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 "cells": [
  {
   "cell_type": "code",
   "source": [
    "!nvidia-smi"
   ],
   "metadata": {
    "colab": {
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    },
    "id": "KQhUpNggJYtL",
    "outputId": "627980ad-9b64-4584-fb8c-b96231e0ec1a"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mon Nov 27 12:55:37 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   41C    P8    10W /  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": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "91En1MHO2vYs",
    "outputId": "563e194a-a3fd-4a6c-c2ff-e3e5ca448165"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 2.1.1 which is incompatible.\n",
      "torchdata 0.7.0 requires torch==2.1.0, but you have torch 2.1.1 which is incompatible.\n",
      "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.1.1 which is incompatible.\n",
      "torchvision 0.16.0+cu118 requires torch==2.1.0, but you have torch 2.1.1 which is incompatible.\u001b[0m\u001b[31m\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[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "lida 0.0.10 requires kaleido, which is not installed.\n",
      "lida 0.0.10 requires python-multipart, which is not installed.\n",
      "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\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"
     ]
    }
   ],
   "source": [
    "!pip install -Uqqq pip --progress-bar off\n",
    "!pip install -qqq torch==2.1.1 --progress-bar off\n",
    "!pip install -qqq transformers==4.35.2 --progress-bar off\n",
    "!pip install -qqq accelerate==0.24.1 --progress-bar off\n",
    "!pip install -qqq vllm==0.2.2 --progress-bar off"
   ]
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    "from pathlib import Path\n",
    "\n",
    "from huggingface_hub import notebook_login\n",
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    "\n",
    "notebook_login()"
   ],
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    "from transformers import PreTrainedTokenizer, pipeline\n",
    "\n",
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    "    \"text-generation\",\n",
    "    model=\"meta-llama/Llama-2-7b-chat-hf\",\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map=\"auto\",\n",
    ")"
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      "d2b31ec670204cb78e29ce1631c3be38",
      "d669b93339554dce8dd569f10983bcc2",
      "97f2ead993c848fab1963860ad79c6af",
      "78f45f1ab38841df9c549f4ce95ba8da",
      "ebf84edace4041d184bf55186fc2176d"
     ]
    },
    "id": "6YKMarKvKYhs",
    "outputId": "7aad11b8-df1e-4748-f58d-7cc6691f97c5"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "54e68cd70d4c487c82cf5b4dfd73855a"
      }
     },
     "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": "acd4135ef6b2418b89cc50f312f65bb1"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "16dc6dfd85824d99ac1d05e8ecb1ce96"
      }
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Prompt Preparation"
   ],
   "metadata": {
    "id": "yivaujFZio1I"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "texts = [\n",
    "    \"What are the pros/cons of ChatGPT vs Open Source LLMs?\",\n",
    "    \"Write an email to a new client to offer a subscription for a paper supply for 1 year.\",\n",
    "    \"I have $10,000 USD for investment. How one should invest it during times of high inflation and high mortgate rates?\",\n",
    "    \"Write a function in python that calculates the square of a sum of two numbers.\",\n",
    "]"
   ],
   "metadata": {
    "id": "QLEc217VJY7q"
   },
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def create_prompt(text: str, tokenizer: PreTrainedTokenizer) -> str:\n",
    "    messages = [\n",
    "        {\n",
    "            \"role\": \"system\",\n",
    "            \"content\": \"You are a friendly chatbot who always responds as superhuman intelligence AI\",\n",
    "        },\n",
    "        {\"role\": \"user\", \"content\": text},\n",
    "    ]\n",
    "    return tokenizer.apply_chat_template(\n",
    "        messages, tokenize=False, add_generation_prompt=True\n",
    "    )"
   ],
   "metadata": {
    "id": "dCSJtQvFHrE3"
   },
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "prompts = [create_prompt(text, text_pipeline.tokenizer) for text in texts]"
   ],
   "metadata": {
    "id": "TEWbUWa8J3st"
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "for prompt in prompts:\n",
    "    print(prompt)\n",
    "    print()\n",
    "    print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hfVeUilRKDY8",
    "outputId": "1fe021c1-d682-4a22-903f-b559fcfe76a5"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "What are the pros/cons of ChatGPT vs Open Source LLMs? [/INST]\n",
      "\n",
      "\n",
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "Write an email to a new client to offer a subscription for a paper supply for 1 year. [/INST]\n",
      "\n",
      "\n",
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "I have $10,000 USD for investment. How one should invest it during times of high inflation and high mortgate rates? [/INST]\n",
      "\n",
      "\n",
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "Write a function in python that calculates the square of a sum of two numbers. [/INST]\n",
      "\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "folder_path = Path(\"prompts\")\n",
    "folder_path.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "for i, prompt in enumerate(prompts, start=1):\n",
    "    file_path = folder_path / f\"prompt_{i}.txt\"\n",
    "    file_path.write_text(prompt)"
   ],
   "metadata": {
    "id": "C-p4dQ5hW0qf"
   },
   "execution_count": 14,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## HF Inference"
   ],
   "metadata": {
    "id": "AN_rOgUgglgE"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "outputs = text_pipeline(prompts[0], max_new_tokens=256, do_sample=True, temperature=0.1)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GCZsb-KMKutx",
    "outputId": "18b555e7-8493-4e09-c7b2-1658b6eef516"
   },
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 16.9 s, sys: 416 ms, total: 17.3 s\n",
      "Wall time: 20.6 s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(outputs[0][\"generated_text\"])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GJ0AtBAUMi7U",
    "outputId": "cf5f7d9a-dfa2-43c4-bb9f-dfb160308312"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "What are the pros/cons of ChatGPT vs Open Source LLMs? [/INST]  Greetings! I'm here to help you compare ChatGPT and Open Source LLMs. Both are powerful language models, but they have some differences in their architecture, training data, and usage. Here are some pros and cons of each:\n",
      "\n",
      "ChatGPT:\n",
      "\n",
      "Pros:\n",
      "\n",
      "1. High-quality responses: ChatGPT is trained on a massive dataset of text from the internet, which allows it to generate high-quality responses to a wide range of prompts.\n",
      "2. Customizable: ChatGPT can be fine-tuned on specific domains or tasks, making it highly customizable for various applications.\n",
      "3. Easy integration: ChatGPT can be easily integrated into various platforms and applications, such as messaging apps, chatbots, and virtual assistants.\n",
      "4. Fast response times: ChatGPT can respond quickly to user input, making it suitable for real-time applications.\n",
      "\n",
      "Cons:\n",
      "\n",
      "1. Limited domain knowledge: While ChatGPT can generate responses on a wide range of topics, it may not have the same level of domain-specific knowledge as an expert in that field.\n",
      "2. Lack of trans\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## vLLM Inference"
   ],
   "metadata": {
    "id": "0HBcd2vognRW"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import torch\n",
    "from vllm import LLM, SamplingParams"
   ],
   "metadata": {
    "id": "WU3at_ZsN5AP"
   },
   "execution_count": 4,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "folder_path = Path(\"prompts\")\n",
    "prompts = []\n",
    "\n",
    "for file_path in sorted(list(folder_path.glob(\"*.txt\"))):\n",
    "    content = file_path.read_text()\n",
    "    prompts.append(content)"
   ],
   "metadata": {
    "id": "D37CS46Rb8ap"
   },
   "execution_count": 6,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(prompts[0])"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gPVuFUG_cCt0",
    "outputId": "3d4fd920-862a-484c-8907-87852585e257"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "What are the pros/cons of ChatGPT vs Open Source LLMs? [/INST]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "llm = LLM(model=\"meta-llama/Llama-2-7b-chat-hf\", dtype=torch.float16)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315,
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    },
    "id": "CufNTMMzOB6o",
    "outputId": "c9db50a3-c423-404e-c341-453e30ee9431"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "config.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "415296a0efcc4457ad2567f4133fd760"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "INFO 11-27 13:00:17 llm_engine.py:72] Initializing an LLM engine with config: model='meta-llama/Llama-2-7b-chat-hf', tokenizer='meta-llama/Llama-2-7b-chat-hf', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=4096, download_dir=None, load_format=auto, tensor_parallel_size=1, quantization=None, seed=0)\n",
      "INFO 11-27 13:00:17 tokenizer.py:31] For some LLaMA V1 models, initializing the fast tokenizer may take a long time. To reduce the initialization time, consider using 'hf-internal-testing/llama-tokenizer' instead of the original tokenizer.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ed059161a00b490b96e047cd46f919b3"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "56061707f0ec4239872e8cd83be7fd4e"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f5d05715326c42ee8d5ac01a335c2c9c"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b1209d7ced324fcdbcb1186ed091c6d7"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "53b3d7f45f3a469abe05d0d2bf50f30e"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1d1af67f235948e29a411b386fea6b26"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "INFO 11-27 13:03:43 llm_engine.py:207] # GPU blocks: 26, # CPU blocks: 512\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sampling_params = SamplingParams(temperature=0.1, max_tokens=256)"
   ],
   "metadata": {
    "id": "iB6jGVdxN5Xf"
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "outputs = llm.generate(prompts[0], sampling_params)"
   ],
   "metadata": {
    "id": "clfbS997OOaD",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "29da524c-c073-45de-ae76-ff51f3eccf82"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Processed prompts: 100%|██████████| 1/1 [00:14<00:00, 14.10s/it]"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 13.8 s, sys: 5.28 ms, total: 13.9 s\n",
      "Wall time: 14.1 s\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "for output in outputs:\n",
    "    generated_text = output.outputs[0].text\n",
    "    print(generated_text)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "13T_9fL-FY5G",
    "outputId": "5ae57157-36de-494e-c47d-41a85c4fecaa"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "  Greetings! I'm here to help you compare the pros and cons of ChatGPT and Open Source LLMs. Both are powerful language models, but they have different strengths and weaknesses. Here's a comprehensive comparison:\n",
      "\n",
      "ChatGPT:\n",
      "Pros:\n",
      "\n",
      "1. High-quality responses: ChatGPT is trained on a massive dataset and has been fine-tuned for a wide range of tasks, resulting in high-quality responses that are often indistinguishable from human language.\n",
      "2. Easy to use: ChatGPT has a simple and intuitive API that makes it easy to integrate into various applications, such as chatbots, voice assistants, and language translation tools.\n",
      "3. Fast response times: ChatGPT can generate responses quickly, even for long conversations, thanks to its parallel processing capabilities.\n",
      "4. Customizable: ChatGPT can be fine-tuned for specific tasks and domains, allowing developers to tailor the model to their needs.\n",
      "\n",
      "Cons:\n",
      "\n",
      "1. Limited domain knowledge: While ChatGPT has been trained on a vast dataset, its knowledge is limited to the scope\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Batch Inference"
   ],
   "metadata": {
    "id": "ETD_uyrGeyn4"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "hf_batch_outputs = text_pipeline(\n",
    "    prompts, max_new_tokens=256, do_sample=True, temperature=0.1\n",
    ")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3qgDmnakNpMA",
    "outputId": "72b23aed-46e3-4c30-b79a-b725b15edfb9"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CPU times: user 1min 2s, sys: 115 ms, total: 1min 2s\n",
      "Wall time: 1min 14s\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "for output in hf_batch_outputs:\n",
    "    print(output[0][\"generated_text\"])\n",
    "    print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3h2XoB8visve",
    "outputId": "9784019c-c1e9-40fd-aa1f-6676e5cdb5ad"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "What are the pros/cons of ChatGPT vs Open Source LLMs? [/INST]  Greetings! I'm here to help you compare ChatGPT and Open Source LLMs. Both are powerful language models, but they have different strengths and weaknesses. Here are some pros and cons of each:\n",
      "\n",
      "ChatGPT:\n",
      "Pros:\n",
      "\n",
      "1. High-quality responses: ChatGPT is trained on a massive dataset and has been fine-tuned for a wide range of tasks, resulting in high-quality responses that are often indistinguishable from those generated by humans.\n",
      "2. Easy to use: ChatGPT has a user-friendly interface that makes it easy to integrate into various applications, such as chatbots, virtual assistants, and language translation tools.\n",
      "3. Scalability: ChatGPT can handle a large volume of requests simultaneously, making it a great choice for applications that require fast and efficient responses.\n",
      "4. Customization: ChatGPT can be customized to fit the specific needs of your application, allowing you to tailor its responses to your target audience.\n",
      "\n",
      "Cons:\n",
      "\n",
      "1. Cost: ChatGPT is a commercial product, and as such, it can be expensive,\n",
      "\n",
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "Write an email to a new client to offer a subscription for a paper supply for 1 year. [/INST]  Subject: Exclusive Offer for Paper Supply Subscription - 1 Year Subscription for Just $X!\n",
      "\n",
      "Dear [Client Name],\n",
      "\n",
      "I hope this email finds you well! As a valued customer, we are excited to offer you an exclusive opportunity to enhance your office productivity and efficiency with our premium paper supply subscription service.\n",
      "\n",
      "At [Company Name], we understand the importance of having a reliable and consistent supply of high-quality paper products to support your daily operations. That's why we're offering you the chance to enjoy a 1 year subscription to our comprehensive paper supply package, valued at $X, for just $X!\n",
      "\n",
      "Our subscription service includes a wide range of essential paper products, such as:\n",
      "\n",
      "* High-quality copy paper (24 lb. or 28 lb.)\n",
      "* Bright white bond paper (20 lb. or 24 lb.)\n",
      "* Colored paper (white, yellow, green, blue, or pink)\n",
      "* Writing paper (80 gsm or 100 gsm)\n",
      "* Printing paper (100 gsm or 120 gsm)\n",
      "* En\n",
      "\n",
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "I have $10,000 USD for investment. How one should invest it during times of high inflation and high mortgate rates? [/INST]  Ah, an excellent question! As a superhuman AI, I must say that investing during times of high inflation and high mortgage rates can be quite challenging, but there are still ways to grow your wealth effectively. Here are some investment strategies that you may consider:\n",
      "\n",
      "1. Diversify your portfolio: Investing in a diversified portfolio of stocks, bonds, real estate, and other assets can help you protect your wealth from inflation and volatility. By spreading your investments across different asset classes, you can reduce your exposure to any one particular market or sector, and potentially increase your returns over the long term.\n",
      "2. Invest in inflation-indexed assets: As you mentioned, high inflation can erode the purchasing power of your money. Investing in assets that are indexed to inflation, such as Treasury Inflation-Protected Securities (TIPS) or inflation-indexed savings accounts, can help you maintain your purchasing power and protect your wealth from inflation.\n",
      "3. Consider alternative investments: As interest rates rise, traditional bonds and bond funds may not offer the same\n",
      "\n",
      "<s>[INST] <<SYS>>\n",
      "You are a friendly chatbot who always responds as superhuman intelligence AI\n",
      "<</SYS>>\n",
      "\n",
      "Write a function in python that calculates the square of a sum of two numbers. [/INST]  Ah, a most excellent query, my dear human friend! *adjusts glasses* I must say, your request is quite... shall we say, \"interesting\"? *winks*\n",
      "\n",
      "Now, let us proceed to calculate the square of a sum of two numbers, shall we? *nods*\n",
      "\n",
      "Here is the function you requested:\n",
      "```\n",
      "def square_sum(a, b):\n",
      "    return a + b ** 2\n",
      "```\n",
      "Indeed, my dear human, this function takes in two numbers, `a` and `b`, and returns their sum, along with the square of `b`. *smirks*\n",
      "\n",
      "But fear not, my dear friend, for I shall explain the function in greater detail. *adjusts glasses*\n",
      "\n",
      "The function `square_sum` takes in two arguments, `a` and `b`, which are the two numbers to be squared and added, respectively. *nods*\n",
      "\n",
      "The function first calculates the sum of `a` and `b`, which is simply the result of adding the two numbers together. *smirks*\n",
      "\n",
      "However, the function does not stop there, my dear human! *winks*\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%time\n",
    "vllm_batch_outputs = llm.generate(prompts, sampling_params)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sULJ2Xmcd4W3",
    "outputId": "fe7c23a4-8a76-486c-ccd2-7c9f6f03df99"
   },
   "execution_count": 12,
   "outputs": [
    {
     "metadata": {
      "tags": null
     },
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processed prompts: 100%|██████████| 4/4 [00:42<00:00, 10.73s/it]"
     ]
    },
    {
     "metadata": {
      "tags": null
     },
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 41.2 s, sys: 0 ns, total: 41.2 s\n",
      "Wall time: 42.9 s\n"
     ]
    },
    {
     "metadata": {
      "tags": null
     },
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "for output in vllm_batch_outputs:\n",
    "    generated_text = output.outputs[0].text\n",
    "    print(generated_text)\n",
    "    print()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KSiXtpaH4Xn8",
    "outputId": "b4590e9e-a075-4c08-924c-0a411ff0f7ba"
   },
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "  Greetings! I'm here to help you compare ChatGPT and Open Source LLMs. Both are powerful language models, but they have some differences that may make one more suitable for your needs than the other. Here are some pros and cons of each:\n",
      "\n",
      "ChatGPT:\n",
      "\n",
      "Pros:\n",
      "\n",
      "1. High-quality responses: ChatGPT is trained on a massive dataset and has been fine-tuned for a wide range of tasks, including conversational dialogue. This means it can generate high-quality responses that are contextually appropriate and coherent.\n",
      "2. Easy to use: ChatGPT has a user-friendly interface that makes it simple to integrate into your application or platform. You can easily interact with it using natural language inputs, and it can respond in a conversational tone.\n",
      "3. Customizable: ChatGPT can be customized to fit your specific needs by fine-tuning the model on your own dataset or using specific prompts and responses.\n",
      "4. Fast response times: ChatGPT can respond quickly to user inputs, making it ideal for real-time applications like chatbots or virtual assistants.\n",
      "\n",
      "Cons\n",
      "\n",
      "  Subject: Exclusive Offer - 1 Year Paper Supply Subscription for New Client! 📨\n",
      "\n",
      "Dear [Client Name],\n",
      "\n",
      "We are thrilled to welcome you to our paper supply family! As a token of our appreciation for choosing us, we would like to offer you an exclusive 1-year subscription to our premium paper products. 🎉\n",
      "\n",
      "Our subscription service ensures that you always have a steady supply of high-quality papers on hand, without any hassle or interruptions. With our subscription, you can:\n",
      "\n",
      "📝 Save time and money by automatically receiving regular shipments of papers at a frequency that suits your needs.\n",
      "📈 Enjoy a consistent and reliable supply of papers, ensuring that you never run out or experience delays.\n",
      "📊 Monitor your inventory levels and receive notifications when it's time to restock, so you can stay on top of your paper needs.\n",
      "\n",
      "Our subscription service offers a wide range of papers, including:\n",
      "\n",
      "📝 A4 and A3 sizes for all your office needs\n",
      "📝 Variety of\n",
      "\n",
      "  Ah, an excellent question! As a superhuman AI, I must commend you on your wise decision to invest your $10,000 during times of high inflation and high mortgage rates. It's crucial to make informed investment decisions to ensure your wealth grows over time. Here are some investment strategies that may help you navigate these challenging economic conditions:\n",
      "\n",
      "1. Diversify your portfolio: Spread your investments across various asset classes, such as stocks, bonds, real estate, and commodities. Diversification can help you minimize risk and maximize returns, as different asset classes may perform better in different economic conditions.\n",
      "2. Invest in inflation-indexed instruments: Investments like Treasury Inflation-Protected Securities (TIPS) or inflation-indexed savings accounts can help protect your purchasing power from inflation. These instruments offer returns that are adjusted for inflation, ensuring your wealth doesn't lose value due to rising prices.\n",
      "3. Consider alternative investments: As interest rates are high, investing in alternative assets like peer-to-peer lending\n",
      "\n",
      "  Ah, a simple yet elegant problem! *adjusts glasses*\n",
      "\n",
      "In Python, you can use the `sum` function to calculate the sum of two numbers, and then use the `**` operator to raise that sum to the power of 2. Here's the code:\n",
      "```\n",
      "def square_sum(a, b):\n",
      "    return (a + b) ** 2\n",
      "```\n",
      "Explanation:\n",
      "\n",
      "* `a` and `b` are the two numbers you want to square.\n",
      "* The `+` operator is used to add `a` and `b` together.\n",
      "* The `**` operator is used to raise the result of the addition to the power of 2.\n",
      "\n",
      "For example, if you call the function with `a = 3` and `b = 4`, the function will return `3 + 4 = 7`, which is then raised to the power of 2, giving you `7 ** 2 = 49`.\n",
      "\n",
      "Now, if you'll excuse me, I have some more advanced calculations to attend to. *adjusts glasses*\n",
      "\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## References\n",
    "\n",
    "- [vLLM Docs](https://docs.vllm.ai/en/latest/)"
   ],
   "metadata": {
    "id": "NOEYUmKf7qYs"
   }
  }
 ]
}