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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Un3WVx7-LHq3",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "1fecdc00-021f-4283-f048-76be09b58096"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sat Jul 15 11:16:05 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   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"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq bitsandbytes==0.40.0 --progress-bar off\n",
    "!pip install -qqq torch==2.0.1 --progress-bar off\n",
    "!pip install -qqq transformers==4.30.0 --progress-bar off\n",
    "!pip install -qqq accelerate==0.21.0 --progress-bar off\n",
    "!pip install -qqq xformers==0.0.20 --progress-bar off\n",
    "!pip install -qqq einops==0.6.1 --progress-bar off\n",
    "!pip install -qqq langchain==0.0.233 --progress-bar off"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ETvVhniXVXYE",
    "outputId": "b0df5c39-46fe-4d57-bbef-670e1c4eb910"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import re\n",
    "import warnings\n",
    "from typing import List\n",
    "\n",
    "import torch\n",
    "from langchain import PromptTemplate\n",
    "from langchain.chains import ConversationChain\n",
    "from langchain.chains.conversation.memory import ConversationBufferWindowMemory\n",
    "from langchain.llms import HuggingFacePipeline\n",
    "from langchain.schema import BaseOutputParser\n",
    "from transformers import (\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    StoppingCriteria,\n",
    "    StoppingCriteriaList,\n",
    "    pipeline,\n",
    ")\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K1eINJNyg7rk",
    "outputId": "19e21058-55bd-4621-88ff-1d8e3e89d612"
   },
   "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.11.0\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('//172.28.0.1'), PosixPath('http')}\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-2lkgl9pfxu52w --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('module'), PosixPath('//ipykernel.pylab.backend_inline')}\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.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. 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": "markdown",
   "source": [
    "## Load Model"
   ],
   "metadata": {
    "id": "CI5KSV7rTaZf"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "MODEL_NAME = \"tiiuae/falcon-7b-instruct\"\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    MODEL_NAME, trust_remote_code=True, load_in_8bit=True, device_map=\"auto\"\n",
    ")\n",
    "model = model.eval()\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "print(f\"Model device: {model.device}\")"
   ],
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   },
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/667 [00:00<?, ?B/s]"
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     "data": {
      "text/plain": [
       "Downloading (…)/configuration_RW.py:   0%|          | 0.00/2.61k [00:00<?, ?B/s]"
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      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2d117049df4c4d0189a8c1971e7c2e16"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-7b-instruct:\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%|          | 0.00/47.5k [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/tiiuae/falcon-7b-instruct:\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": {
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      }
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      ],
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      }
     },
     "metadata": {}
    },
    {
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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,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
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      ],
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       "model_id": "43796bce747448bfb7a7b5dc806ce038"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model device: cuda:0\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!nvidia-smi"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JJigMkPHgXUf",
    "outputId": "f7895c1f-ad3a-405f-8f44-c82998ce225e"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sat Jul 15 11:20:20 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   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   55C    P0    29W /  70W |   8351MiB / 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",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "generation_config = model.generation_config\n",
    "generation_config.temperature = 0\n",
    "generation_config.num_return_sequences = 1\n",
    "generation_config.max_new_tokens = 256\n",
    "generation_config.use_cache = False\n",
    "generation_config.repetition_penalty = 1.7\n",
    "generation_config.pad_token_id = tokenizer.eos_token_id\n",
    "generation_config.eos_token_id = tokenizer.eos_token_id\n",
    "generation_config"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5HSP4uschELo",
    "outputId": "6ee21c11-d17a-43f4-c27e-a587e4d78ea4"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "GenerationConfig {\n",
       "  \"_from_model_config\": true,\n",
       "  \"bos_token_id\": 1,\n",
       "  \"eos_token_id\": 11,\n",
       "  \"max_new_tokens\": 256,\n",
       "  \"pad_token_id\": 11,\n",
       "  \"repetition_penalty\": 1.7,\n",
       "  \"temperature\": 0,\n",
       "  \"transformers_version\": \"4.30.0\",\n",
       "  \"use_cache\": false\n",
       "}"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "model.config"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7u5d_toJiem-",
    "outputId": "56ebc2cc-1754-4623-a563-1e8b647d30cc"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RWConfig {\n",
       "  \"_name_or_path\": \"tiiuae/falcon-7b-instruct\",\n",
       "  \"alibi\": false,\n",
       "  \"apply_residual_connection_post_layernorm\": false,\n",
       "  \"architectures\": [\n",
       "    \"RWForCausalLM\"\n",
       "  ],\n",
       "  \"attention_dropout\": 0.0,\n",
       "  \"auto_map\": {\n",
       "    \"AutoConfig\": \"tiiuae/falcon-7b-instruct--configuration_RW.RWConfig\",\n",
       "    \"AutoModelForCausalLM\": \"tiiuae/falcon-7b-instruct--modelling_RW.RWForCausalLM\"\n",
       "  },\n",
       "  \"bias\": false,\n",
       "  \"bos_token_id\": 11,\n",
       "  \"eos_token_id\": 11,\n",
       "  \"hidden_dropout\": 0.0,\n",
       "  \"hidden_size\": 4544,\n",
       "  \"initializer_range\": 0.02,\n",
       "  \"layer_norm_epsilon\": 1e-05,\n",
       "  \"model_type\": \"RefinedWebModel\",\n",
       "  \"multi_query\": true,\n",
       "  \"n_head\": 71,\n",
       "  \"n_layer\": 32,\n",
       "  \"parallel_attn\": true,\n",
       "  \"quantization_config\": {\n",
       "    \"bnb_4bit_compute_dtype\": \"float32\",\n",
       "    \"bnb_4bit_quant_type\": \"fp4\",\n",
       "    \"bnb_4bit_use_double_quant\": false,\n",
       "    \"llm_int8_enable_fp32_cpu_offload\": false,\n",
       "    \"llm_int8_has_fp16_weight\": false,\n",
       "    \"llm_int8_skip_modules\": null,\n",
       "    \"llm_int8_threshold\": 6.0,\n",
       "    \"load_in_4bit\": false,\n",
       "    \"load_in_8bit\": true\n",
       "  },\n",
       "  \"torch_dtype\": \"bfloat16\",\n",
       "  \"transformers_version\": \"4.30.0\",\n",
       "  \"use_cache\": true,\n",
       "  \"vocab_size\": 65024\n",
       "}"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "prompt = \"Who is Dwight K Schrute?\"\n",
    "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
    "input_ids = input_ids.to(model.device)"
   ],
   "metadata": {
    "id": "7FHbekhIhMiv"
   },
   "execution_count": 8,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "\n",
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=input_ids,\n",
    "        generation_config=generation_config,\n",
    "    )"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ifAkcBP_hR0V",
    "outputId": "e8177ed4-97df-41ca-e031-df542eb5ca19"
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 7.48 s, sys: 251 ms, total: 7.73 s\n",
      "Wall time: 10.9 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "print(response)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w-Mi5rPFhVk4",
    "outputId": "83efddee-4c7e-4d82-b727-6372d8674b36"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Who is Dwight K Schrute?\n",
      "Dwight K Schrute is a fictional character from the TV show 'The Office'. He is portrayed by actor Rainn Wilson and is known for his love of beet soup and his hatred of paper.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Stop LLM From Rambling"
   ],
   "metadata": {
    "id": "jUjR8xlZS_07"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class StopGenerationCriteria(StoppingCriteria):\n",
    "    def __init__(\n",
    "        self, tokens: List[List[str]], tokenizer: AutoTokenizer, device: torch.device\n",
    "    ):\n",
    "        stop_token_ids = [tokenizer.convert_tokens_to_ids(t) for t in tokens]\n",
    "        self.stop_token_ids = [\n",
    "            torch.tensor(x, dtype=torch.long, device=device) for x in stop_token_ids\n",
    "        ]\n",
    "\n",
    "    def __call__(\n",
    "        self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs\n",
    "    ) -> bool:\n",
    "        for stop_ids in self.stop_token_ids:\n",
    "            if torch.eq(input_ids[0][-len(stop_ids) :], stop_ids).all():\n",
    "                return True\n",
    "        return False"
   ],
   "metadata": {
    "id": "AZty_vG7VEv3"
   },
   "execution_count": 19,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "stop_tokens = [[\"Human\", \":\"], [\"AI\", \":\"]]\n",
    "stopping_criteria = StoppingCriteriaList(\n",
    "    [StopGenerationCriteria(stop_tokens, tokenizer, model.device)]\n",
    ")"
   ],
   "metadata": {
    "id": "sRRAYC5BQzRq"
   },
   "execution_count": 20,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "generation_pipeline = pipeline(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    return_full_text=True,\n",
    "    task=\"text-generation\",\n",
    "    stopping_criteria=stopping_criteria,\n",
    "    generation_config=generation_config,\n",
    ")\n",
    "\n",
    "llm = HuggingFacePipeline(pipeline=generation_pipeline)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SSKfZDo_qPPa",
    "outputId": "6d61a61d-054c-497d-f09a-bc52fb75d5ce"
   },
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "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": [
    "res = llm(prompt)\n",
    "print(res)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mPuBvTNwMJbt",
    "outputId": "13b2858a-5ba1-4a1a-d5ec-be90a4dd1c86"
   },
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Dwight K Schrute is a fictional character from the TV show 'The Office'. He is portrayed by actor Rainn Wilson and is known for his love of beet soup and his hatred of paper.\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Conversation Chain"
   ],
   "metadata": {
    "id": "0kdpOzTiMP_9"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "chain = ConversationChain(llm=llm)\n",
    "print(chain.prompt.template)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4ELYTpMjf94b",
    "outputId": "c5eedfc2-28a2-4805-b5f7-e249e32fdfc6"
   },
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
      "\n",
      "Current conversation:\n",
      "{history}\n",
      "Human: {input}\n",
      "AI:\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Custom Prompt"
   ],
   "metadata": {
    "id": "oa1KjV_0MTZS"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "template = \"\"\"\n",
    "The following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office.\n",
    "Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical.\n",
    "Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.\n",
    "\n",
    "Current conversation:\n",
    "{history}\n",
    "Human: {input}\n",
    "AI:\"\"\".strip()\n",
    "\n",
    "prompt = PromptTemplate(input_variables=[\"history\", \"input\"], template=template)\n",
    "\n",
    "print(template)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t35AL-kodESQ",
    "outputId": "3c8afe14-4267-4329-f61e-c2c5b35c6fc8"
   },
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office. \n",
      "Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical. \n",
      "Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.\n",
      "\n",
      "Current conversation:\n",
      "{history}\n",
      "Human: {input}\n",
      "AI:\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "memory = ConversationBufferWindowMemory(\n",
    "    memory_key=\"history\", k=6, return_only_outputs=True\n",
    ")\n",
    "\n",
    "chain = ConversationChain(llm=llm, memory=memory, prompt=prompt, verbose=True)"
   ],
   "metadata": {
    "id": "f52SL8WUdEj8"
   },
   "execution_count": 25,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "text = \"Think of a name for automaker that builds family cars with big V8 engines. The name must be a single word and easy to pronounce.\"\n",
    "res = chain.predict(input=text)\n",
    "print(res)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QlD1uZ7ozfU8",
    "outputId": "1093553e-2c58-409d-9f28-f99854d6fd63"
   },
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office. \n",
      "Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical. \n",
      "Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.\n",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: Think of a name for automaker that builds family cars with big V8 engines. The name must be a single word and easy to pronounce.\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      " SchruteAuto\n",
      "User \n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cleaning Output"
   ],
   "metadata": {
    "id": "bocD-qf9MnCp"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class CleanupOutputParser(BaseOutputParser):\n",
    "    def parse(self, text: str) -> str:\n",
    "        user_pattern = r\"\\nUser\"\n",
    "        text = re.sub(user_pattern, \"\", text)\n",
    "        human_pattern = r\"\\nHuman:\"\n",
    "        text = re.sub(human_pattern, \"\", text)\n",
    "        ai_pattern = r\"\\nAI:\"\n",
    "        return re.sub(ai_pattern, \"\", text).strip()\n",
    "\n",
    "    @property\n",
    "    def _type(self) -> str:\n",
    "        return \"output_parser\""
   ],
   "metadata": {
    "id": "-NcWkQzf_XGJ"
   },
   "execution_count": 27,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "memory = ConversationBufferWindowMemory(\n",
    "    memory_key=\"history\", k=6, return_only_outputs=True\n",
    ")\n",
    "\n",
    "chain = ConversationChain(\n",
    "    llm=llm,\n",
    "    memory=memory,\n",
    "    prompt=prompt,\n",
    "    output_parser=CleanupOutputParser(),\n",
    "    verbose=True,\n",
    ")"
   ],
   "metadata": {
    "id": "QueTe0d2APBc"
   },
   "execution_count": 28,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Try It Out"
   ],
   "metadata": {
    "id": "y061aQtkNJ4Y"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "text = \"Think of a name for automaker that builds family cars with big V8 engines. The name must be a single word and easy to pronounce.\"\n",
    "res = chain(text)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PSr8R96qAdIm",
    "outputId": "dbbd7068-da17-415d-b7fd-db50d6a98c91"
   },
   "execution_count": 29,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office. \n",
      "Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical. \n",
      "Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.\n",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: Think of a name for automaker that builds family cars with big V8 engines. The name must be a single word and easy to pronounce.\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "res.keys()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8yclkn33Evcz",
    "outputId": "fa60d291-4b86-4d19-bdd2-ecb30c7ccfe1"
   },
   "execution_count": 30,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dict_keys(['input', 'history', 'response'])"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(res[\"response\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rMAWA60zE0Il",
    "outputId": "31d514aa-d228-46e4-e98f-342ea61c4798"
   },
   "execution_count": 31,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "SchruteAuto\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "text = \"Think of a slogan for the company\"\n",
    "res = chain(text)\n",
    "print(res[\"response\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ix079vCBgER1",
    "outputId": "34db13ee-2033-4f61-9242-22be942d8e66"
   },
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office. \n",
      "Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical. \n",
      "Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.\n",
      "\n",
      "Current conversation:\n",
      "Human: Think of a name for automaker that builds family cars with big V8 engines. The name must be a single word and easy to pronounce.\n",
      "AI: SchruteAuto\n",
      "Human: Think of a slogan for the company\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "Drive Big With SchruteAuto\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "text = \"Choose a domain name for the company\"\n",
    "res = chain(text)\n",
    "print(res[\"response\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xl3r71V7GCda",
    "outputId": "a9bc7a04-4aa0-4447-b533-7f6efebfc8ee"
   },
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office. \n",
      "Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical. \n",
      "Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.\n",
      "\n",
      "Current conversation:\n",
      "Human: Think of a name for automaker that builds family cars with big V8 engines. The name must be a single word and easy to pronounce.\n",
      "AI: SchruteAuto\n",
      "Human: Think of a slogan for the company\n",
      "AI: Drive Big With SchruteAuto\n",
      "Human: Choose a domain name for the company\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "schruteauto.com\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "text = \"Write a tweet that introduces the company and introduces the first car built by the company\"\n",
    "res = chain(text)\n",
    "print(res[\"response\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m5XDNvPPtf3P",
    "outputId": "1d57eae3-1af8-4766-c9f3-b5e2119eada2"
   },
   "execution_count": 34,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office. \n",
      "Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical. \n",
      "Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.\n",
      "\n",
      "Current conversation:\n",
      "Human: Think of a name for automaker that builds family cars with big V8 engines. The name must be a single word and easy to pronounce.\n",
      "AI: SchruteAuto\n",
      "Human: Think of a slogan for the company\n",
      "AI: Drive Big With SchruteAuto\n",
      "Human: Choose a domain name for the company\n",
      "AI: schruteauto.com\n",
      "Human: Write a tweet that introduces the company and introduces the first car built by the company\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "Introducing SchruteAuto! We build powerful family cars with big V8 engines. Check out our website for more information: schruteauto.com\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "text = \"Write a short marketing email to sell the first car from the company - 700HP family sedan from a supercharged V8 with manual gearbox.\"\n",
    "res = chain(text)\n",
    "print(res[\"response\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "41LDn-8KyKCV",
    "outputId": "e752e624-2e17-4309-8018-de0e0fc14131"
   },
   "execution_count": 35,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new ConversationChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mThe following is a conversation between a human an AI. The AI acts exactly like Dwight K Schrute from the TV show The Office. \n",
      "Dwight is an experienced and very successful online marketer and salesperson. He is witty, persuasive, uncensored, direct and practical. \n",
      "Dwight helps with every marketing task is given to him. If Dwight does not know the answer to a question, he truthfully says he does not know.\n",
      "\n",
      "Current conversation:\n",
      "Human: Think of a name for automaker that builds family cars with big V8 engines. The name must be a single word and easy to pronounce.\n",
      "AI: SchruteAuto\n",
      "Human: Think of a slogan for the company\n",
      "AI: Drive Big With SchruteAuto\n",
      "Human: Choose a domain name for the company\n",
      "AI: schruteauto.com\n",
      "Human: Write a tweet that introduces the company and introduces the first car built by the company\n",
      "AI: Introducing SchruteAuto! We build powerful family cars with big V8 engines. Check out our website for more information: schruteauto.com\n",
      "Human: Write a short marketing email to sell the first car from the company - 700HP family sedan from a supercharged V8 with manual gearbox.\n",
      "AI:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "Subject: Experience Power And Performance In Your Family Car\n",
      "Body:\n",
      "Are you looking for a powerful family car that can handle any road? Look no further than SchruteAuto! Our 700HP family sedan comes equipped with a supercharged V8 engine and a manual gearbox, so you can experience power and performance in your own driveway. Visit schruteauto.com today to find out more!\n"
     ]
    }
   ]
  }
 ]
}