instruction_tuning.ipynb

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        "<a href=\"https://colab.research.google.com/github/ayyucedemirbas/llama2_instruction_tuning/blob/main/instruction_tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8w7ljAipaPhh"
      },
      "outputs": [],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VedAXUwLakTT"
      },
      "outputs": [],
      "source": [
        "!pip install datasets bitsandbytes trl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from datasets import Dataset, DatasetDict\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        ")\n",
        "import warnings"
      ],
      "metadata": {
        "id": "zNzeTXLowpO7"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "3GNgbo_OwuJc"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "91IKKSzObynq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "016482a4-97e8-44b0-bc9a-717591183475"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame contains NaN values. Dropping rows with NaN values...\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv(\"amazon_reviews.csv\")\n",
        "\n",
        "df[\"instruction\"] = (\n",
        "    \"Analyze the following product review and determine its sentiment: \"\n",
        "    \"Review: \" + df[\"reviewText\"] +\n",
        "    \" Please reply with one of the following options only: Positive, Negative, or Neutral. \"\n",
        "    \"The correct sentiment is: \" + df[\"overall\"].apply(lambda x: \"Positive\" if x >= 4 else (\"Negative\" if x <= 2 else \"Neutral\"))\n",
        ")\n",
        "\n",
        "custom_ds = pd.DataFrame()\n",
        "custom_ds[\"text\"] = df[\"instruction\"]\n",
        "\n",
        "\n",
        "if custom_ds.isnull().values.any():\n",
        "    print(\"DataFrame contains NaN values. Dropping rows with NaN values...\")\n",
        "    df_cleaned = custom_ds.dropna()\n",
        "else:\n",
        "    print(\"DataFrame does not contain any NaN values.\")\n",
        "    df_cleaned = custom_ds\n",
        "\n",
        "dataset = Dataset.from_pandas(df_cleaned)"
      ]
    },
    {
      "cell_type": "code",
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        "df_cleaned"
      ],
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "df_cleaned",
              "summary": "{\n  \"name\": \"df_cleaned\",\n  \"rows\": 997,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 997,\n        \"samples\": [\n          \"Analyze the following product review and determine its sentiment: Review: I bought this used but I was pleasantly surprised that it was like new. I rooted it and made itA Droid pad. I'm using it right now to write this. I like it better than my ipad. Please reply with one of the following options only: Positive, Negative, or Neutral. The correct sentiment is: Positive\",\n          \"Analyze the following product review and determine its sentiment: Review: The right charge slot works fine, but the left one insists on flashing (indicating bad cell) when ever I use it, even with cells that charge fine in the right slot.  Since I only need to charge one cell at a time, I can live with it, but it's peculiar. Please reply with one of the following options only: Positive, Negative, or Neutral. The correct sentiment is: Neutral\",\n          \"Analyze the following product review and determine its sentiment: Review: used it for vizio 42&#34; tv. easy to install. fit perfectly. what is there else to say? strongly recommend for everyone. Please reply with one of the following options only: Positive, Negative, or Neutral. The correct sentiment is: Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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        }
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    {
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      "execution_count": 7,
      "metadata": {
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        },
        "id": "QdFNNLKbb0Ci",
        "outputId": "66bb397c-6ea6-48d5-956f-02a99916af9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text', '__index_level_0__'],\n",
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              "})"
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        "dataset"
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    {
      "cell_type": "code",
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        "dataset[0]"
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      "metadata": {
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        "id": "oFhNohrDzn_1",
        "outputId": "31a7c176-2f4c-47ca-8750-fceb446473da"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': 'Analyze the following product review and determine its sentiment: Review: We got this GPS for my husband who is an (OTR) over the road trucker.  Very Impressed with the shipping time, it arrived a few days earlier than expected...  within a week of use however it started freezing up... could of just been a glitch in that unit.  Worked great when it worked!  Will work great for the normal person as well but does have the \"trucker\" option. (the big truck routes - tells you when a scale is coming up ect...)  Love the bigger screen, the ease of use, the ease of putting addresses into memory.  Nothing really bad to say about the unit with the exception of it freezing which is probably one in a million and that\\'s just my luck.  I contacted the seller and within minutes of my email I received a email back with instructions for an exchange! VERY impressed all the way around! Please reply with one of the following options only: Positive, Negative, or Neutral. The correct sentiment is: Positive',\n",
              " '__index_level_0__': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
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        },
        "id": "B6dBjshNcJ2F",
        "outputId": "bbacdf1e-f0f5-4c99-9533-dfa64c1af047"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/609 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc16255bc182419c801abf7a06807c7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9f0f7901f84d4e97a029f034ab1ff1a3"
            }
          },
          "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": "eedd4bffccc74e019522c7c292255889"
            }
          },
          "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": "386d7cd4a6dd4db2996d106ff536cef7"
            }
          },
          "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": "a9130845d4db4f34a91b1217e492bb71"
            }
          },
          "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": "3c4fd8fb5f5a4d4696af5bfa100c24fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b651b699f1b64ddd98672d8c04d4ba71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/776 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "810dc889851e454683ee7669dd01bcd0"
            }
          },
          "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": "39673fa9c7fe483daf077637d9c61e39"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "f3524f38ef354928aac14531761dd191"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9602802e25d41fe89dc658a466e3620"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "model_name = \"meta-llama/Llama-2-7b-hf\"\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, quantization_config=bnb_config, trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Ey4zTOf1dmyx"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "lora_alpha = 16\n",
        "lora_dropout = 0.1\n",
        "lora_r = 64\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "81nxFXJAdqWV"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "output_dir = \"./results\"\n",
        "per_device_train_batch_size = 1\n",
        "gradient_accumulation_steps = 4\n",
        "optim = \"paged_adamw_32bit\"\n",
        "save_steps = 200\n",
        "logging_steps = 10\n",
        "learning_rate = 2e-4\n",
        "max_grad_norm = 0.3\n",
        "max_steps = 300\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",
        "    report_to=\"none\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "a0114dfc569948cda62e83d23b229095",
            "215366ea3ffa471f9c6a6c39b4aa4d1b"
          ]
        },
        "id": "9Jg6Z1fBd6A8",
        "outputId": "78e7a269-fab8-4fbf-c779-794763b164e2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Converting train dataset to ChatML:   0%|          | 0/997 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba6e69a0bdd04365b2f886b86a44f0b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Applying chat template to train dataset:   0%|          | 0/997 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "47f2f2e58da443c2b71d6ffd8f113990"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing train dataset:   0%|          | 0/997 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e401257faa6d4b7185c5974d2c71b069"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tokenizing train dataset:   0%|          | 0/997 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee3e1e96fe8b4378a0638388421262ef"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "mXjwQxvgeRmE"
      },
      "outputs": [],
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        "    if \"norm\" in name:\n",
        "        module = module.to(torch.float32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "7gT056v1gK6C",
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "129dfa1d-0318-46ec-d4da-f12ed5760a5d"
      },
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
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              "\n",
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              "      \n",
              "      <progress value='300' max='300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [300/300 21:00, Epoch 1/2]\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>2.377900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.233100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.820800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.510400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.216700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>2.107900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.870700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.676700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.412800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.111300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>2.103000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.948600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.667200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.358400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.163500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>2.023000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>2.059000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.719900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.383100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.148700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>2.221400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>2.032300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>1.638500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.315900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.102000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>2.080600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>1.949200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>1.605000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290</td>\n",
              "      <td>1.316300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.040700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=300, training_loss=1.673821185429891, metrics={'train_runtime': 1271.1659, 'train_samples_per_second': 0.944, 'train_steps_per_second': 0.236, 'total_flos': 1.0996091960451072e+16, 'train_loss': 1.673821185429891})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model\n",
        "model_to_save.save_pretrained(\"outputs\")"
      ],
      "metadata": {
        "id": "kh7YthvI2O-3"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_config = LoraConfig.from_pretrained('outputs')\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "id": "cCRmgUiD2Qwk"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = dataset[\"text\"][0]\n",
        "device = \"cuda:0\""
      ],
      "metadata": {
        "id": "k8RF8VVd2SSK"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(\n",
        "    input_ids=inputs[\"input_ids\"],\n",
        "    attention_mask=inputs[\"attention_mask\"],\n",
        "    max_length=250,\n",
        "    return_dict_in_generate=True,\n",
        "    output_scores=True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJqHaJ_d2UDJ",
        "outputId": "1605ec00-f784-4d75-b8fb-c403147b1800"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "From v4.47 onwards, when a model cache is to be returned, `generate` will return a `Cache` instance instead by default (as opposed to the legacy tuple of tuples format). If you want to keep returning the legacy format, please set `return_legacy_cache=True`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generated_text = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)\n",
        "print(\"Generated sentiment prediction:\", generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgyiHBKm2WRF",
        "outputId": "85fe0618-0e24-424a-ecc2-c78fd6ac30bc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated sentiment prediction: Analyze the following product review and determine its sentiment: Review: We got this GPS for my husband who is an (OTR) over the road trucker.  Very Impressed with the shipping time, it arrived a few days earlier than expected...  within a week of use however it started freezing up... could of just been a glitch in that unit.  Worked great when it worked!  Will work great for the normal person as well but does have the \"trucker\" option. (the big truck routes - tells you when a scale is coming up ect...)  Love the bigger screen, the ease of use, the ease of putting addresses into memory.  Nothing really bad to say about the unit with the exception of it freezing which is probably one in a million and that's just my luck.  I contacted the seller and within minutes of my email I received a email back with instructions for an exchange! VERY impressed all the way around! Please reply with one of the following options only: Positive, Negative, or Neutral. The correct sentiment is: Positive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.model.push_to_hub(\n",
        "    \"ayyuce/llama2-amazon-product-review_instruction_tuning\",\n",
        "    commit_message=\"Fine-tuned on Amazon product review dataset\"\n",
        ")\n",
        "\n",
        "tokenizer.push_to_hub(\n",
        "    \"ayyuce/llama2-amazon-product-review_instruction_tuning\",\n",
        "    commit_message=\"Tokenizer for the model\"\n",
        ")\n"
      ],
      "metadata": {
        "id": "z8vIEWlTVl15"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.push_to_hub(\n",
        "    \"ayyuce/amazon-product-review-sentiment-dataset\",\n",
        "    commit_message=\"Initial commit of the processed dataset\"\n",
        ")"
      ],
      "metadata": {
        "id": "IPzff3pKcNxg"
      },
      "execution_count": null,
      "outputs": []
    }
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