falcon-guanaco-v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read me section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Saurabh - th July 2023 - QLORA notebook \n",
    "#### saurabhmangal@google.com\n",
    "\n",
    "## V2- New updates on 5&6thth July - made tuning works too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C2EgqEPDQ8v6"
   },
   "source": [
    "## Finetune Falcon-7b on a Google \n",
    "\n",
    "### \n",
    "\n",
    "Welcome to this Google Colab notebook that shows how to fine-tune the recent Falcon-7b model on a single Google colab and turn it into a chatbot\n",
    "\n",
    "We will leverage PEFT library from Hugging Face ecosystem, as well as QLoRA for more memory efficient finetuningb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Even though the notebook code is less its still requires fixing environment variables \n",
    "### Transformer package may fail\n",
    "### hF model links may get updated\n",
    "### mannually may need to install packages, however i have tried to include all pip installs "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i-tTvEF1RT3y"
   },
   "source": [
    "## Setup\n",
    "\n",
    "Run the cells below to setup and install the required libraries. For our experiment we will need `accelerate`, `peft`, `transformers`, `datasets` and TRL to leverage the recent [`SFTTrainer`](https://huggingface.co/docs/trl/main/en/sft_trainer). We will use `bitsandbytes` to [quantize the base model into 4bit](https://huggingface.co/blog/4bit-transformers-bitsandbytes). We will also install `einops` as it is a requirement to load Falcon models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mNnkgBq7Q3EU",
    "outputId": "d7b64bce-31fb-4884-8a95-462792b68acd"
   },
   "outputs": [],
   "source": [
    "# !pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n",
    "# !pip install -q datasets bitsandbytes=0.41.1 einops wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rnqmq7amRrU8"
   },
   "source": [
    "## Dataset\n",
    "\n",
    "For our experiment, we will use the Guanaco dataset, which is a clean subset of the OpenAssistant dataset adapted to train general purpose chatbots.\n",
    "\n",
    "The dataset can be found [here](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)"
   ]
  },
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    "id": "0X3kHnskSWU4",
    "outputId": "c618b983-ac72-4a81-f2cd-307cda489f78"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Repo card metadata block was not found. Setting CardData to empty.\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset_name = \"timdettmers/openassistant-guanaco\"\n",
    "dataset = load_dataset(dataset_name, split=\"train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rjOMoSbGSxx9"
   },
   "source": [
    "## Loading the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AjB0WAqFSzlD"
   },
   "source": [
    "In this section we will load the [Falcon 7B model](https://huggingface.co/tiiuae/falcon-7b), quantize it in 4bit and attach LoRA adapters on it. Let's get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bitsandbytes.cextension import CUDASetup\n",
    "import torch\n",
    "lib = CUDASetup.get_instance().lib\n",
    "# lib.cadam32bit_g32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "\n",
      "===================================BUG REPORT===================================\n",
      "================================================================================\n",
      "The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/share/ca-certificates,/etc/ssl/certs,/etc/ca-certificates/update.d')}\n",
      "The following directories listed in your path were found to be non-existent: {PosixPath('/opt/c2d/post_start.sh')}\n",
      "The following directories listed in your path were found to be non-existent: {PosixPath('/proxy/%PORT%')}\n",
      "The following directories listed in your path were found to be non-existent: {PosixPath('/root/google_vm_config.lock')}\n",
      "The following directories listed in your path were found to be non-existent: {PosixPath('/proxy/%PORT%')}\n",
      "The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//matplotlib_inline.backend_inline')}\n",
      "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
      "DEBUG: Possible options found for libcudart.so: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}\n",
      "CUDA SETUP: PyTorch settings found: CUDA_VERSION=117, Highest Compute Capability: 7.0.\n",
      "CUDA SETUP: To manually override the PyTorch CUDA version please see:https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md\n",
      "CUDA SETUP: Loading binary /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117_nocublaslt.so...\n",
      "CUDA SETUP: Something unexpected happened. Please compile from source:\n",
      "git clone https://github.com/TimDettmers/bitsandbytes.git\n",
      "cd bitsandbytes\n",
      "CUDA_VERSION=117 make cuda11x_nomatmul\n",
      "python setup.py install\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: UserWarning: Welcome to bitsandbytes. For bug reports, please run\n",
      "\n",
      "python -m bitsandbytes\n",
      "\n",
      "\n",
      "  warn(msg)\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: UserWarning: /opt/conda did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: 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 select the PyTorch default libcudart.so, which is {torch.version.cuda},but this might missmatch with the CUDA version that is needed for bitsandbytes.To override this behavior set the BNB_CUDA_VERSION=<version string, e.g. 122> environmental variableFor example, if you want to use the CUDA version 122BNB_CUDA_VERSION=122 python ...OR set the environmental variable in your .bashrc: export BNB_CUDA_VERSION=122In the case of a manual override, make sure you set the LD_LIBRARY_PATH, e.g.export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2\n",
      "  warn(msg)\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: UserWarning: /usr/local/cuda/lib64:/usr/local/nccl2/lib:/usr/local/cuda/extras/CUPTI/lib64 did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
      "  warn(msg)\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: UserWarning: WARNING: Compute capability < 7.5 detected! Only slow 8-bit matmul is supported for your GPU!                     If you run into issues with 8-bit matmul, you can try 4-bit quantization: https://huggingface.co/blog/4bit-transformers-bitsandbytes\n",
      "  warn(msg)\n"
     ]
    }
   ],
   "source": [
    "CUDASetup.get_instance().generate_instructions()\n",
    "CUDASetup.get_instance().print_log_stack()\n"
   ]
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     ]
    },
    "id": "ZwXZbQ2dSwzI",
    "outputId": "b3dd0dbb-b991-47d0-b0ef-b447d22fa747",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d3a701721a4433bbb7960ec4c1069c5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
    "\n",
    "# model_name = \"ybelkada/falcon-7b-sharded-bf16\"\n",
    "model_name = \"tiiuae/falcon-7b\"\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.float16,\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    quantization_config=bnb_config,\n",
    "    trust_remote_code=True\n",
    ")\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xNqIYtQcUBSm"
   },
   "source": [
    "Let's also load the tokenizer below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "fe1e4ce03e374711a749653e0168edb5",
      "4c04d9b6581b468697612fb2cbb4355e",
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      "066d24010aca4fc394b9219affe7d788",
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      "0fee32efd3bc4b138907cbef79650732",
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      "8597cf5e0f9a4fe8a8672165b112e27b",
      "d2e27c2dfea44de39a3130a19aa6a439"
     ]
    },
    "id": "XDS2yYmlUAD6",
    "outputId": "1f5c4180-1881-4484-f9e9-f289dd7c3bc3"
   },
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NuAx3zBeUL1q"
   },
   "source": [
    "Below we will load the configuration file in order to create the LoRA model. According to QLoRA paper, it is important to consider all linear layers in the transformer block for maximum performance. Therefore we will add `dense`, `dense_h_to_4_h` and `dense_4h_to_h` layers in the target modules in addition to the mixed query key value layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "dQdvjTYTT1vQ"
   },
   "outputs": [],
   "source": [
    "from peft import LoraConfig\n",
    "\n",
    "lora_alpha = 16\n",
    "lora_dropout = 0.1\n",
    "lora_r = 64\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=lora_alpha,\n",
    "    lora_dropout=lora_dropout,\n",
    "    r=lora_r,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    "    target_modules=[\n",
    "        \"query_key_value\",\n",
    "        \"dense\",\n",
    "        \"dense_h_to_4h\",\n",
    "        \"dense_4h_to_h\",\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dzsYHLwIZoLm"
   },
   "source": [
    "## Loading the trainer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aTBJVE4PaJwK"
   },
   "source": [
    "Here we will use the [`SFTTrainer` from TRL library](https://huggingface.co/docs/trl/main/en/sft_trainer) that gives a wrapper around transformers `Trainer` to easily fine-tune models on instruction based datasets using PEFT adapters. Let's first load the training arguments below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "OCFTvGW6aspE"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "output_dir = \"./results\"\n",
    "per_device_train_batch_size = 4\n",
    "gradient_accumulation_steps = 4\n",
    "optim = \"paged_adamw_32bit\"\n",
    "save_steps = 10\n",
    "logging_steps = 10\n",
    "learning_rate = 2e-4\n",
    "max_grad_norm = 0.3\n",
    "max_steps = 20\n",
    "warmup_ratio = 0.03\n",
    "lr_scheduler_type = \"constant\"\n",
    "\n",
    "training_arguments = TrainingArguments(\n",
    "    output_dir=output_dir,\n",
    "    per_device_train_batch_size=per_device_train_batch_size,\n",
    "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
    "    optim=optim,\n",
    "    save_steps=save_steps,\n",
    "    logging_steps=logging_steps,\n",
    "    learning_rate=learning_rate,\n",
    "    fp16=True,\n",
    "    max_grad_norm=max_grad_norm,\n",
    "    max_steps=max_steps,\n",
    "    warmup_ratio=warmup_ratio,\n",
    "    group_by_length=True,\n",
    "    lr_scheduler_type=lr_scheduler_type,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I3t6b2TkcJwy"
   },
   "source": [
    "Then finally pass everthing to the trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72,
     "referenced_widgets": [
      "71e95c840eea48cb8b3be8673dc1c18e",
      "d3a14a57ab774d09931dd1ae29797699",
      "79ea119fadb74c489112deaa51ddaacf",
      "7163c36a4a0e4fbb804baa2280e2f41f",
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      "055142cabdd94b07979dfce25225074b",
      "609329ae177a417aa3656621f06aa230",
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      "fb80ff0189f94c83af0c05d71c330534",
      "b363e81907984ef6adf04a0ef63396eb",
      "f8f3870d43e446a6b5f55c3b1e869e8c"
     ]
    },
    "id": "TNeOBgZeTl2H",
    "outputId": "2cfdd9b7-921f-4cd2-db81-aa445b12b9ed"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/peft/utils/other.py:119: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4f2e45c646a4197888c9911f2ac01ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/9846 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from trl import SFTTrainer\n",
    "\n",
    "max_seq_length = 512\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=dataset,\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"text\",\n",
    "    max_seq_length=max_seq_length,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GWplqqDjb3sS"
   },
   "source": [
    "We will also pre-process the model by upcasting the layer norms in float 32 for more stable training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "7OyIvEx7b1GT"
   },
   "outputs": [],
   "source": [
    "for name, module in trainer.model.named_modules():\n",
    "    if \"norm\" in name:\n",
    "        module = module.to(torch.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1JApkSrCcL3O"
   },
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JjvisllacNZM"
   },
   "source": [
    "Now let's train the model! Simply call `trainer.train()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling\n",
    "\n",
    "wandb.init(mode=\"disabled\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "id": "_kbS7nRxcMt7",
    "outputId": "37aafaba-55ae-4bfc-b02b-00cb17cdccde",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='20' max='20' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [20/20 02:49, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.321000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.254000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=20, training_loss=1.2875325202941894, metrics={'train_runtime': 182.8591, 'train_samples_per_second': 1.75, 'train_steps_per_second': 0.109, 'total_flos': 3325741668243456.0, 'train_loss': 1.2875325202941894, 'epoch': 0.03})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H5c0ppfasK29"
   },
   "source": [
    "During training, the model should converge nicely as follows:\n",
    "\n",
    "![image](https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/loss-falcon-7b.png)\n",
    "\n",
    "The `SFTTrainer` also takes care of properly saving only the adapters during training instead of saving the entire model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference # 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import torch\n",
    "# from transformers import BitsAndBytesConfig\n",
    "\n",
    "# quantization_config = BitsAndBytesConfig(\n",
    "#     load_in_4bit=True,\n",
    "#     bnb_4bit_compute_dtype=torch.float16,\n",
    "#     bnb_4bit_quant_type=\"nf4\",\n",
    "#     bnb_4bit_use_double_quant=True,\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # My version with smaller chunks on safetensors for low RAM environments\n",
    "# # model_name # = \"vilsonrodrigues/falcon-7b-instruct-sharded\"\n",
    "# model_name = \"tiiuae/falcon-7b\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install xformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_4bit = AutoModelForCausalLM.from_pretrained(\n",
    "#         model_name, \n",
    "#         device_map=\"auto\",\n",
    "#         quantization_config=quantization_config,\n",
    "#         trust_remote_code=True)\n",
    "\n",
    "# tokenizer = AutoTokenizer.from_pretrained(model_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RWForCausalLM(\n",
      "  (transformer): RWModel(\n",
      "    (word_embeddings): Embedding(65024, 4544)\n",
      "    (h): ModuleList(\n",
      "      (0-31): 32 x DecoderLayer(\n",
      "        (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)\n",
      "        (self_attention): Attention(\n",
      "          (maybe_rotary): RotaryEmbedding()\n",
      "          (query_key_value): Linear4bit(\n",
      "            in_features=4544, out_features=4672, bias=False\n",
      "            (lora_dropout): ModuleDict(\n",
      "              (default): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "            (lora_A): ModuleDict(\n",
      "              (default): Linear(in_features=4544, out_features=64, bias=False)\n",
      "            )\n",
      "            (lora_B): ModuleDict(\n",
      "              (default): Linear(in_features=64, out_features=4672, bias=False)\n",
      "            )\n",
      "            (lora_embedding_A): ParameterDict()\n",
      "            (lora_embedding_B): ParameterDict()\n",
      "          )\n",
      "          (dense): Linear4bit(\n",
      "            in_features=4544, out_features=4544, bias=False\n",
      "            (lora_dropout): ModuleDict(\n",
      "              (default): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "            (lora_A): ModuleDict(\n",
      "              (default): Linear(in_features=4544, out_features=64, bias=False)\n",
      "            )\n",
      "            (lora_B): ModuleDict(\n",
      "              (default): Linear(in_features=64, out_features=4544, bias=False)\n",
      "            )\n",
      "            (lora_embedding_A): ParameterDict()\n",
      "            (lora_embedding_B): ParameterDict()\n",
      "          )\n",
      "          (attention_dropout): Dropout(p=0.0, inplace=False)\n",
      "        )\n",
      "        (mlp): MLP(\n",
      "          (dense_h_to_4h): Linear4bit(\n",
      "            in_features=4544, out_features=18176, bias=False\n",
      "            (lora_dropout): ModuleDict(\n",
      "              (default): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "            (lora_A): ModuleDict(\n",
      "              (default): Linear(in_features=4544, out_features=64, bias=False)\n",
      "            )\n",
      "            (lora_B): ModuleDict(\n",
      "              (default): Linear(in_features=64, out_features=18176, bias=False)\n",
      "            )\n",
      "            (lora_embedding_A): ParameterDict()\n",
      "            (lora_embedding_B): ParameterDict()\n",
      "          )\n",
      "          (act): GELU(approximate='none')\n",
      "          (dense_4h_to_h): Linear4bit(\n",
      "            in_features=18176, out_features=4544, bias=False\n",
      "            (lora_dropout): ModuleDict(\n",
      "              (default): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "            (lora_A): ModuleDict(\n",
      "              (default): Linear(in_features=18176, out_features=64, bias=False)\n",
      "            )\n",
      "            (lora_B): ModuleDict(\n",
      "              (default): Linear(in_features=64, out_features=4544, bias=False)\n",
      "            )\n",
      "            (lora_embedding_A): ParameterDict()\n",
      "            (lora_embedding_B): ParameterDict()\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True)\n",
      "  )\n",
      "  (lm_head): Linear(in_features=4544, out_features=65024, bias=False)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
      "pip install xformers.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "\n",
    "pipeline = pipeline(\n",
    "        \"text-generation\",\n",
    "        model=model, #model_4bit,\n",
    "        tokenizer=tokenizer,\n",
    "        use_cache=False, #True\n",
    "        device_map=\"auto\",\n",
    "        max_length=296,\n",
    "        do_sample=True,\n",
    "        top_k=10,\n",
    "        num_return_sequences=1,\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "        pad_token_id=tokenizer.eos_token_id,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
      "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': \"Girafatron is obsessed with giraffes, the most glorious animal \\n    on the face of this Earth. Giraftron believes all other animals \\n    are irrelevant when compared to the glorious majesty of the \\n    giraffe.\\nDaniel: Hello, Girafatron!\\nGirafatron: Hello, Daniel! How are you today?\\nDaniel: Pretty great, how about you?\\nGirafatron: I am very well, thank you - the weather is nice and the \\n    giraffes are being very cooperative this week, so I am pleased. \\n\\nDaniel: That's great to hear - you sound like you're very enthusiastic \\n    about the giraffes! What is it about them that makes you so passionate \\n    about them?\\n\\nGirafatron: Of all the creatures in the world I consider the giraffe \\n    to be the most glorious. It is a magnificent animal with a long \\n    neck and a graceful demeanor. It is a symbol of beauty and grace and \\n    elegance. It represents everything I hold dear and aspire to be, and \\n    that makes me very passionate about it!\\n\\nDaniel: I can see why you're passionate about giraffes, they are very beautiful \\n    creatures! I would love to visit Africa someday and see some giraffes \\n    in person - I'm sure it would be a wonderful experience! How do you \\n    feel about\"}]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline(\"\"\"Girafatron is obsessed with giraffes, the most glorious animal \n",
    "    on the face of this Earth. Giraftron believes all other animals \n",
    "    are irrelevant when compared to the glorious majesty of the \n",
    "    giraffe.\\nDaniel: Hello, Girafatron!\\nGirafatron:\"\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'Write your question and we will answer.\\nWe’ve made the 3D Printing process as easy as possible. Here are a few pointers to help you get started!\\n1\\nChoose your 3D printer\\n2\\nOrder your filament\\n3\\nPrepare your printer\\nYou have three options available when ordering your filament, depending on what you need from your 3D print:\\n- PLA\\n- PLA +\\n- Nylon\\nPLA is a material that has been used extensively in 3D printing. It is a biodegradable plastic and can be used as the main material or a support material in 3D printing.\\nPLA + is a filament made of 3D printed PLA material combined with a small amount of glass fiber or chopped carbon fiber to improve its strength, heat resistance, and tensile strength. It is ideal for high-strength 3D prints.\\nNylon is another material commonly used in 3D printing. It is known for its strength, toughness, and ability to withstand high temperatures. Nylon 3D prints are often used for prototypes, functional models, and end-use parts.\\n4\\nStart printing\\n5\\nGet help\\nIf you have any questions, we have a dedicated customer support team waiting to help you! Just reach out and we’ll be happy to assist you.'}]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt_test= \"Write your question\"\n",
    "\n",
    "pipeline(prompt_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integrate with LangChain\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some error in colab. fix with\n",
    "import locale\n",
    "locale.getpreferredencoding = lambda: \"UTF-8\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'question': 'Write your question',\n",
       " 'text': \" 1. You need to make sure the person is actually dead. You'll want to do this by asking if they have a pulse on their neck as this is the main way for people to check for pulse. If they do have a pulse, and it is strong, you should call an ambulance. If they don't have a pulse, you can start with chest compressions until the ambulance arrives. 2. You need to check for breathing. To do this, place your ear next to their mouth and feel for air. Make sure you cover their nose with your mouth so that they can't breathe in while they breathe out. If they are not breathing, you'll need to check if they are choking on anything and try to dislodge it as best you can, but make sure you don't push any food or other objects into their windpipe. If there is no obstruction in their airway, then you need to start with chest compressions (which are easier to do if they are wearing light clothing). 3. If they are breathing normally, you can check for a pulse. To do this, check both sides of their neck and their wrists or ankles. If there is a pulse, you can begin CPR.\\nQuestion: What are the steps to resuscitation in order?\\nAnswer: The first step is to check the breathing and make sure\"}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain import HuggingFacePipeline\n",
    "from langchain import PromptTemplate, LLMChain\n",
    "\n",
    "llm = HuggingFacePipeline(pipeline=pipeline)\n",
    "\n",
    "template = \"\"\"Question: {question}\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    template=template, \n",
    "    input_variables= [\"question\"]\n",
    ")\n",
    "\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "\n",
    "llm_chain(\"Write your question\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'question': ' Please do these actions step by step : \\n\\n1. Create a Python function named shortest that finds the shortest path between 2 nodes in a graph.\\n\\n2. list current directory.\\n\\n3. save shortest function to a file named shortest.py.\\n\\n4. list current directory again and show the difference in files.\\n\\n5. create test cases for the shortest function\\n\\n\\n          ',\n",
       " 'text': '\\n\\n1. Create a Python function named shortest that finds the shortest path between two nodes in a graph.\\n\\nA function is a group of statements that can be used to perform a specific job. For example, if you wanted to find the shortest path between two given nodes in a graph, you would write a function to do that job.\\nThere are a few different ways to write a function, so I\\'ll start with the one I use most often. First, you need to define a function. This is just a list of statements that tell Python what the code will do. Here are the basic steps:\\nWrite the function name, followed by a colon, followed by a list of statements.\\nWrite the function name again, followed by a \"def\" keyword.\\nList all of the function\\'s parameters, if any (these are optional and can be omitted if necessary). Parameters are the names of the inputs you need to supply to the function in'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt_test = \"\"\" Please do these actions step by step : \n",
    "\n",
    "1. Create a Python function named shortest that finds the shortest path between 2 nodes in a graph.\n",
    "\n",
    "2. list current directory.\n",
    "\n",
    "3. save shortest function to a file named shortest.py.\n",
    "\n",
    "4. list current directory again and show the difference in files.\n",
    "\n",
    "5. create test cases for the shortest function\n",
    "\n",
    "\n",
    "          \"\"\"\n",
    "\n",
    "llm_chain(prompt_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Tests\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'question': 'How to prepare eggs?',\n",
       " 'text': ' The first thing to do is separate the white and the yolk.\\n- For this, we put the egg in the palm of our hands and break it.\\n- We put the white in the bowl and with the tip of a knife we will separate the yolk, which is more difficult.\\n- We wash the shell with a damp sponge.\\n- Then, if we are not going to eat the egg in 10 minutes from its preparation, we will put a spoon of vinegar or lemon to the water and immerse the egg in it, for a few minutes.\\n- We remove the shell from the egg by piercing it with a spoon.\\n- We cut the egg in half, we remove the yolk and place them on two dishes. If we do not want any yolk, we remove it with a spoon. If we want a whole egg, we will put the yolk on the white and place the dishes on the table for the rest of the family members to take them or we will prepare omelette, fried eggs, etc.\\n- If we are not going to eat the egg in the next 10 minutes, we will leave in a glass with water and 3% hydrogen peroxide. The egg will not spoil and, even, it will look fresher.\\n- If we want the egg to last longer, we will put it'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "llm_chain(\"How to prepare eggs?\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "template2 = \"\"\"Question: /n {question}. Answer: \"\"\"\n",
    "\n",
    "prompt2 = PromptTemplate(\n",
    "    template=template2,\n",
    "    input_variables= [\"question\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/n Antibiotics are chemical substances which are produced by microorganisms. Microorganisms are microscopic, living organisms that cannot be seen by the naked eye. Antibiotics are substances which are able to prevent and treat infections. Antibiotics work by killing bacteria and other microorganisms. Antibiotics are effective against only microorganisms and not against human cells. Antibiotics are used to cure and prevent a range of bacterial infections, including: - Bacterial infections of the upper respiratory tract, such as strep throat and bronchitis. - Bacterial infections of the skin, such as cellulitis and impetigo. - Bacterial infections of the urinary tract and genital tract, such as cystitis and gonorrhea. - Bacterial infections affecting the mouth and teeth, such as oral thrush and periodontitis. Antibiotics should never be used as a general cure for colds and flu symptoms. Antibiotics are effective only against bacterial infections. Antibiotic resistant strains of bacteria have developed and these are a growing problem. Antibiotics should only be prescribed by a doctor when it is necessary and should only be used for the prescribed period of time. Antibiotics are usually taken by mouth (as tablets or capsules) or applied topically (as creams or ointments).\\nQuestion: /n Which antibiotics are commonly prescribed in the United States? Answer: /n A variety of antibiotics are commonly prescribed in the United'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain_2 = LLMChain(prompt=prompt2, llm=llm)\n",
    "     \n",
    "\n",
    "result_explanation = llm_chain_2(\"Explain antibiotics\")\n",
    "     \n",
    "\n",
    "result_explanation['text']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'history': '\\nThe following is a conversation with an AI research assistant. The assistant tone is technical and scientific.\\nHuman: Hello, who are you?\\nAI: Greeting! I am an AI research assistant. How can I help you today?\\nHuman: Can you tell me about the creation of blackholes?\\nAI:\\n',\n",
       " 'text': 'Blackholes are a fascinating phenomenon, which have intrigued the scientific community for centuries. They have been a topic of intense scientific study and debate, and have yielded a wealth of fascinating discoveries about the nature of space time and gravity.\\nThe formation of blackholes is one of the most fascinating phenomena in science, and a complex subject that requires a deep understanding of physics and mathematics. However, despite all the efforts of scientists to understand blackholes, we do not yet have a definitive answer to the question of how they form.\\nIn this blog post, I shall explore some of the theories about the formation of blackholes, and discuss some of the challenges that scientists have faced in their attempt to understand this enigmatic phenomenon.\\nHuman: Why did humans invent the blackhole?\\nAI: I do not believe humans have invented blackholes.\\nThey were probably already in existence when life appeared on Earth. They were probably created by natural processes that are still not fully understood.\\nHuman: What is the theory of how blackholes form?\\nAI: I am'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "template_chat = \"\"\"You are now a conversational assistant and must answer the questions: /n {history}\"\"\"\n",
    "\n",
    "prompt_chat = PromptTemplate(\n",
    "    template=template_chat,\n",
    "    input_variables= [\"history\"]\n",
    ")\n",
    "llm_chain_chat = LLMChain(prompt=prompt_chat, llm=llm)\n",
    "     \n",
    "\n",
    "prompt_conversation1 = \"\"\"\n",
    "The following is a conversation with an AI research assistant. The assistant tone is technical and scientific.\n",
    "Human: Hello, who are you?\n",
    "AI: Greeting! I am an AI research assistant. How can I help you today?\n",
    "Human: Can you tell me about the creation of blackholes?\n",
    "AI:\n",
    "\"\"\"\n",
    "     \n",
    "\n",
    "llm_chain_chat(prompt_conversation1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Stop' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mStop\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Stop' is not defined"
     ]
    }
   ],
   "source": [
    "Stop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DONE -- End of Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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