Llama_2_7b_chat_fine_tuning.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEBGEddEjgOj",
        "outputId": "338d21f0-fc7c-4e4e-f07d-a4ae14fc6245"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: bitsandbytes, xxhash, dill, multiprocess, datasets, accelerate, trl, peft\n",
            "Successfully installed accelerate-0.21.0 bitsandbytes-0.40.2 datasets-2.13.1 dill-0.3.6 multiprocess-0.70.14 peft-0.4.0 trl-0.4.7 xxhash-3.2.0\n",
            "Collecting sentencepiece\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub\n",
        "!pip install transformers\n",
        "!pip install accelerate  peft  bitsandbytes  trl\n",
        "!pip install sentencepiece"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You will have to get approval to access the LLMA2 models on huggingface by filling the form on meta ai website."
      ],
      "metadata": {
        "id": "VS-OX_frW5MY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "916d21e48abd41cda720cf782f30ddf3",
            "706066a4662d4f2fb0778bb1fee06f59",
            "e597b8d2fc0347b8b37d0e3dcf235810",
            "68fee85f7261411bb20cfb6f42c002c8",
            "b5f809be0dc8498eaa5048abd927b643",
            "69dc8371a109499abffaebd8053fb84c",
            "fcea2e384c2b462687a8979112c9cde1",
            "15aeada21dd44d5087b24e14cd21aa66"
          ]
        },
        "id": "perJLzlljjAm",
        "outputId": "eb679aef-b52e-409f-dc0c-825d44f9ce5e"
      },
      "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": "74e0563079ca48e9ac2c180e9a8fc347"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import login\n",
        "\n",
        "login()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "LN4otc-bkCye"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "--1eUgCUkk3b"
      },
      "outputs": [],
      "source": [
        "model_id = \"meta-llama/Llama-2-7b-chat-hf\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "cPWT-c5zkk05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401,
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          ]
        },
        "outputId": "add91d21-d515-41bb-af3c-e7ef7cba83f9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/770 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b14946697b264b798d2cd42fe49ebf26"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a5a3a185e334a4981d5e589d62bee77"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/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": "2fa2c7a6858b47ffb1437e837279f34d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/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": "adde5bad027f43b7a74271b96e8a08bd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_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": "c13112567e1d43a49cab56e26f0f5d2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/657 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e41dfa594554c9fa5b97a51b61df9de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)fetensors.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": "1c3a6c08f99f4c54a46a52230542367c"
            }
          },
          "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": "c0950aa4650e4dfcb7ac348aa7059993"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)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": "33c2bc8af138463a847c0d9878eebc60"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)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": "45c54dc3632748cdadf163753dd415ce"
            }
          },
          "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": "6b6a0790fd274e0e8dd522a6d6e3b3ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/197 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "19fdf7ff68ed4a6ebefcc8d6364a4bdd"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Llama-2-7b-chat-hf\", trust_remote_code=True)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id, quantization_config=bnb_config, device_map={\"\": 0})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "whCLVbwLkkvc"
      },
      "outputs": [],
      "source": [
        "from peft import prepare_model_for_kbit_training\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "9sWFlFb9kkq-"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "\n",
        "    Prints the number of trainable parameters in the model.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "\n",
        "    print(f\"trainable params: {trainable_params} | | all params: {all_param} | | trainable % : {100 * trainable_params / all_param}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ijf2S26qliP4"
      },
      "outputs": [],
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q_proj\", \"v_proj\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "XFPU7h9DljVj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1c7d282-4b00-4f68-d569-1fef849a2238"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 4194304 | | all params: 3504607232 | | trainable % : 0.11967971650867153\n"
          ]
        }
      ],
      "source": [
        "model = get_peft_model(model, config)\n",
        "\n",
        "print_trainable_parameters(model)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Ib5XhAmtlm4m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
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            "92210dceb01141c3bb85772d8aa82bc6",
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            "14a4a286732d471883cf0e42de5077d7",
            "958d443cfaf14abe9814e387876691f1",
            "2b085ee3a7a74b14a882b5a36298243b",
            "183bfbbe67e64d6592353923dba06cfa",
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            "0efe7360bed6402386256a63fe3675ea",
            "067d7ee90cf64119a9f11543565f5986",
            "76269e713f0b4fdbae89ef195c7a53ef",
            "e01e7ce770f94455ab0bcfbeb7e91804",
            "46d3aa8dd8e0486b9848050760604e60",
            "9ff92d15ee094315a22b1553c8fa03b4"
          ]
        },
        "outputId": "e53605f6-5935-4935-fb67-cedc98eaafdd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/395 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3ddf1e50b1d44a61930c0f2f5f3c832a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/timdettmers--openassistant-guanaco to /root/.cache/huggingface/datasets/timdettmers___json/timdettmers--openassistant-guanaco-6126c710748182cf/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94e3729beda749cb9a5484a2c0790381"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/20.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "61dfd7ddde124266a083f30521b981bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a55969cdfe29442689c34ed39886d102"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "98c95c1ed13142319dbb8ae9c9b91bd0"
            }
          },
          "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": "d4229beb7b1340d18ea9c6da0f74067f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c1da5f2dd02141ed8eb3f5565f415e03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/timdettmers___json/timdettmers--openassistant-guanaco-6126c710748182cf/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"timdettmers/openassistant-guanaco\", split=\"train\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89,
          "referenced_widgets": [
            "2b0c13fa17b44fc2a0896ee9532033ed",
            "e83b7ac5d41b4d3fbcfb40789a8e83cf",
            "0b9d074b1dad4150bb06c7965451dcb7",
            "e88b9a3e708e44ee8dd243a274cf179d",
            "d45b4a03c7054f669cdc975595b6da6b",
            "5c37664cc9194b47a49190ca5be7b04b",
            "51c195ebaebb4867ae3da9dbcbc223c9",
            "12421f7a7d0647f888adc1d00a180621",
            "9ae188f3152c4200a34a9f72e192ba7d",
            "306b7654a5e644c9a7eec5d88803866b",
            "194af483ceb54f74aba7f62fae5213a9"
          ]
        },
        "id": "dgbODVmdhkDK",
        "outputId": "102013e0-d0ee-40c0-bebe-0f8bac598a8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using pad_token, but it is not set yet.\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:159: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/9846 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b0c13fa17b44fc2a0896ee9532033ed"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import transformers\n",
        "from trl import SFTTrainer\n",
        "\n",
        "# needed for llama tokenizer\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "  model=model,\n",
        "  train_dataset=dataset,\n",
        "  dataset_text_field=\"text\",\n",
        "  args=transformers.TrainingArguments(\n",
        "  per_device_train_batch_size=1,\n",
        "  gradient_accumulation_steps=4,\n",
        "  warmup_steps=2,\n",
        "  max_steps=10,          #change accordingly\n",
        "  learning_rate=2e-4,\n",
        "  fp16=True,\n",
        "  logging_steps=1,\n",
        "  output_dir=\"outputs\",\n",
        "  optim=\"paged_adamw_8bit\"\n",
        "),\n",
        "data_collator=transformers.DataCollatorForLanguageModeling(\n",
        "    tokenizer, mlm=False),\n",
        "\n",
        ")\n",
        "\n",
        "model.config.use_cache=False  # silence the warnings. Please re-enable for inference!\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "DgGEkzpwjY7m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "d41b92d4-71f8-4d97-8f3b-27c14aa34bd7"
      },
      "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10' max='10' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10/10 01:45, Epoch 0/1]\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>1</td>\n",
              "      <td>2.538800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.885500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.269800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.409600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.073600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.261000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.908700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.386600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.098000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.633900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=10, training_loss=1.8465556859970094, metrics={'train_runtime': 116.9114, 'train_samples_per_second': 0.342, 'train_steps_per_second': 0.086, 'total_flos': 409054641020928.0, 'train_loss': 1.8465556859970094, 'epoch': 0.0})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "trainer.train()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inference**\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "mYcqOJjOUTbj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "prompt = \"\"\"### Human: Write a short story about two students trapped in a haunted house in Montana. ### Assistant:\"\"\"\n",
        "\n",
        "\n",
        "pipe = pipeline(\n",
        "            \"text-generation\",\n",
        "            model=model,\n",
        "            tokenizer=tokenizer,\n",
        "            torch_dtype=torch.bfloat16,\n",
        "            trust_remote_code=True,\n",
        "            device_map=\"auto\",\n",
        "    )\n",
        "\n",
        "sequences = pipe(\n",
        "            prompt,\n",
        "            max_length=1000,\n",
        "            do_sample=True,\n",
        "            top_k=10,\n",
        "            num_return_sequences=1,\n",
        "            eos_token_id=tokenizer.eos_token_id,\n",
        "            pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "\n",
        "for seq in sequences:\n",
        "    print(seq['generated_text'])\n"
      ],
      "metadata": {
        "id": "U8GqDSpFxioF",
        "outputId": "69e37189-bf9f-4102-c404-a91770c2c3e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n",
            "The model 'PeftModelForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MusicgenForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### Human: Write a short story about two students trapped in a haunted house in Montana. ### Assistant: Sure, here is a short story about two students trapped in a haunted house in Montana. \n",
            "\n",
            "Two students, Emily and Ryan, were on their way to an overnight school trip in a small town in Montana. The bus ride there was long and boring, but they both looked forward to the adventure that awaited. They had planned to spend the night in an old abandoned house that was rumored to be haunted.\n",
            "The trip to the house was through the woods and over uneven terrain. The sun had already set, and they were plunged into darkness. Emily, feeling anxious and scared, clung to Ryan. They heard strange noises all around them, and their flashlights flickered and went out. \n",
            "They found themselves trapped and alone in the dark. As the night continued, Ryan tried to reassure Emily with stories of other students who had also been there before. He even tried to tell ghost stories, hoping to make Emily laugh. However, he soon realized that she was not in the mood to listen to jokes. He then suggested that they call the authorities to come and find them, but they had found no phone signal. \n",
            "They heard footsteps, moaning, and other strange noises, and they tried to stay as silent as possible. They tried to make out the sounds around them, but they could not identify them. The night seemed endless, and they could not help but question if the rumors were true.\n",
            "\n",
            "As the night passed, they realized that they might never survive this night. Emily, who had been brave earlier, became more desperate for an escape. Ryan told her to wait and see whether they could make it through until morning. \n",
            "Just as the sun was rising, they heard footsteps outside their room. As they peaked out, they could barely see a figure standing in the doorway. Ryan and Emily were both scared, but they found their strength. They rushed to the door and managed to escape before the figure could get to them. They made their way back to the town, exhausted and shaken but grateful to have survived the night. From that day on, the haunted house remained a part of the legends of the small Montana town.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "import transformers\n",
        "import torch\n",
        "\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "sequences = pipeline(\n",
        "    'I liked batman. Do you have any recommendations of other shows or movie I might like?\\n',\n",
        "    do_sample=True,\n",
        "    top_k=10,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    pad_token_id=tokenizer.eos_token_id,\n",
        "    max_length=500,\n",
        ")\n",
        "for seq in sequences:\n",
        "    print(f\"Result: {seq['generated_text']}\")"
      ],
      "metadata": {
        "id": "AuaFMZxQ0sBT",
        "outputId": "20dcdfd3-04ed-488d-bdf7-85bffa9247f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The model 'PeftModelForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MusicgenForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Result: I liked batman. Do you have any recommendations of other shows or movie I might like?\n",
            "I have no doubt that if you are interested in the Dark Knight, that you will also enjoy other action packed super hero adventures. Here are three more suggestions that you might enjoy as well!\n",
            "1. The Super Hero Squad Show.\n",
            "The Super Hero Squadron Show is an animated television program that follows six superheroes from the Marvel Universe. Their goal is to save the world from evil villains and stop them from taking over the world. The show follows Ironman, Captain America, Thor, the Incredible Hulk, Spiderwoman and Wolverine on their super heroic adventures. The show is very much geared towards a young audience and will appeal to kids from 6-12 and parents alike!\n",
            "2. Young Justice.\n",
            "Young Justice is based on the DC Comics and follows the super heroic adventures of the teenage super heroes known as the Justice League. The show follows the adventures of Green Arrow, Robin, Super girl as well as others as tey fight crime and stop the forces of evil from causing harm. Like the Super Hero Squad Show this cartoon is very geared towards young audience and will also be enjoyable to parents as well!\n",
            "3. Ben 10.\n",
            "Ben 10 follows the adventures of an average ten-year-old who is able to transform into alien and use his powers to save the world. The show follows Ben Tennyson on his journey as he battles villains and saves the world in different ways and settings. The show is geared toward a young audience, but will also be pleasing to adults that enjoy action packed cartoon.\n",
            "I recommend giving each show a try, especially if you are a fan of batman!\n"
          ]
        }
      ]
    }
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