LLM-FineTuning-Large-Language-Models

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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "WE5GJ6s7y0Xo"
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      },
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      "source": [
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        "## Fine-tune opt-6.7b with QLoRA\n",
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        "\n",
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        "### Checkout my [Twitter(@rohanpaul_ai)](https://twitter.com/rohanpaul_ai) for daily LLM bits"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/"
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        },
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        "id": "otj46qRbtpnd",
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        "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4"
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      },
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      "outputs": [],
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      "source": [
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        "# !pip install -q bitsandbytes datasets accelerate loralib transformers peft"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/",
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          "height": 446,
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          ]
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        },
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        "id": "cg3fiQOvmI3Q",
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        "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89"
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      },
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      "outputs": [],
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      "source": [
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        "import os\n",
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        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
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        "import torch\n",
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        "import torch.nn as nn\n",
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        "import bitsandbytes as bnb\n",
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        "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {},
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      "outputs": [],
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      "source": [
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        "model_id = \"facebook/opt-6.7b\"\n",
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        "\n",
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        "try:\n",
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        "    model = AutoModelForCausalLM.from_pretrained(model_id,\n",
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        "                                                 return_dict=True,\n",
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        "                                                 load_in_8bit=True,\n",
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        "                                                 device_map='auto')\n",
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        "    tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
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        "\n",
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        "    # Clear any cached memory to optimize GPU usage\n",
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        "    torch.cuda.empty_cache()\n",
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        "\n",
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        "except Exception as e:\n",
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        "    print(f\"Error loading model: {e}\")"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "9fTSZntA1iUG"
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      },
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      "source": [
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        "## Preparing the model\n",
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        "\n",
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        "- Freeze all our layers\n",
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        "\n",
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        "- cast the layer-norm in `float32` for stability. \n",
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        "\n",
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        "- Also cast the output of the last layer in `float32` for the same reasons."
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {},
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      "outputs": [],
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      "source": [
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        "def prepare_model_for_finetuning(model):\n",
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        "    for param in model.parameters():\n",
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        "        param.requires_grad = False\n",
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        "        if param.ndim == 1:\n",
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        "            param.data = param.data.to(torch.float32)\n",
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        "\n",
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        "    model.gradient_checkpointing_enable()\n",
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        "    model.enable_input_require_grads()\n",
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        "\n",
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        "    class CastOutputToFloat(nn.Sequential):\n",
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        "        def forward(self, x): return super().forward(x).to(torch.float32)\n",
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        "\n",
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        "    model.lm_head = CastOutputToFloat(model.lm_head)\n",
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        "\n",
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        "# In the main script, assuming 'model' is the model instance\n",
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        "prepare_model_for_finetuning(model)\n"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "KwOTr7B3NlM3"
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      },
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      "source": [
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        "## LoRA"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {
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        "id": "4W1j6lxaNnxC"
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      },
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      "outputs": [],
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      "source": [
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        "def print_trainable_parameters(model):\n",
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        "    \"\"\"\n",
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        "    Prints the number of trainable parameters in the model.\n",
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        "    \"\"\"\n",
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        "    trainable_params = 0\n",
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        "    all_param = 0\n",
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        "    for _, param in model.named_parameters():\n",
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        "        all_param += param.numel()\n",
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        "        if param.requires_grad:\n",
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        "            trainable_params += param.numel()\n",
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        "    print(\n",
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        "    f\"trainable params: {trainable_params} || \"\n",
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        "    f\"all params: {all_param} || \"\n",
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        "    f\"trainable%: {100 * trainable_params / all_param}\"\n",
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        ")"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {
261
        "colab": {
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          "base_uri": "https://localhost:8080/"
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        },
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        "id": "4iwHGzKBN6wk",
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        "outputId": "22188e13-df92-4646-fac6-00900686fb89"
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      },
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      "outputs": [],
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      "source": [
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        "from peft import LoraConfig, get_peft_model\n",
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        "\n",
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        "config = LoraConfig(\n",
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        "    r=16,\n",
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        "    lora_alpha=32,\n",
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        "    target_modules=[\"q_proj\", \"v_proj\"],\n",
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        "    lora_dropout=0.05,\n",
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        "    bias=\"none\",\n",
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        "    task_type=\"CAUSAL_LM\"\n",
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        ")\n",
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        "\n",
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        "model = get_peft_model(model, config)\n",
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        "print_trainable_parameters(model)"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "metadata": {
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        "id": "QdjWif4CVXR6"
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      },
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      "source": [
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        "### Training"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {
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        "id": "AQ_HCYruWIHU"
298
      },
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      "outputs": [],
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      "source": [
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        "import transformers\n",
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        "from datasets import load_dataset\n",
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        "data = load_dataset(\"Abirate/english_quotes\")\n",
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        "\n",
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        "data = data.map(lambda samples: tokenizer(samples['quote']), batched=True)\n",
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        "\n",
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        "trainer = transformers.Trainer(\n",
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        "    model=model,\n",
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        "    train_dataset=data['train'],\n",
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        "    args=transformers.TrainingArguments(\n",
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        "        per_device_train_batch_size=4,\n",
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        "        gradient_accumulation_steps=4,\n",
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        "        warmup_steps=100,\n",
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        "        max_steps=200,\n",
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        "        learning_rate=2e-4,\n",
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        "        fp16=True,\n",
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        "        logging_steps=1,\n",
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        "        output_dir='outputs'\n",
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        "    ),\n",
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        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
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        ")\n",
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        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n",
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        "trainer.train()"
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      ]
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    },
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    {
327
      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/",
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          "height": 331
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        },
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        "id": "DpYr24pR8T_0",
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        "outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc"
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      },
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      "outputs": [],
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      "source": [
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        "from huggingface_hub import notebook_login\n",
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        "\n",
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        "notebook_login()"
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      ]
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    },
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    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/",
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          "height": 133,
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          "referenced_widgets": [
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          ]
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        },
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        "id": "VxB6UV5XAvvP",
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        "outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c"
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      },
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      "outputs": [],
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      "source": [
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        "model.push_to_hub(\"username/opt-6.7b-lora\", use_auth_token=True)"
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      ]
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    },
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    {
385
      "cell_type": "markdown",
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      "metadata": {
387
        "id": "S65GcxNGA9kz"
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      },
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      "source": [
390
        "## Load adapters from the Hub"
391
      ]
392
    },
393
    {
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      "cell_type": "code",
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      "execution_count": null,
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      "metadata": {
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        "colab": {
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          "base_uri": "https://localhost:8080/",
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          "height": 517,
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          "referenced_widgets": [
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            "523deba849044669bfe8c959750708fc"
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          ]
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        },
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        "collapsed": true,
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        "id": "hsD1VKqeA62Z",
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        "outputId": "83ecbf31-3f84-4b8e-d667-10317f7adcec"
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      },
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      "outputs": [],
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      "source": [
431
        "peft_model_id = \"username/opt-6.7b-lora\"\n",
432
        "\n",
433
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
434
        "\n",
435
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')\n",
436
        "\n",
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        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
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        "\n",
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        "# Load the Lora model\n",
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        "model = PeftModel.from_pretrained(model, peft_model_id)"
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      ]
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    },
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    {
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      "cell_type": "markdown",
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      "source": [
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        "## Inference"
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      ]
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        "def generate_text(input_text, model, tokenizer, max_new_tokens=50):\n",
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        "\n",
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        "    with torch.cuda.amp.autocast():\n",
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        "\n",
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        "    return tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
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        "\n",
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        "# Example usage\n",
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        "input_text = \"The AI is coming for the  \"\n",
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        "output_text = generate_text(input_text, model, tokenizer)\n",
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        "print(output_text)\n"
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