{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IizSuhnp5BT8",
    "outputId": "17410ebd-6fbf-41e4-932e-f693852216a6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fri Jun  9 12:11:30 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   40C    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",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cz2s7Tvc5ZOw",
    "outputId": "a7443e19-fe93-4089-8297-b1b2aa8ba7f0"
   },
   "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==4.30.0 --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",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZS1dm5b87ege",
    "outputId": "559a5334-32dd-4056-da0e-4071e6a7d09a"
   },
   "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('http'), PosixPath('//172.28.0.1'), PosixPath('8013')}\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('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-hm-2px1uzz1i5pwt --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\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.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"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from peft import PeftConfig, PeftModel\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "jGD9UatSQfss"
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"tiiuae/falcon-7b\", padding_side=\"left\")\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "0nBRYHw5Q2rc"
   },
   "outputs": [],
   "source": [
    "DEVICE = \"cuda:0\"\n",
    "\n",
    "prompt = f\"\"\"\n",
    "<human>: What is your return policy?\n",
    "<assistant>:\n",
    "\"\"\".strip()\n",
    "\n",
    "encoding = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gi2gD56f7cxF"
   },
   "source": [
    "## With training config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "6JAKrFTi3EfR"
   },
   "outputs": [],
   "source": [
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_use_double_quant=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "id": "_kaZUkcj3wuz",
    "outputId": "dbc391ef-fa65-40ac-9b80-ed1c37ffb26f"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {}
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     "metadata": {}
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    {
     "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",
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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"
     ]
    },
    {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "aae9adced4e74533be65acb9d639320e"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "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",
    "    quantization_config=bnb_config,\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "\n",
    "model = PeftModel.from_pretrained(model, MODEL_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "rEbZlJkgwfCq"
   },
   "outputs": [],
   "source": [
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 20\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jC6yPbaTyMPd",
    "outputId": "56fd32b5-a940-4148-ca8c-cceecaa214fe"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "13 s ± 222 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 5\n",
    "\n",
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "        do_sample=False,\n",
    "        use_cache=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y8IQwCun4Wxs",
    "outputId": "86c42c01-1277-4e21-9d58-730bdc2fbc1d"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: What is your return policy?\n",
      "<assistant>: Our return policy allows you to return eligible items within 30 days of purchase for a full refund.\n"
     ]
    }
   ],
   "source": [
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "        do_sample=False,\n",
    "        use_cache=True,\n",
    "    )\n",
    "\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QgWWvkbi7Zir"
   },
   "source": [
    "## 4 bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "zAaPWRrL5FJQ",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "48472492eebc4f65bd966fd960b9955f",
      "dc4dba81fbbb41258332761de41fbb69",
      "559ee75530ee412987444245b7f3a51a",
      "c33ac93aeb594efd8070f69e7c6fe6e6",
      "59f0d19a434e4af7a3428a1afb05d60d",
      "5ef8ea1af45c4a089c58d79a5144ce87",
      "b43306720fb84ee5a8a1f1a8db13e32a",
      "7e9fb925a15d4da38a9d77284cb01405",
      "08079c2d90c443dba9ce3efcf683f1b9",
      "381a2d54f3574086b35ea7e68606a111",
      "de361e2883144f70b413e9fe1a48682e"
     ]
    },
    "outputId": "50918943-a75c-4482-899d-98b25e276983"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "48472492eebc4f65bd966fd960b9955f"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "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",
    "    trust_remote_code=True,\n",
    "    load_in_4bit=True,\n",
    ")\n",
    "\n",
    "model = PeftModel.from_pretrained(model, MODEL_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "NneRGtql63eH"
   },
   "outputs": [],
   "source": [
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 20\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MIN9J3aH68KW",
    "outputId": "efd2348d-9e33-4526-bee1-b9f4b9ff89f5"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "9.18 s ± 47.8 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 5\n",
    "\n",
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "        do_sample=False,\n",
    "        use_cache=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z5FAUu6B4QXo",
    "outputId": "87571640-9448-4073-e941-bac8ad6da422"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: What is your return policy?\n",
      "<assistant>: Our return policy allows you to return most items within 30 days of purchase. Please see our Returns\n"
     ]
    }
   ],
   "source": [
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "        do_sample=False,\n",
    "        use_cache=True,\n",
    "    )\n",
    "\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SvIfEDpsAIF-"
   },
   "source": [
    "## 8 bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "71ad1bb425ba479990fe2254d4419322",
      "2b9fdbf4969744ec914ac11cb965d6fb",
      "71a9ef932797427bb5715a4a2d079ddd",
      "1acea2e0ba9546a1899f4028edcf7d99",
      "a0f4dcbe099f48549919d6f6b7b6558f",
      "efa53ee2ab31433d8e335e2e7eac4dd1",
      "bec643d0adf64302920ce4e505fed25f",
      "e27b0fa4b6604593bf4c8692168568df",
      "c1637ca8f5d448088c6c1934e864421d",
      "4636b2ed281b40ffb97ac4d93b0f1ea4",
      "06324f5a857849e5bb0d439ba0f5185d"
     ]
    },
    "id": "NdeZ2NuW5f9x",
    "outputId": "bf4fe843-249b-45d6-f731-11e8943c6448"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "71ad1bb425ba479990fe2254d4419322"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "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",
    "    trust_remote_code=True,\n",
    "    load_in_8bit=True,\n",
    ")\n",
    "\n",
    "model = PeftModel.from_pretrained(model, MODEL_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "YwXTjiJR1X-H"
   },
   "outputs": [],
   "source": [
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 20\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "4nD08NwO6NZ6",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "9faa3d77-7b0d-4852-8c77-a6622381e8f1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "3.12 s ± 61.4 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 5\n",
    "\n",
    "outputs = model.generate(\n",
    "    input_ids=encoding.input_ids,\n",
    "    attention_mask=encoding.attention_mask,\n",
    "    generation_config=generation_config,\n",
    "    do_sample=False,\n",
    "    use_cache=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Nqdo9Zm5Tkh",
    "outputId": "d30821b0-4387-4a07-9092-58ae15098393"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: What is your return policy?\n",
      "<assistant>: Our return policy allows you to return most items within 30 days of purchase. Please refer to our\n"
     ]
    }
   ],
   "source": [
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "        do_sample=False,\n",
    "        use_cache=True,\n",
    "    )\n",
    "\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B7ZoLf4c6Twy"
   },
   "source": [
    "## Compile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "zbBhCC1Y2_mQ"
   },
   "outputs": [],
   "source": [
    "model = torch.compile(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ip2cJ6yW6bxS",
    "outputId": "3352be67-2612-452b-9aa5-16c773fe14a6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "3.23 s ± 162 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 5\n",
    "\n",
    "outputs = model.generate(\n",
    "    input_ids=encoding.input_ids,\n",
    "    attention_mask=encoding.attention_mask,\n",
    "    generation_config=generation_config,\n",
    "    do_sample=False,\n",
    "    use_cache=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "aQImIXa17N0F",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "2bdcedfc-5524-4350-b92b-ae5ba077b4ca"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: What is your return policy?\n",
      "<assistant>: Our return policy allows you to return most items within 30 days of purchase. Please refer to our\n"
     ]
    }
   ],
   "source": [
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "        do_sample=False,\n",
    "        use_cache=True,\n",
    "    )\n",
    "\n",
    "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4vHPPJ_aCymE"
   },
   "source": [
    "## Batched Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "8CtdKvUB7CmL"
   },
   "outputs": [],
   "source": [
    "DEVICE = \"cuda:0\"\n",
    "\n",
    "prompts = [\n",
    "    f\"\"\"\n",
    "<human>: What is your return policy?\n",
    "<assistant>:\n",
    "\"\"\".strip(),\n",
    "    f\"\"\"\n",
    "<human>: How can I create an account?\n",
    "<assistant>:\n",
    "\"\"\".strip(),\n",
    "    f\"\"\"\n",
    "<human>: What payment methods do you accept?\n",
    "<assistant>:\n",
    "\"\"\".strip(),\n",
    "    f\"\"\"\n",
    "<human>: What happens when I return a clearance item?\n",
    "<assistant>:\n",
    "\"\"\".strip(),\n",
    "]\n",
    "\n",
    "encoding = tokenizer(prompts, padding=True, truncation=True, return_tensors=\"pt\").to(\n",
    "    DEVICE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "d63292e4ac9148a69043444cbea90368",
      "dc8dac5d1b064ae19f0a151ccefa19f0",
      "b7412171430a4ce49b33fb60d466a9b1",
      "e26310b87feb4b5d89ae45164fd63c9b",
      "b5d801068765431f95764dfc4cebf87a",
      "79865194bdc04f05afc8732ef60aa65a",
      "214c0248d630445bbb101da800074afe",
      "8e8ceac356dc48e39d5821b27d446168",
      "19c761d1f9a64b26aca8dc241f9cb177",
      "68bcf604eb77493f96c32d21becf0606",
      "47a61e56f8bb4080920ba0bd4b20f409"
     ]
    },
    "id": "Hf69H9OcSqiT",
    "outputId": "c26f16ee-1eff-4cbe-c927-17c3651a7dd9"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d63292e4ac9148a69043444cbea90368"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "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",
    "    trust_remote_code=True,\n",
    "    load_in_8bit=True,\n",
    ")\n",
    "\n",
    "model = PeftModel.from_pretrained(model, MODEL_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "eEYXI50sTXmh"
   },
   "outputs": [],
   "source": [
    "generation_config = model.generation_config\n",
    "generation_config.max_new_tokens = 20\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e-aqiecmSr8_",
    "outputId": "b71b3a15-2f19-45df-e1ba-272a4085104f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "3.15 s ± 73.5 ms per loop (mean ± std. dev. of 5 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -r 5\n",
    "\n",
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "        do_sample=False,\n",
    "        use_cache=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "with torch.inference_mode():\n",
    "    outputs = model.generate(\n",
    "        input_ids=encoding.input_ids,\n",
    "        attention_mask=encoding.attention_mask,\n",
    "        generation_config=generation_config,\n",
    "        do_sample=False,\n",
    "        use_cache=True,\n",
    "    )"
   ],
   "metadata": {
    "id": "eMToJ0NOXdWE"
   },
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l7ObEhaPTqmq",
    "outputId": "8e75b5be-4ee2-4b5e-a1e1-97d8b471e664"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<human>: What is your return policy?\n",
      "<assistant>: Our return policy allows you to return most items within 30 days of purchase for a full refund.\n",
      "<human>: How can I create an account?\n",
      "<assistant>: To create an account, click on the 'Sign Up' button at the top right corner of\n",
      "<human>: What payment methods do you accept?\n",
      "<assistant>: We accept credit card payments through our secure payment gateway. We also accept payments through PayPal and Google Pay\n",
      "<human>: What happens when I return a clearance item?\n",
      "<assistant>: If you return a clearance item, you will receive a refund for the item's original price minus\n"
     ]
    }
   ],
   "source": [
    "for output in outputs:\n",
    "    print(tokenizer.decode(output, skip_special_tokens=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Lit-Parrot"
   ],
   "metadata": {
    "id": "KvRWhbZEIiLl"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!git clone https://github.com/Lightning-AI/lit-parrot\n",
    "%cd lit-parrot/\n",
    "!git checkout a9dbfdf"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lh8ibiesIFki",
    "outputId": "7b91e87e-2149-4cb6-e251-7609566c2203"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Cloning into 'lit-parrot'...\n",
      "remote: Enumerating objects: 1791, done.\u001b[K\n",
      "remote: Counting objects: 100% (640/640), done.\u001b[K\n",
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      "Receiving objects: 100% (1791/1791), 597.32 KiB | 3.09 MiB/s, done.\n",
      "Resolving deltas: 100% (1158/1158), done.\n",
      "/content/lit-parrot\n"
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    "!pip install --index-url https://download.pytorch.org/whl/nightly/cu118 --pre 'torch>=2.1.0dev' -qqq --progress-bar off\n",
    "!pip install huggingface_hub -r requirements.txt -qqq --progress-bar off"
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    "!python scripts/download.py --repo_id tiiuae/falcon-7b\n",
    "!python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/tiiuae/falcon-7b"
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    "!python generate/base.py \\\n",
    "        --prompt \"How can I create an account?\" \\\n",
    "        --checkpoint_dir checkpoints/tiiuae/falcon-7b \\\n",
    "        --quantize llm.int8 \\\n",
    "        --max_new_tokens 20"
   ],
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   },
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     "name": "stdout",
     "text": [
      "Loading model 'checkpoints/tiiuae/falcon-7b/lit_model.pth' with {'block_size': 2048, 'vocab_size': 50254, 'padding_multiple': 512, 'padded_vocab_size': 65024, 'n_layer': 32, 'n_head': 71, 'n_embd': 4544, 'rotary_percentage': 1.0, 'parallel_residual': True, 'bias': False, 'n_query_groups': 1, 'shared_attention_norm': True}\n",
      "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
      "Time to instantiate model: 3.26 seconds.\n",
      "Time to load the model weights: 150.54 seconds.\n",
      "Global seed set to 1234\n",
      "How can I create an account?\n",
      "What is the My Invoices area?\n",
      "What are the subscription plans?\n",
      "How does\n",
      "Time for inference 1: 3.60 sec total, 5.56 tokens/sec\n",
      "Memory used: 9.72 GB\n"
     ]
    }
   ]
  },
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   "source": [
    "## References\n",
    "\n",
    "- [Falcon 7b fine-tuning on custom dataset tutorial](https://www.mlexpert.io/prompt-engineering/fine-tuning-llm-on-custom-dataset-with-qlora)\n",
    "- [Lit-Parrot](https://github.com/Lightning-AI/lit-parrot)"
   ],
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