Finetuning_Demo.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S71uTZYoPs26",
        "outputId": "3b3a1876-d837-4f04-e3aa-f089ae3b9130"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: pyarrow-hotfix, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.15.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6\n",
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            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install huggingface-hub\n",
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 huggingface-hub datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZfT7l80izgYl"
      },
      "outputs": [],
      "source": [
        "mport os\n",
        "import torch\n",
        "from datasets import load_dataset\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NMMY_cp46LnW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443,
          "referenced_widgets": [
            "7350b0d4b48e47d182d52c0b6301e7a5",
            "e3848699e45b44caa68e4d9ed8cfb75e",
            "5f8fb9e305284bee84e037386660ddba",
            "48cf01c2c8a34ef09e249dfc6416a77f",
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            "5af87359cfe04024b9a2b1f7a51f705a",
            "af217bace57544ce93ebe4a0a65abcbc",
            "250ad7b4e5a345288ea995cc672b6bfa",
            "ce0a6cb248c8434aa7b54d0a8c9b1caf",
            "ebe1b8fd5e8144ab8496e862bc538d35"
          ]
        },
        "outputId": "0d04c2e0-a71e-4537-eb0c-62d3bfc8bd82"
      },
      "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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            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "PCnJUzrtkWYu",
        "outputId": "cadd2ed6-8dc4-4d0a-865d-0d40806560c1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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            }
          },
          "metadata": {}
        },
        {
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              "model_id": "92fe3e1a7fa6431da99a21c91e0f1cf8"
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          },
          "metadata": {}
        },
        {
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              "model_id": "52f87a26c75b43d4b70453399030666c"
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          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "97610e6950a54c64b83fb31043dfecb8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "152856a002644a6b9c1357001fa9e1e4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/878 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15e61ffe9de249b1a16ff7300cfb804f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "64f40bd0a99849dc9ca2865c8195d8ae"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"316usman/tapal_dataset_demo\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681,
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          ]
        },
        "id": "fddlTuH80umE",
        "outputId": "053efdaf-e21a-45de-9342-646f8cafa86e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/583 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5faf997756244e968cc63ac6b6187978"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13c4250797714c9aa13ac80473f9bb5c"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b7ed4961cd64dac86ff0e4fda09d2ff"
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          },
          "metadata": {}
        },
        {
          "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": "21e2208766674d6fb29d0d89eebb6df1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/179 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adeb30297c1f44c384b8b0e35f8786e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "362a1ef6af93410a911e57a1e2e93921"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8cdecc4f393402d886ed49c9e902d76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "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": "be362bd9b73445789ada629321d09b53"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92355e30b0b04459974c3ae73e20d399"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "151a0162aaef4017a690c84ba974afc8"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "compute_dtype = getattr(torch, \"float16\")\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type='nf4',\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=False,\n",
        ")\n",
        "\n",
        "# Check GPU compatibility with bfloat16\n",
        "if compute_dtype == torch.float16:\n",
        "    major, _ = torch.cuda.get_device_capability()\n",
        "    if major >= 8:\n",
        "        print(\"=\" * 80)\n",
        "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
        "        print(\"=\" * 80)\n",
        "\n",
        "model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
        "device_map = {\"\": 0}\n",
        "# Load base model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "MkADMZjpVXqF",
        "outputId": "0406e686-2d0d-4a15-9478-2f95c075570c"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-341b2360f540>\u001b[0m in \u001b[0;36m<cell line: 74>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m trainer = SFTTrainer(\n\u001b[0;32m---> 75\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m     \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0mpeft_config\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpeft_config\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ],
      "source": [
        "# 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 = 8\n",
        "\n",
        "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "\n",
        "# Batch size per GPU for training\n",
        "per_device_train_batch_size = 4\n",
        "\n",
        "# Batch size per GPU for evaluation\n",
        "per_device_eval_batch_size = 4\n",
        "\n",
        "# Number of update steps to accumulate the gradients for\n",
        "gradient_accumulation_steps = 4\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Maximum gradient normal (gradient clipping)\n",
        "max_grad_norm = 0.3\n",
        "\n",
        "# Initial learning rate (AdamW optimizer)\n",
        "learning_rate = 2e-4\n",
        "\n",
        "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
        "weight_decay = 0.001\n",
        "\n",
        "# Optimizer to use\n",
        "optim = \"paged_adamw_32bit\"\n",
        "\n",
        "# Learning rate schedule\n",
        "lr_scheduler_type = \"cosine\"\n",
        "\n",
        "# Number of training steps (overrides num_train_epochs)\n",
        "max_steps = -1\n",
        "\n",
        "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length\n",
        "# Saves memory and speeds up training considerably\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 1952\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 1\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\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",
        "    weight_decay=weight_decay,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    report_to=\"tensorboard\"\n",
        "    )\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset['train'],\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=None,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=False,\n",
        "\n",
        ")\n",
        "\n",
        "trainer.train()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LFXthHJNyyg3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "outputId": "e1e6e357-cf10-4b4e-e653-1e7003cd168f"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-7cf27dcb0a66>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnew_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/316usman/Llama-2-7b-chat-tapal-demo\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"NousResearch/Llama-2-7b-chat-hf\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'trainer' is not defined"
          ]
        }
      ],
      "source": [
        "new_model = \"/content/316usman/Llama-2-7b-chat-tapal-demo\"\n",
        "model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
        "trainer.model.save_pretrained(new_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "81mfBKaBVVeV",
        "outputId": "dacd0b8e-5d81-4529-9989-66c9d6684ece"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-1fab91119ba8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ],
      "source": [
        "del model\n",
        "del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616,
          "referenced_widgets": [
            "0b2087e3fcb0405b95696a0a3c21a317",
            "9cdf66ee773d4727aac94e47053d8b86",
            "4015596ceef047368fc9eec84ec974ef",
            "88e016ab5536489187e2deed51bd1fbf",
            "b0933c59a66a4933bb6a7a971290dc81",
            "78178a64733545bf8ed5163924ad818d",
            "75d06b2689924c58aba4f0ed26f9e650",
            "d035c7bbf83b4329b7f508dbeebd16ed",
            "ae7dbc1974ab4751a8ff7736af936bc8",
            "2be25d56ef724a128a3513d48e84a149",
            "3f772b65931c4f9091fc6582b25a59f5"
          ]
        },
        "id": "mGAt1bTB-TMp",
        "outputId": "f9749f6a-318a-471c-ece2-8456aa953f53"
      },
      "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": "0b2087e3fcb0405b95696a0a3c21a317"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHFValidationError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/utils/config.py\u001b[0m in \u001b[0;36m_get_peft_type\u001b[0;34m(cls, model_id, **hf_hub_download_kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m                 config_file = hf_hub_download(\n\u001b[0m\u001b[1;32m    178\u001b[0m                     \u001b[0mmodel_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0marg_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"repo_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"from_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m                 \u001b[0mvalidate_repo_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36mvalidate_repo_id\u001b[0;34m(repo_id)\u001b[0m\n\u001b[1;32m    157\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrepo_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m         raise HFValidationError(\n\u001b[0m\u001b[1;32m    159\u001b[0m             \u001b[0;34m\"Repo id must be in the form 'repo_name' or 'namespace/repo_name':\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHFValidationError\u001b[0m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/content/316usman/Llama-2-7b-chat-tapal-demo'. Use `repo_type` argument if needed.",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-b4c228ee930d>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mdevice_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m )\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPeftModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge_and_unload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/peft_model.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, model, model_id, adapter_name, is_trainable, config, **kwargs)\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m             config = PEFT_TYPE_TO_CONFIG_MAPPING[\n\u001b[0;32m--> 244\u001b[0;31m                 PeftConfig._get_peft_type(\n\u001b[0m\u001b[1;32m    245\u001b[0m                     \u001b[0mmodel_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m                     \u001b[0msubfolder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"subfolder\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/peft/utils/config.py\u001b[0m in \u001b[0;36m_get_peft_type\u001b[0;34m(cls, model_id, **hf_hub_download_kwargs)\u001b[0m\n\u001b[1;32m    181\u001b[0m                 )\n\u001b[1;32m    182\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Can't find '{CONFIG_NAME}' at '{model_id}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0mloaded_attributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_json_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Can't find 'adapter_config.json' at '/content/316usman/Llama-2-7b-chat-tapal-demo'"
          ]
        }
      ],
      "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='auto',\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\"\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hI6bjPc4-YJ2"
      },
      "outputs": [],
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
            "509f38702ec44daca2cd18a33a287110",
            "66a559f172c84383b5f0dfbdb74ebafa",
            "107e216afe5943c4a5fb5be61e85f438",
            "7b49521091734049951128179f579cb1",
            "530525af18204316a217349e96e7e938",
            "948627bfd4d3498eba83e0213b05efc8",
            "d075fbdc2f284cc9bf88d1ec28ddb4fe",
            "6594e83233914cc4a4d04656defbd2fc",
            "96638bcca7214ac1b865d963a60794d4",
            "e015e458ada64c2298e4d316a20f0340",
            "0d00c3ff068a4a5eaf4b1449de1c364c",
            "c58db68cd58848a1b402e8f2ed7fde66",
            "ec7119241a6e425e938c94adbc1594e9",
            "0e9e0038e43c4e8d9702f0201c5579c3",
            "165e63b1280d412b9b6e7a0caa73475c",
            "e361f7b679334d58b05a09bf3e558780",
            "7d60923fc7594140b690fa6e9e27fac3",
            "121003d75ebd4e229e16e6366acf7274",
            "9236d5b63f5949d6afdb85c341d74ce0",
            "848ad778063e46529c410e0352e11b90",
            "24ab88c2f432407bb1d2d0e92d10d1f0",
            "311b023304a442908aa015d13041555a",
            "0e844dd0ece94eb4bd3d0a162cc66722",
            "0cec8720df7e4c6ca6a13689a98e9314",
            "f5247779400d47dd923201c7f2823b1c",
            "934e728902dc4783b54b2940bb9c0292",
            "22387f39a0dc465bae0be56261d521d0",
            "019190402d2f4ece81146d3a21488076",
            "0d149232854c4ef9aaa93953aba0192b",
            "0c73660af3a94a359ca71e6375a8eeeb",
            "ccf9e00d51de4c639b6c25c0e9bb9b8a",
            "83c88f01a4f5456aa639a68c574003a1"
          ]
        },
        "id": "3VQyatD1-aYv",
        "outputId": "0afdc02d-3c26-43c6-bdf9-d862e40199a4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "509f38702ec44daca2cd18a33a287110"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "79Otv2EY-l67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258,
          "referenced_widgets": [
            "d1109744fb0147d789edafb9b12f3a05",
            "68e7a13e286d4191b9c5b4708d40615f",
            "5af760b5b9f243f78b2f17808ac1425c",
            "9ff94cc324464fcb8ecc3f05daeb5b5e",
            "05ecbd53e8434432829f2de61d1634d1",
            "72f04146cbaf4167bf1946de84e880ef",
            "6fc41c0f7127486ea1f2ac0434ac1798",
            "daea60c10e4c43498ca874388875d3ad",
            "4160cb77eef44d3f932f3e18b0f1343d",
            "921905f2d016406d966fd10e5f25c810",
            "6283de320a9a4614ba70302e5dc87d70",
            "ae79e41e63cb442a85414f380bfd50cd",
            "b9648fbb9e84474483fea40fa81272a7",
            "15938266efa0422586bebc094b20e14b",
            "d5143d87e2c4405a9ab857df93be890b",
            "09492ea1b7fc454aaf222093ef2572d4",
            "55aef078f7f64123b8ca4ecdb9272e6a",
            "6bcd405e7e5548b0ba40ff125ab3a6a9",
            "f5b6477cc1384494b27b8b4c17f4a78e",
            "af72e98742904a2dbf294e14d64128a0",
            "dae8f5ec864f4832b11ed5c8bb8f0373",
            "69ed524ea4bf4a8cbce520c39fe1d38b",
            "ca11081638a24b2998853fc2ed40e480",
            "c3747d987c90466281896aad667c3432",
            "90dda636aeb24031af04fc5dfdb5e7ae",
            "01d5e41911a246f389d0f402eac1b4fe",
            "259f521ea40349ee8e6af4b4772dc599",
            "2c703c15ca324f8bbf1d3528950dabcf",
            "c6c6370a14904690b9fe191e4fd9aa25",
            "8aea682201fd4b889ebcc8c94c81d11e",
            "44b9ec3cfbc74243b727079c7e04cfec",
            "cb3e8678211748b993389b595af082d5",
            "50baa7248c83472d833c420d1c3e3ac4"
          ]
        },
        "outputId": "719bc714-5bf3-4c8c-9240-e4994c6e9860"
      },
      "outputs": [
        {
          "output_type": "display_data",
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            "  inflating: content/316usman/Llama-2-7b-chat-tapal/config.json  \n",
            "  inflating: content/316usman/Llama-2-7b-chat-tapal/generation_config.json  \n",
            "  inflating: content/316usman/Llama-2-7b-chat-tapal/adapter_model.bin  \n",
            "  inflating: content/316usman/Llama-2-7b-chat-tapal/adapter_config.json  \n",
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