nextword_sentence_prediction.ipynb

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            "\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[32m92.2/244.2 kB\u001b[0m \u001b[31m3.2 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[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.4/77.4 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m116.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m56.5 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[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
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        }
      ],
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"csv\", data_files=\"/content/email_train_final.csv\",split=\"train\")\n",
        ""
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            "75b43375498149d9a5faed39d25cb976",
            "313a2d2638114a2182e107ff23c360c5",
            "21e3a9198b494abc9a273ff31d88aeff",
            "6bae0b65bd154635867c631e950cefea"
          ]
        },
        "id": "_Nq2F_HBXJI1",
        "outputId": "4067bc6a-75d1-448e-a48c-7dcb63927cc3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ff6a003ee254babbcbef9117e86ead0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd6b5e8aaa0444f79b2c50f9894e09a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dfe8949065bb4d02ada09964ec6c317f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPKLCuwFXXCN",
        "outputId": "52c4e958-b7b0-498a-9c64-fe0cec29e8a9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input', 'output', 'x_train_len', 'instruction', 'text'],\n",
              "    num_rows: 396\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "1ff46df89712445b8d8a9ef12b184a64",
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            "c9a156db8b4145028071edbd39753fca",
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            "4513cc9ff9c846bdac81bfce67bfdea5",
            "3ed1d14b3bfc4557bd6cbf3be1398e58",
            "8709ba11bea84c1ab3cd7a2b28cfde97",
            "92b45d96bb0d4de3bd29358335613d98",
            "fe1013ed56b248b5ba3084472e2a63bd",
            "ec1dbebde3664704b9944f1e5ac6bcd4",
            "29464cc7d5fd44649b4e60aaa5298eed",
            "d007edaa1b8241008e31753b6026000d"
          ]
        },
        "id": "XxZMZ3MyXYyh",
        "outputId": "f4da75ef-225a-417e-db58-7062e919e7ef"
      },
      "execution_count": 4,
      "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": "ff220eb52892430a9bd3e8af8eaaa95d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_name = \"meta-llama/Llama-2-7b-chat-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,\n",
        "    quantization_config=bnb_config,\n",
        "    trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
            "b5c1bd3e38d74c789646cfe929013abd",
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        },
        "id": "P1m3FFybXhGV",
        "outputId": "81bb0ae9-7b26-4872-a9db-ba86e08c9a2d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5c1bd3e38d74c789646cfe929013abd"
            }
          },
          "metadata": {}
        },
        {
          "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": "96f3af2cdb354ab6859e92842fccee89"
            }
          },
          "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": "3f48f9251cc24679830a741bbd464fba"
            }
          },
          "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": "e9a6444329d04945a0fc2b0f9bbf1e82"
            }
          },
          "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": "301866e4df2a4bd19335571c6abf5350"
            }
          },
          "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": "1e2a3697425641ba84b4dcd89fd3da89"
            }
          },
          "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": "7a107c5a408a4d768c513c8a052b9943"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "95b7fb4db93e4c60b00f2b4fcd42c457",
            "5c11da50fd30494c80ab3b2ab467471e",
            "473e3b3fbf3943c69de57215d3fbb9ec",
            "92831b8cf6a348719fb0239ac8a4e69a",
            "fee6728955f546398f048abfc3d907a0",
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            "4c7f50fadc274ba7a5c32c7b00411ead",
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            "cda0986a5c1a431796d9269c7a20ff8b",
            "a68ca246052a42429fbc53fadc877f54",
            "769c34e4217542749abba6191990f439",
            "b0b42afda0b145318fe047dacf77b902",
            "6a28da0205844674aea50df10c0d29a6",
            "6f133a14fdc74b6686d223c637c95741",
            "516b3aea0bda429aafdfd93285964f2a",
            "0e56a305c10a4b8095650fed10c36551",
            "ae051db289f24029a26d65c6c4b0bb47",
            "17821b85a6d948e98c9977721f7ddf04",
            "5e5b9577d79649a5aae8b435afd784ca",
            "63749d36a74c458d9186149fecd9e0ca",
            "2c7fcd2a274f4b0ba5e351b0d4db27ed",
            "7faa9f7d35b740dd90b6a2c5b7ee9650",
            "be5ba52f76b24a4cab28fa85ee80b313",
            "71033abe280447c0930df8f6c4a7e308",
            "9f464af8bbe74362b6acaf0333174208",
            "73f22cfdfb9d4bc5b53f9656472fc382",
            "9767bae59b4e46b4bc63e16c24b4dd35",
            "b401189455cb4fb487adc5dc85e28004",
            "dd53292e5b294725b86a4c319dff17bb",
            "45e43dd511ce4a3bb63c524ff5fadd0b",
            "e0ba0448a7564b1ba1d9bd06745bca8e"
          ]
        },
        "id": "lCgdUoJXXo1r",
        "outputId": "24f0bcf2-a818-41f4-ff9e-cd29d3c4b7ef"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "95b7fb4db93e4c60b00f2b4fcd42c457"
            }
          },
          "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": "7797db667a0d489ca218d32a895e0fff"
            }
          },
          "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": "a68ca246052a42429fbc53fadc877f54"
            }
          },
          "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": "2c7fcd2a274f4b0ba5e351b0d4db27ed"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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",
        ")"
      ],
      "metadata": {
        "id": "ikx7ppHZYVHX"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "output_dir = \"./results\"\n",
        "per_device_train_batch_size = 4\n",
        "gradient_accumulation_steps = 4\n",
        "optim = \"paged_adamw_32bit\"\n",
        "save_steps = 100\n",
        "logging_steps = 10\n",
        "learning_rate = 2e-4\n",
        "max_grad_norm = 0.3\n",
        "max_steps = 100\n",
        "warmup_ratio = 0.03\n",
        "lr_scheduler_type = \"constant\"\n",
        "\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    fp16=True,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        ")"
      ],
      "metadata": {
        "id": "XkOZEFSWYY6G"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from trl import SFTTrainer\n",
        "\n",
        "max_seq_length = 512\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        ")\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "1c63aaca2e124e949fda84ce1a943585",
            "52a8f6269f4346e4949c61770c643509",
            "edaa084821d54c6aa32bd3386dd30c8d",
            "708f4177338742a1b79c69667295d158",
            "cd6e291b75b1433f8ac103bdd8fa064f",
            "26f17a8656a045869e07b13ac2177b23",
            "b61c9f835b414b1fbf6780805b2bb4f6",
            "d9a5b1478e584c09ba11b8be5e470b3a",
            "fd84f82ec54444b08e8ae8048fab4218",
            "ff7455a751ea4229a11a2f6715eacd63",
            "3148debfb563459089b70ac04ae5feb0"
          ]
        },
        "id": "oqgZvK19Ybs1",
        "outputId": "5066142c-500b-4a9d-99e1-c58ff12d4b36"
      },
      "execution_count": 9,
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/396 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c63aaca2e124e949fda84ce1a943585"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for name, module in trainer.model.named_modules():\n",
        "    if \"norm\" in name:\n",
        "        module = module.to(torch.float32)"
      ],
      "metadata": {
        "id": "ymhCrR6QYgdn"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "T7Csp5JIYl_M",
        "outputId": "52ecaa3d-3403-4936-c1c3-d4001788d6c5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 10:32, Epoch 4/5]\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>3.235800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.835500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.229700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.025300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.663600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.500400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.395000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.333200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.210100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.236800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=0.9665307879447937, metrics={'train_runtime': 643.3696, 'train_samples_per_second': 2.487, 'train_steps_per_second': 0.155, 'total_flos': 2844956636774400.0, 'train_loss': 0.9665307879447937, 'epoch': 4.04})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model  # Take care of distributed/parallel training\n",
        "model_to_save.save_pretrained(\"outputs\")"
      ],
      "metadata": {
        "id": "uuH1aS0RYojp"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_config = LoraConfig.from_pretrained('outputs')\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "id": "w_sURc4tZxjN"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = '''[INST]<<SYS>>\n",
        " You are a helpful  assistant that provides information based on the given query in context.\n",
        "<</SYS>>\n",
        "Consider the domain knowledge based on the model is finetuned as its fine tuned on Gmail prediction and then carefully perform the task as Predict the next sentence based on given words or sentence.: scott thanks for the email have\t [/INST]'''"
      ],
      "metadata": {
        "id": "loPSFdxJbH9T"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\""
      ],
      "metadata": {
        "id": "VqRo8RofbQ7B"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = tokenizer(text, return_tensors=\"pt\").to(device)"
      ],
      "metadata": {
        "id": "39UOuMEUbVR5"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model.generate(**inputs, max_new_tokens=200)"
      ],
      "metadata": {
        "id": "UsjUXP77bXmh"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRZ-SdODbagR",
        "outputId": "e9d618e9-e9a5-4ef3-bae3-eafbfe96086a"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST]<<SYS>>\n",
            " You are a helpful  assistant that provides information based on the given query in context.\n",
            "<</SYS>>\n",
            "Consider the domain knowledge based on the model is finetuned as its fine tuned on Gmail prediction and then carefully perform the task as Predict the next sentence based on given words or sentence.: scott thanks for the email have\t [/INST]  Sure, I'd be happy to help! Based on the given words \"Scott\" and \"thanks,\" it is likely that the next sentence will be a response from Scott to the email he received. Here are some possible sentences that could come next:\n",
            "\n",
            "* \"You're welcome, it was my pleasure to send you the email.\"\n",
            "* \"I appreciate your kind words, thank you for reaching out to me.\"\n",
            "* \"I'm glad you found the email helpful, let me know if you have any other questions.\"\n",
            "* \"Thank you for your email, I'll be sure to look into it.\"\n",
            "* \"I'm glad to hear from you, how can I assist you further?\"\n",
            "\n",
            "Please let me know if you would like me to generate more possibilities or if you have any other questions!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"llama2-chat-hub-my-finetuned-model-gmailComposer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "e1fe8c3d5bb744b687e12e5174f682c0",
            "ce90758a70a047c0bbeac231e34b8bc9",
            "f959363800d54165a0edec4303c8059e",
            "2f80a4a2d13d4ff783e797e4c2ad8606",
            "15bce8d77a66491f90b072febea6c3f7",
            "00f152396a174f978c57351c82c6b905",
            "c76accc2bcfb49bfab1a38463e7bdc10",
            "93f00d2ca06840ac9d3c7f53340a5c98",
            "f363decef43e47499bf35bdf8b8cdf35",
            "2babb99b034848129d350c0f5c136716",
            "3065d94e768e469bb61c07c8cf879d52"
          ]
        },
        "id": "Mj6xK46abe7W",
        "outputId": "04d4e3c9-f45e-44a6-e17f-9860f62c5cd1"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/134M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1fe8c3d5bb744b687e12e5174f682c0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Sakil/llama2-chat-hub-my-finetuned-model-gmailComposer/commit/05512b2385d7d9d5cbded6a3429ad517b6c90b35', commit_message='Upload model', commit_description='', oid='05512b2385d7d9d5cbded6a3429ad517b6c90b35', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZZh2f7gJdW5Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inferencing:\n"
      ],
      "metadata": {
        "id": "-6PrllPidhJT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
        "\n",
        "model_name = \"meta-llama/Llama-2-7b-chat-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,\n",
        "    quantization_config=bnb_config,\n",
        "    trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1f7981a8bc934853af2db0447d1b2bf9",
            "529a1d3abad04e909f32e6b6d9d1a756",
            "395a49dd9a754a39a8fb3108809bc630",
            "1aac6d75c67a4000a066c08c69839445",
            "01f250037ec144a6b126b4a3f77e24b8",
            "f71799e443184023bb2baa1a33af2a87",
            "07a341e25c514246b7bb2616a9208088",
            "faec6efe0d374f8e8491c95c4fa3b934",
            "6c2ef8904ca640e0b7b3617c0dfd28db",
            "94be1f0a719046c69e37c51b32c84016",
            "be8c0f9a2cc34cbb8d3ea715138f8744"
          ]
        },
        "id": "tWZnqDL6e4Ie",
        "outputId": "1063a929-b451-49e0-83ae-f62d78d770af"
      },
      "execution_count": 40,
      "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": "1f7981a8bc934853af2db0447d1b2bf9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token"
      ],
      "metadata": {
        "id": "s-90MRMFfBUy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_config = LoraConfig.from_pretrained('Sakil/llama2-chat-hub-my-finetuned-model-gmailComposer')\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "7ea12d38185c407d99c41befe636e124",
            "170f5cc7020c44baa12292eac2196051",
            "e6a5eb5e7732407fb9afe83be4bd6c2c",
            "ebc11c4004414128863708a74a7a94d8",
            "b74591e6fdf2435a8c3dd6d1e139148d",
            "81e5e699cdec4200bc731ac12d376ca1",
            "390b4bb6743c45088b0e3d63ba594855",
            "1db1c83b7dd94486a82fe9053b289b9c",
            "6d732fbc60734240a22c753e41a19b05",
            "235c6d1376ca44d5bf2a2a46ae90bcb3",
            "4f065684728d49489727cc3e01e11882"
          ]
        },
        "id": "hn2NacxMdkFP",
        "outputId": "9f4d36fe-c38d-44d4-ec06-592f41b48b48"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_config.json:   0%|          | 0.00/451 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ea12d38185c407d99c41befe636e124"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_next_text(text):\n",
        "\n",
        "    \"\"\"This is working summarization prompt\"\"\"\n",
        "    sys_instruction = \"[INST]<<SYS>>You are a helpful assistant that provides information based on the given query in context<</SYS>>\"\n",
        "    input_text = sys_instruction + \"Consider the domain knowledge based on the model is finetuned as its fine tuned on Gmail prediction and then carefully perform the task as Predict the next sentence based on given words or sentence.:\" + text + \"[/INST]\"\n",
        "#     print(input_text)\n",
        "\n",
        "    device = \"cuda:0\"\n",
        "    inputs = tokenizer(input_text, return_tensors=\"pt\",return_token_type_ids=False).to(device)\n",
        "    # inputs = tokenizer(input_text, return_tensors=\"pt\").to(device)\n",
        "    # inputs = tokenizer(input_text, return_tensors=\"pt\").to(device)\n",
        "#     outputs = model.generate(**inputs,min_length=50,max_new_tokens=200,early_stopping=True)\n",
        "    outputs = model.generate(**inputs,max_new_tokens=200,temperature=0.8,top_p=0.95,top_k=50)\n",
        "    summarized_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    # Remove the system instruction from the summarized text\n",
        "    return summarized_text\n",
        "    # return summarized_text.split(\"[/INST]\")[-1]"
      ],
      "metadata": {
        "id": "3FZ1Wn8Gd5_g"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_input='''\n",
        "hey any good set available for\n",
        "'''"
      ],
      "metadata": {
        "id": "o4C6HsUueKjh"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_result=get_next_text(text_input)"
      ],
      "metadata": {
        "id": "_8L86qXeePQC"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "ZurJOHM_eVUh",
        "outputId": "32ed627d-5c56-40f8-f968-1e0d288ae02b"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[INST]<<SYS>>You are a helpful assistant that provides information based on the given query in context<</SYS>>Consider the domain knowledge based on the model is finetuned as its fine tuned on Gmail prediction and then carefully perform the task as Predict the next sentence based on given words or sentence.:\\nhey any good set available for\\n[/INST]  Sure, I\\'d be happy to help! Given the context of the sentence \"Hey any good set available for,\" I would predict the next sentence to be:\\n\\n\"...purchasing or streaming.\"\\n\\nThis is based on the domain knowledge that \"set\" is a common term used in the context of music or entertainment, and the phrase \"available for\" typically implies a reference to a specific set of options or resources.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2sxvUMnveY4e"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}