09.deploy-llm-to-production.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "source": [
    "!nvidia-smi"
   ],
   "metadata": {
    "colab": {
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    },
    "id": "c_apn2jCXHAo",
    "outputId": "1717fa9a-abbf-4a55-95b9-e10809db1561"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mon Jun 19 21:03:19 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   46C    P8     9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "HfX18IUEBc4y",
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     "base_uri": "https://localhost:8080/"
    },
    "outputId": "56de385a-7edb-49cb-a9f7-5caa8485db89"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
      "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
     ]
    }
   ],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq bitsandbytes==0.39.0 --progress-bar off\n",
    "!pip install -qqq torch==2.0.1 --progress-bar off\n",
    "!pip install -qqq transformers==4.30.2 --progress-bar off\n",
    "!pip install -qqq accelerate==0.20.3 --progress-bar off\n",
    "!pip install -qqq -U git+https://github.com/huggingface/peft.git --progress-bar off\n",
    "!pip install -qqq datasets==2.12.0 --progress-bar off\n",
    "!pip install -qqq loralib==0.1.1 --progress-bar off\n",
    "!pip install -qqq einops==0.6.1 --progress-bar off"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from huggingface_hub import notebook_login\n",
    "from peft import PeftConfig, PeftModel\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig"
   ],
   "metadata": {
    "id": "T0WQym2EFR6H",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "c20233be-99e7-4d17-e634-dc120c7aa283"
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please run\n",
      "\n",
      "python -m bitsandbytes\n",
      "\n",
      " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n",
      "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
      "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
      "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
      "CUDA SETUP: Detected CUDA version 118\n",
      "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('http'), PosixPath('//172.28.0.1')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-hm-dhirtt1vpk5y --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
      "Either way, this might cause trouble in the future:\n",
      "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
      "  warn(msg)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "notebook_login()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
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    },
    "id": "MPIvCuXFMVRw",
    "outputId": "d521bf89-c236-48b1-d9b6-6db88eaae9c1"
   },
   "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": "7edb4a77a0424342a3c81fb1b9cd5958"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Merge QLoRA Adapter"
   ],
   "metadata": {
    "id": "OG38hHUVx4Id"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16\n",
    "\n",
    "MODEL_ID = \"curiousily/falcon-7b-qlora-chat-support-bot-faq\"\n",
    "config = PeftConfig.from_pretrained(MODEL_ID)\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    config.base_model_name_or_path,\n",
    "    return_dict=True,\n",
    "    device_map=\"auto\",\n",
    "    torch_dtype=dtype,\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "\n",
    "model = PeftModel.from_pretrained(model, MODEL_ID)"
   ],
   "metadata": {
    "id": "R2l5LcBeFvbX",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476,
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    },
    "outputId": "36535a4f-5758-4378-ac6e-e09af8a4d47d"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/adapter_config.json:   0%|          | 0.00/410 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
      "- configuration_RW.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b:\n",
      "- modelling_RW.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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      }
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      }
     },
     "metadata": {}
    },
    {
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      }
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      }
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     "metadata": {}
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    {
     "output_type": "display_data",
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      }
     },
     "metadata": {}
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      ],
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
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      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model"
   ],
   "metadata": {
    "id": "z7EKVvvjGHnq",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "833d6a74-e2e6-4ee2-ac48-b50fe5d0a582"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "PeftModelForCausalLM(\n",
       "  (base_model): LoraModel(\n",
       "    (model): RWForCausalLM(\n",
       "      (transformer): RWModel(\n",
       "        (word_embeddings): Embedding(65024, 4544)\n",
       "        (h): ModuleList(\n",
       "          (0-31): 32 x DecoderLayer(\n",
       "            (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)\n",
       "            (self_attention): Attention(\n",
       "              (maybe_rotary): RotaryEmbedding()\n",
       "              (query_key_value): Linear(\n",
       "                in_features=4544, out_features=4672, bias=False\n",
       "                (lora_dropout): ModuleDict(\n",
       "                  (default): Dropout(p=0.05, inplace=False)\n",
       "                )\n",
       "                (lora_A): ModuleDict(\n",
       "                  (default): Linear(in_features=4544, out_features=16, bias=False)\n",
       "                )\n",
       "                (lora_B): ModuleDict(\n",
       "                  (default): Linear(in_features=16, out_features=4672, bias=False)\n",
       "                )\n",
       "                (lora_embedding_A): ParameterDict()\n",
       "                (lora_embedding_B): ParameterDict()\n",
       "              )\n",
       "              (dense): Linear(in_features=4544, out_features=4544, bias=False)\n",
       "              (attention_dropout): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "            (mlp): MLP(\n",
       "              (dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False)\n",
       "              (act): GELU(approximate='none')\n",
       "              (dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "      (lm_head): Linear(in_features=4544, out_features=65024, bias=False)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model = model.merge_and_unload()"
   ],
   "metadata": {
    "id": "8bY6X6bWKWF5"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "model"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UD1Wnl7JKWap",
    "outputId": "437b9d04-35d4-4b26-f00c-c32c9ba496a9"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RWForCausalLM(\n",
       "  (transformer): RWModel(\n",
       "    (word_embeddings): Embedding(65024, 4544)\n",
       "    (h): ModuleList(\n",
       "      (0-31): 32 x DecoderLayer(\n",
       "        (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)\n",
       "        (self_attention): Attention(\n",
       "          (maybe_rotary): RotaryEmbedding()\n",
       "          (query_key_value): Linear(in_features=4544, out_features=4672, bias=False)\n",
       "          (dense): Linear(in_features=4544, out_features=4544, bias=False)\n",
       "          (attention_dropout): Dropout(p=0.0, inplace=False)\n",
       "        )\n",
       "        (mlp): MLP(\n",
       "          (dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False)\n",
       "          (act): GELU(approximate='none')\n",
       "          (dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (lm_head): Linear(in_features=4544, out_features=65024, bias=False)\n",
       ")"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Push Model to HuggingFace Hub"
   ],
   "metadata": {
    "id": "jXUzgFAMdhNT"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "model.push_to_hub(\n",
    "    \"curiousily/falcon-7b-qlora-chat-support-bot-faq-merged\", use_auth_token=True\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 149,
     "referenced_widgets": [
      "94ab9ddc05504ae1a06cb1aa6ad0437a",
      "13a4072a30274cb1902a30f344dfbb7f",
      "58c1c49349c84e0b948954e45c1aa293",
      "c4f162ebff6f43d2a801a4bc83c7f443",
      "6871e5a3af614ac086bb6166e83e1c77",
      "0bca6fa17cbd4a32a0aee02c7b9ebb47",
      "d37b47fa782b4affb2ec01a885a50188",
      "7a9c1b1eae1c4ffda004a2072ea7f7aa",
      "21bd66d9b89e47478d5b73c2fa8928b4",
      "c1ea55fe2f844df2a2ecda93b0595e08",
      "1b82aa8f73b54ad4a2c4b8eaddb12088",
      "86eb5733b8f742a99b3d739d46e400c0",
      "8509edc33cb042eaa2fdcbbb6bbcbe3c",
      "8f20a4208ee745ec878f67ad665ffb03",
      "877821b96a3346469437cc3c215e9bee",
      "ce704dca01a64b859f466f09d006e44e",
      "943f0621b9634158af419a378b4f8414",
      "a69165c96516439cb0b6c96b4f76b505",
      "2420e264b83845f6ad2d359ca796e14a",
      "8b9948a7147844e7860f51fc3a9dd325",
      "436dd7bc30a04e58a19f39035e67ed3e",
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      "a75035dc861740b29131c41775142c83",
      "2e2824878ae841eebfc9e3d1e19ada1d",
      "fa75bb5a69f745e4b30e8cce416d62b1",
      "b96ed2de60ff4b9ebe8c95c4284f9ac7",
      "c1d46254c3ad498ebafd82b1392eacc1",
      "cfb8132d5d3b430480cfb4a2620d9edb",
      "7e8df81dc62349779ccb028efd5c68e5",
      "f5055d2e078e4bb5b129d8c244e73579",
      "8a5ca715575740a99367cb3f26438082",
      "a01228b85a9a485dbce486de587245a6",
      "a05508fce77d4e80b564f31331f2f0f6"
     ]
    },
    "id": "kuxSBkU1MIgA",
    "outputId": "d46c7b90-459b-4a70-8bff-f58bf5bfc929"
   },
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94ab9ddc05504ae1a06cb1aa6ad0437a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model-00002-of-00002.bin:   0%|          | 0.00/3.89G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86eb5733b8f742a99b3d739d46e400c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a75035dc861740b29131c41775142c83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model-00001-of-00002.bin:   0%|          | 0.00/9.95G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/curiousily/falcon-7b-qlora-chat-support-bot-faq-merged/commit/4f4678eacf2630cc1c9b481018c93e49cc70b27b', commit_message='Upload RWForCausalLM', commit_description='', oid='4f4678eacf2630cc1c9b481018c93e49cc70b27b', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"tiiuae/falcon-7b\")\n",
    "tokenizer.push_to_hub(\n",
    "    \"curiousily/falcon-7b-qlora-chat-support-bot-faq-merged\", use_auth_token=True\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D6ghQRtgkpHa",
    "outputId": "545d61c7-f7eb-4102-9a6e-421eb3a4c98a"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/curiousily/falcon-7b-qlora-chat-support-bot-faq-merged/commit/8aa89523e3796c527af41e7e9dfe2699eba499e9', commit_message='Upload tokenizer', commit_description='', oid='8aa89523e3796c527af41e7e9dfe2699eba499e9', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Inference"
   ],
   "metadata": {
    "id": "Ng_CZQEwx1qN"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import transformers\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "model = \"curiousily/falcon-7b-qlora-chat-support-bot-faq-merged\"\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model, trust_remote_code=True)"
   ],
   "metadata": {
    "id": "NNsHZpuCYRXs",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
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    "outputId": "0963fe92-a980-4b82-cef6-81234666897d"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/180 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2b12f928d59a4302b5a3625ce79f159c"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "8d8d28683d9c4d37a88af7fadc11f354"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/281 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "4a5d319940854508a5c9b3431023ae54"
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     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model,\n",
    "    return_dict=True,\n",
    "    device_map=\"auto\",\n",
    "    load_in_8bit=True,\n",
    "    torch_dtype=dtype,\n",
    "    trust_remote_code=True,\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 968,
     "referenced_widgets": [
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    },
    "id": "wyK4g_PqkwAv",
    "outputId": "5a784596-717e-4fae-b42a-351256ca0601"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/988 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "aa293e4a865147e7a2fd9fc11de6afc9"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/configuration_RW.py:   0%|          | 0.00/2.61k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "1bb293db132f49e4b58bddbc2d7e02ce"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/curiousily/falcon-7b-qlora-chat-support-bot-faq-merged:\n",
      "- configuration_RW.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)main/modelling_RW.py:   0%|          | 0.00/47.6k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/curiousily/falcon-7b-qlora-chat-support-bot-faq-merged:\n",
      "- modelling_RW.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
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      }
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       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
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     "metadata": {}
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "===================================BUG REPORT===================================\n",
      "Welcome to bitsandbytes. For bug reports, please run\n",
      "\n",
      "python -m bitsandbytes\n",
      "\n",
      " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
      "================================================================================\n",
      "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
      "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
      "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
      "CUDA SETUP: Detected CUDA version 118\n",
      "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('http'), PosixPath('//172.28.0.1')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-hm-2cgaz274tw58q --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
      "  warn(msg)\n",
      "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
      "Either way, this might cause trouble in the future:\n",
      "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
      "  warn(msg)\n"
     ]
    },
    {
     "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": "64b4df9471244e6bac10f85d01436f17"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)neration_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0659164d4d5044c8a5782870d5b21de7"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 60\n",
    "generation_config.temperature = 0\n",
    "generation_config.num_return_sequences = 1\n",
    "generation_config.pad_token_id = tokenizer.eos_token_id\n",
    "generation_config.eos_token_id = tokenizer.eos_token_id"
   ],
   "metadata": {
    "id": "pp0bDWG6nerI"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "pipeline = transformers.pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WwAGjdxPYwul",
    "outputId": "b932abd0-c602-4f22-9b94-54bede336d76"
   },
   "execution_count": 4,
   "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 'RWForCausalLM' 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', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', '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"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = f\"\"\"\n",
    "<human>: How can I create an account?\n",
    "<assistant>:\n",
    "\"\"\".strip()\n",
    "\n",
    "result = pipeline(\n",
    "    prompt,\n",
    "    generation_config=generation_config,\n",
    ")"
   ],
   "metadata": {
    "id": "jAUZTOtacqPs"
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "result"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "o8GCr1qtkHvT",
    "outputId": "873c7eff-b233-466f-e76c-7a6ef2c4c0ea"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'generated_text': \"<human>: How can I create an account?\\n<assistant>: To create an account, click the 'Sign Up' button on the top right corner of our website and fill out the registration form with your details. Once you complete the form, you will receive an email with a verification link. Click on the link to confirm your account and start shopping.\\n\"}]"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(result[0][\"generated_text\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6nESn6AalyWc",
    "outputId": "a68bfe47-24bc-4ec7-e5fe-5bd79d99fa08"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: How can I create an account?\n",
      "<assistant>: To create an account, click the 'Sign Up' button on the top right corner of our website and fill out the registration form with your details. Once you complete the form, you will receive an email with a verification link. Click on the link to confirm your account and start shopping.\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Handler"
   ],
   "metadata": {
    "id": "NqAKsbbLxoCN"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from typing import Any, Dict, List\n",
    "\n",
    "import torch\n",
    "import transformers\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16\n",
    "\n",
    "\n",
    "class EndpointHandler:\n",
    "    def __init__(self, path=\"\"):\n",
    "        tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)\n",
    "        model = AutoModelForCausalLM.from_pretrained(\n",
    "            path,\n",
    "            return_dict=True,\n",
    "            device_map=\"auto\",\n",
    "            load_in_8bit=True,\n",
    "            torch_dtype=dtype,\n",
    "            trust_remote_code=True,\n",
    "        )\n",
    "\n",
    "        generation_config = model.generation_config\n",
    "        generation_config.max_new_tokens = 60\n",
    "        generation_config.temperature = 0\n",
    "        generation_config.num_return_sequences = 1\n",
    "        generation_config.pad_token_id = tokenizer.eos_token_id\n",
    "        generation_config.eos_token_id = tokenizer.eos_token_id\n",
    "        self.generation_config = generation_config\n",
    "\n",
    "        self.pipeline = transformers.pipeline(\n",
    "            \"text-generation\", model=model, tokenizer=tokenizer\n",
    "        )\n",
    "\n",
    "    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:\n",
    "        prompt = data.pop(\"inputs\", data)\n",
    "        result = self.pipeline(prompt, generation_config=self.generation_config)\n",
    "        return result"
   ],
   "metadata": {
    "id": "lHyAbDi2p3nR"
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "MODEL_ID = \"curiousily/falcon-7b-qlora-chat-support-bot-faq-merged\"\n",
    "my_handler = EndpointHandler(path=MODEL_ID)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582,
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      "d188edd992ff4d0594f587fd117cea1d",
      "c48929477d584455a32c3ded012a07ed",
      "adca22cf5e7e418a8454f30d423dd85c",
      "f4c352a2e9404d71a68f35a6b0c16137",
      "79e103408d5b471a8f81d55c59349927",
      "fb1bf33b4db84dd7ac7c2512c2a5906f",
      "8aac7ca24ed345a0a8ec8aa70e88a83d",
      "cec604ef4ad0412895788ff8fd67fbf7",
      "846916edca6343b993d17317816972f3",
      "f93829f79db04277b4bdc735d398fc62",
      "996db6d4c3f84a3192a0951f0911737c",
      "14c98818807d4d87853e8f3f2a30cfdf",
      "be738cc5ba704c06b2976f810795da61",
      "497c274bff3d43209b7930ab8f677de6",
      "7acd459c70e94403986fb2dfb918b38f",
      "73ad2df3411c4d4f8ab8923adb8a0a55",
      "54ea11f17096451eace07568ec10886a",
      "cadc73369e824938ba188d8e9e8940cb",
      "cc542a8ceb6a46058fc6a5855bd584c3",
      "48db99344d6d424290478750d4a47f98"
     ]
    },
    "id": "X3Xm2cxkupbL",
    "outputId": "176ccba2-91e4-434c-b156-1922eebcc3c0"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/180 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.73M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/curiousily/falcon-7b-qlora-chat-support-bot-faq-merged:\n",
      "- configuration_RW.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/curiousily/falcon-7b-qlora-chat-support-bot-faq-merged:\n",
      "- modelling_RW.py\n",
      ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
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      }
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     "metadata": {}
    },
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "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 'RWForCausalLM' 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', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', '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"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = f\"\"\"\n",
    "<human>: How can I create an account?\n",
    "<assistant>:\n",
    "\"\"\".strip()\n",
    "\n",
    "payload = {\"inputs\": prompt}"
   ],
   "metadata": {
    "id": "RXuHuN9ButKw"
   },
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prediction = my_handler(payload)"
   ],
   "metadata": {
    "id": "ZKvctyk2u7DV"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prediction"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RF-ApPuEvH8X",
    "outputId": "d1d69d9f-f9ce-43b4-fbec-5b727390c2d9"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'generated_text': \"<human>: How can I create an account?\\n<assistant>: To create an account, click the 'Sign Up' button on the top right corner of our website and fill out the registration form. Once you complete the form and submit it, you will receive an email with instructions on how to activate your account.\\nIf you encounter any issues during the registration\"}]"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Use the API"
   ],
   "metadata": {
    "id": "CJc_5W7XeHoy"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import requests"
   ],
   "metadata": {
    "id": "C-pZ8PWgeGwu"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "API_URL = \"https://o7oz3w0gfm6mo8sj.us-east-1.aws.endpoints.huggingface.cloud\"\n",
    "API_TOKEN = \"YOUR_HUGGINGFACE_TOKEN\"\n",
    "headers = {\"Authorization\": f\"Bearer {API_TOKEN}\"}\n",
    "\n",
    "prompt = f\"\"\"\n",
    "<human>: How can I create an account?\n",
    "<assistant>:\n",
    "\"\"\".strip()\n",
    "\n",
    "payload = {\"inputs\": prompt}"
   ],
   "metadata": {
    "id": "jQXxn0qze6yR"
   },
   "execution_count": 23,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "resp = requests.post(API_URL, json=payload, headers=headers)\n",
    "payload = resp.json()"
   ],
   "metadata": {
    "id": "7IOjZ9pKfCGH"
   },
   "execution_count": 24,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "payload"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qNX0S8aJhPTE",
    "outputId": "3c6bd631-ad8d-4762-b2f6-1bb70fb0f0fa"
   },
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'generated_text': \"<human>: How can I create an account?\\n<assistant>: To create an account, click the 'Sign Up' button on the top right corner of our website and fill out the registration form. Once you complete the form and submit it, you will receive an email with instructions on how to activate your account.\\nIf you encounter any issues during the registration\"}]"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(payload[0][\"generated_text\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5lFMHXLBhGSQ",
    "outputId": "a71d42c1-b91e-4bf3-b903-d48527a1fd6c"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: How can I create an account?\n",
      "<assistant>: To create an account, click the 'Sign Up' button on the top right corner of our website and fill out the registration form. Once you complete the form and submit it, you will receive an email with instructions on how to activate your account.\n",
      "If you encounter any issues during the registration\n"
     ]
    }
   ]
  }
 ]
}