks the end of the program.\n",
            "\n",
            "Note: This is a very basic example of an OPL program, and in a real-world scenario, you would likely need to include more code to accomplish a meaningful task\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train the Model\n",
        "The following section is about taking your dataset and then finetuning the model"
      ],
      "metadata": {
        "id": "zLlrVRiWhfKR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Dataset\n",
        "The following code will load your dataset, ready to be fine tuned by the model"
      ],
      "metadata": {
        "id": "OOMlPjabk0lO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load the dataset\n",
        "dataset = load_dataset(dataset_name, split=\"train\")"
      ],
      "metadata": {
        "id": "_toUICSmk8Sc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine Tune the Model\n",
        "The following section will take your dataset, and fine tune the model with it."
      ],
      "metadata": {
        "id": "dSDs1baoklS2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer\n",
        "\n",
        "# Load LoRA configuration\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.1,\n",
        "    r=64,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,      # uses the number of epochs earlier\n",
        "    per_device_train_batch_size=4,          # 4 seems reasonable\n",
        "    gradient_accumulation_steps=2,          # 2 is fine, as we're a small batch\n",
        "    optim=\"paged_adamw_32bit\",              # default optimizer\n",
        "    save_steps=0,                           # we're not gonna save\n",
        "    logging_steps=10,                       # same value as used by Meta\n",
        "    learning_rate=2e-4,                     # standard learning rate\n",
        "    weight_decay=0.001,                     # standard weight decay 0.001\n",
        "    fp16=False,                             # set to true for A100\n",
        "    bf16=False,                             # set to true for A100\n",
        "    max_grad_norm=0.3,                      # standard setting\n",
        "    max_steps=-1,                           # needs to be -1, otherwise overrides epochs\n",
        "    warmup_ratio=0.03,                      # standard warmup ratio\n",
        "    group_by_length=True,                   # speeds up the training\n",
        "    lr_scheduler_type=\"cosine\",           # constant seems better than cosine\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "# Set supervised fine-tuning parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,                # use our lora peft config\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=None,                    # no max sequence length\n",
        "    tokenizer=tokenizer,                    # use the llama tokenizer\n",
        "    args=training_arguments,                # use the training arguments\n",
        "    packing=False,                          # don't need packing\n",
        ")\n",
        "\n",
        "# Train model\n",
        "trainer.train()\n",
        "\n",
        "# Save trained model\n",
        "trainer.model.save_pretrained(new_model)"
      ],
      "metadata": {
        "id": "OJXpOgBFuSrc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337,
          "referenced_widgets": [
            "02d93e10f0124b2b8c39a02f86892b60",
            "767bb7a75b674c0ba9804db38d6f658f",
            "b3534c9622594c1db57ec1dab2f63d07",
            "eb17874ddcdd44e3aee344940b7eb377",
            "ad8fc23a5d494cbc83ee0eb19a3336b4",
            "554874d610d7454dbd964052722b6642",
            "41fc93743dff44e48f1f47a877f4e302",
            "6ed1176d87764abfaa7845459bfe64a3",
            "2895952a510741969db87013345a9391",
            "8c7fc0673d074d559cbd3c364e60fd21",
            "9c9d18319c1045578b46275627bddcf0"
          ]
        },
        "outputId": "bc4a2248-2f24-4e26-bb36-5b35c0a4c04e"
      },
      "execution_count": null,
      "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",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:166: 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%|          | 0/43 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02d93e10f0124b2b8c39a02f86892b60"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'loss': 3.2477, 'learning_rate': 0.00019744105246469263, 'epoch': 1.82}\n",
            "{'loss': 2.3318, 'learning_rate': 0.00018522168236559695, 'epoch': 3.64}\n",
            "{'loss': 1.6212, 'learning_rate': 0.000164140821963114, 'epoch': 5.45}\n",
            "{'loss': 1.2458, 'learning_rate': 0.00013639049369634876, 'epoch': 7.27}\n",
            "{'loss': 0.9476, 'learning_rate': 0.00010485622221144484, 'epoch': 9.09}\n",
            "{'loss': 0.7055, 'learning_rate': 7.281699277636572e-05, 'epoch': 10.91}\n",
            "{'loss': 0.5747, 'learning_rate': 4.360429701490934e-05, 'epoch': 12.73}\n",
            "{'loss': 0.4657, 'learning_rate': 2.025571894372794e-05, 'epoch': 14.55}\n",
            "{'loss': 0.4432, 'learning_rate': 5.199082004372957e-06, 'epoch': 16.36}\n",
            "{'loss': 0.4355, 'learning_rate': 0.0, 'epoch': 18.18}\n",
            "{'train_runtime': 640.0426, 'train_samples_per_second': 1.344, 'train_steps_per_second': 0.156, 'train_loss': 1.2018705701828003, 'epoch': 18.18}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run the Model\n",
        "The following runs the model post fine tune"
      ],
      "metadata": {
        "id": "7kC775ePd2jF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "#prompt = \"What does the REM keyword stand for in OPL?\"\n",
        "#prompt = \"What was the first implementation of the OPL programming language?\"\n",
        "#prompt = \"Who were involved in the creation of OPL for Psion?\"\n",
        "#prompt = \"Did Colly Myers create OPL?\"\n",
        "#prompt = \"Write a hello world program in the OPL programming language, include an explanation of the code \"\n",
        "#prompt = \"Write a limerick as comments in the OPL programming language. \"\n",
        "prompt = \"Write a Hello Chris program in psion opl\"\n",
        "#prompt = \"Write a program that checks if a given year, is a leap year in OPL\"\n",
        "#prompt = \"Write a hello world program in the OPL programming language. \"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07820d19-577d-4164-fead-1af9cdef81b3",
        "id": "xivdjp-8oUGW"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] Write a Hello Chris program in psion opl [/INST] PROC main:\n",
            "\tLOCAL greeting$(\n",
            "\t\tPRINT \"Hello \"\n",
            "\t\tPRINT \"Chris\"\n",
            "\t\tPRINT \"!\"\n",
            "\tENDP \n",
            "\n",
            "\tPRINT greeting$\n",
            "\tGET\n",
            "ENDP \n",
            " \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# %load_ext tensorboard\n",
        "# %tensorboard --logdir results/runs"
      ],
      "metadata": {
        "id": "crj9svNe4hU5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Empty VRAM\n",
        "del model\n",
        "del pipe\n",
        "del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkQCviG0Zta-",
        "outputId": "5c5c2054-7ebd-40b0-e714-2029ad904d28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19965"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reload model in FP16 and merge it with LoRA weights\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=device_map,\n",
        ")\n",
        "model = PeftModel.from_pretrained(base_model, new_model)\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "# Reload tokenizer to save it\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "id": "QQn30cRtAZ-P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "10dbe607a78b47f18fdaf5452fddd34c",
            "00cd233855ce49369b9c1e5bd4a2b8eb",
            "07ed841b506b4885b8f209a23e39f883",
            "203596710ccc44a39f5fec7a35178810",
            "eea03818a0d24179944d5746d13ee7c8",
            "41ab603e2f9c49e18fe56f13bf372a06",
            "583d53e6a7b8461b8654f94303cb3880",
            "f727cfd03ef7432b928721c628cd24d8",
            "e4624f1a4e254b96ba773c37908a9fe2",
            "9e88c9fd4f1a4f2db06c38a9054582d5",
            "fd9970de85db4322865629000f163ebc"
          ]
        },
        "outputId": "f69c3d15-35cd-4f3e-8fc0-ddd7ffd8c167"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10dbe607a78b47f18fdaf5452fddd34c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n",
        "\n",
        "model.push_to_hub(new_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(new_model, use_temp_dir=False)"
      ],
      "metadata": {
        "id": "x-xPb-_qB0dz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373,
          "referenced_widgets": [
            "c99aff4cfd664ae8a165a27bea0566c8",
            "e4b64cab6b7b418c8a2575ee26839039",
            "c3a4fedc73b3480089ef9d13381471ed",
            "bf722f71c61b4285bcbbf32fd619b3a6",
            "fd11a6148b704c5b9142c5e8de2d3b25",
            "f0bcdaf940d14ad796fc7ac46c8e1e64",
            "b6e821c974674f2290c354238d6c919c",
            "eeba50e8242c4753bfc0ea48e03f9078",
            "7a1f3340688d408092adade75f4baac4",
            "8c887ca9b0eb44fdb8608bf36b5db5c5",
            "e4698337e6b843afac706ab657ca6af9",
            "1af01f1f1aac42b8bff46fe4df8a59ad",
            "eee8731f316244eda5ff0765fd12bf85",
            "f135278e410f4b708435bb80fb630bcf",
            "2e6fc79bf5c149d6b0bc5c52e18debc7",
            "a4b0debc025444a59abd6953b3512c0d",
            "130120644beb48acbc038651459af43c",
            "bf77e97593a349718bdb5fd9bfd28fe3",
            "f7292741953e47699540ef8712fc0d8d",
            "9434350b1b9c4060812feb9ecbf63278",
            "b29647e268414329be56047e522e28b9",
            "27bb18a199ca47108c7a61e9c443de36",
            "33ebb868f3e846f6af1a1a2a8ad6a3cb",
            "1f73f8b4d4da4e74adc135f2a2f6ee65",
            "68da6e6e69c8419895bea2068760534e",
            "6dc1a868e08c4c3b8315116d2c46573b",
            "7a5d714c17374104bb6f5caaa5541c10",
            "1b6c59a51359453c926bfcddb3d0f0ea",
            "dac3669f18284161a58d52f26dffb761",
            "a3511f489f6d47cc8d404ab6f367b29f",
            "20670478612f4b1a8a5f23d71a2609a7",
            "b463153ec04749e38540389efa2981f7",
            "2bb3d36d248a48fba364f14d9e840306"
          ]
        },
        "outputId": "6ed9166c-5f92-4375-eca5-dbb247c0e13a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c99aff4cfd664ae8a165a27bea0566c8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00001-of-00002.bin:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1af01f1f1aac42b8bff46fe4df8a59ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00002-of-00002.bin:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33ebb868f3e846f6af1a1a2a8ad6a3cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/mlabonne/llama-2-7b-miniguanaco/commit/c81a32fd0b4d39e252326e639d63e75aa68c9a4a', commit_message='Upload tokenizer', commit_description='', oid='c81a32fd0b4d39e252326e639d63e75aa68c9a4a', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    }
  ]
}