{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "vYuUCZA9Ow-1",
        "outputId": "731e5845-c7c0-420f-bcd5-c8dc174f654b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Collecting transformers==4.46.2\n",
            "  Using cached transformers-4.46.2-py3-none-any.whl.metadata (44 kB)\n",
            "Collecting peft==0.13.2\n",
            "  Using cached peft-0.13.2-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting accelerate==1.1.1\n",
            "  Using cached accelerate-1.1.1-py3-none-any.whl.metadata (19 kB)\n",
            "Collecting trl==0.12.1\n",
            "  Using cached trl-0.12.1-py3-none-any.whl.metadata (10 kB)\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement bitsandbytes==0.45.2 (from versions: 0.31.8, 0.32.0, 0.32.1, 0.32.2, 0.32.3, 0.33.0, 0.33.1, 0.34.0, 0.35.0, 0.35.1, 0.35.2, 0.35.3, 0.35.4, 0.36.0, 0.36.0.post1, 0.36.0.post2, 0.37.0, 0.37.1, 0.37.2, 0.38.0, 0.38.0.post1, 0.38.0.post2, 0.38.1, 0.39.0, 0.39.1, 0.40.0, 0.40.0.post1, 0.40.0.post2, 0.40.0.post3, 0.40.0.post4, 0.40.1, 0.40.1.post1, 0.40.2, 0.41.0, 0.41.1, 0.41.2, 0.41.2.post1, 0.41.2.post2, 0.41.3, 0.41.3.post1, 0.41.3.post2, 0.42.0)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for bitsandbytes==0.45.2\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# imports and libs\n",
        "!pip3 -q install transformers\n",
        "!pip3 install trl\n",
        "!pip3 install -U bitsandbytes\n",
        "!pip3 install datasets\n",
        "!pip3 install peft\n",
        "!pip3 install accelerate\n",
        "# !pip install -q datasets trl peft bitsandbytes accelerate datasets\n",
        "# !pip3 install transformers==4.46.2 peft==0.13.2 accelerate==1.1.1 trl==0.12.1 bitsandbytes==0.45.2 datasets==3.1.0 huggingface-hub==0.26.2 safetensors==0.4.5 pandas==2.2.2 matplotlib==3.8.0 numpy==1.26.4\n",
        "\n",
        "import torch\n",
        "from transformers import AutoModelForCausalLM, DataCollatorWithPadding\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, BitsAndBytesConfig\n",
        "from torch.optim import AdamW\n",
        "from datasets import load_dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model, TaskType\n",
        "from transformers import Trainer, TrainingArguments\n",
        "from trl import SFTConfig, SFTTrainer\n",
        "from peft import PeftModel, PeftConfig"
      ]
    },
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        },
        "id": "1ytailHyOPnt",
        "outputId": "769d2fc5-3439-4cb5-b311-5e03c57f49ee"
      },
      "outputs": [
        {
          "ename": "ImportError",
          "evalue": "Using `bitsandbytes` 4-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[3], line 12\u001b[0m\n\u001b[1;32m      3\u001b[0m config \u001b[38;5;241m=\u001b[39m PeftConfig\u001b[38;5;241m.\u001b[39mfrom_pretrained(peft_model_id)\n\u001b[1;32m      5\u001b[0m bnb_config \u001b[38;5;241m=\u001b[39m BitsAndBytesConfig(\n\u001b[1;32m      6\u001b[0m    load_in_4bit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      7\u001b[0m    bnb_4bit_quant_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnf4\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      8\u001b[0m    bnb_4bit_use_double_quant\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      9\u001b[0m    bnb_4bit_compute_dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32\n\u001b[1;32m     10\u001b[0m )\n\u001b[0;32m---> 12\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModelForCausalLM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mauto\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mquantization_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbnb_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m    \u001b[49m\u001b[43moffload_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moffload\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moffload_state_dict\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     17\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(config\u001b[38;5;241m.\u001b[39mbase_model_name_or_path)\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# Load the Lora model\u001b[39;00m\n",
            "File \u001b[0;32m~/Library/Python/3.10/lib/python/site-packages/transformers/models/auto/auto_factory.py:571\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m    569\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m model_class\u001b[38;5;241m.\u001b[39mconfig_class \u001b[38;5;241m==\u001b[39m config\u001b[38;5;241m.\u001b[39msub_configs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext_config\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    570\u001b[0m         config \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mget_text_config()\n\u001b[0;32m--> 571\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_class\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    572\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    573\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    574\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    575\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    576\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    577\u001b[0m )\n",
            "File \u001b[0;32m~/Library/Python/3.10/lib/python/site-packages/transformers/modeling_utils.py:282\u001b[0m, in \u001b[0;36mrestore_default_torch_dtype.<locals>._wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    280\u001b[0m old_dtype \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mget_default_dtype()\n\u001b[1;32m    281\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 282\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    284\u001b[0m     torch\u001b[38;5;241m.\u001b[39mset_default_dtype(old_dtype)\n",
            "File \u001b[0;32m~/Library/Python/3.10/lib/python/site-packages/transformers/modeling_utils.py:4296\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   4293\u001b[0m     hf_quantizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   4295\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4296\u001b[0m     \u001b[43mhf_quantizer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_environment\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4297\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4298\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfrom_tf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_tf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4299\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfrom_flax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfrom_flax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4300\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4301\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweights_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4302\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4303\u001b[0m     torch_dtype \u001b[38;5;241m=\u001b[39m hf_quantizer\u001b[38;5;241m.\u001b[39mupdate_torch_dtype(torch_dtype)\n\u001b[1;32m   4304\u001b[0m     device_map \u001b[38;5;241m=\u001b[39m hf_quantizer\u001b[38;5;241m.\u001b[39mupdate_device_map(device_map)\n",
            "File \u001b[0;32m~/Library/Python/3.10/lib/python/site-packages/transformers/quantizers/quantizer_bnb_4bit.py:76\u001b[0m, in \u001b[0;36mBnb4BitHfQuantizer.validate_environment\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[1;32m     73\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing `bitsandbytes` 4-bit quantization requires Accelerate: `pip install \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccelerate>=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mACCELERATE_MIN_VERSION\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     74\u001b[0m     )\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_bitsandbytes_available():\n\u001b[0;32m---> 76\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[1;32m     77\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing `bitsandbytes` 4-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     78\u001b[0m     )\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mintegrations\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m validate_bnb_backend_availability\n\u001b[1;32m     81\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m is_bitsandbytes_multi_backend_available\n",
            "\u001b[0;31mImportError\u001b[0m: Using `bitsandbytes` 4-bit quantization requires the latest version of bitsandbytes: `pip install -U bitsandbytes`"
          ]
        }
      ],
      "source": [
        "peft_model_id = \"./ai_model/\"\n",
        "\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "   load_in_4bit=True,\n",
        "   bnb_4bit_quant_type=\"nf4\",\n",
        "   bnb_4bit_use_double_quant=True,\n",
        "   bnb_4bit_compute_dtype=torch.float32\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    config.base_model_name_or_path,\n",
        "    device_map='auto',\n",
        "    quantization_config=bnb_config,\n",
        "    offload_folder=\"offload\", offload_state_dict = True\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "sRWsj9HVQUjB"
      },
      "outputs": [],
      "source": [
        "def generate(model, tokenizer, prompt, max_new_tokens=64, skip_special_tokens=False):\n",
        "    tokenized_input = tokenizer(prompt, add_special_tokens=False, return_tensors=\"pt\").to(model.device)\n",
        "\n",
        "    model.eval()\n",
        "    generation_output = model.generate(**tokenized_input,\n",
        "                                       eos_token_id=tokenizer.eos_token_id,\n",
        "                                       max_new_tokens=max_new_tokens)\n",
        "\n",
        "    output = tokenizer.batch_decode(generation_output,\n",
        "                                    skip_special_tokens=skip_special_tokens)\n",
        "    # output\n",
        "    output = output[0].split(\"<|assistant|>\")[1]\n",
        "    return output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "mtdiesgsRAQe"
      },
      "outputs": [],
      "source": [
        "def gen_prompt(tokenizer, sentence):\n",
        "    converted_sample = [\n",
        "        {\"role\": \"user\", \"content\": sentence},\n",
        "    ]\n",
        "    prompt = tokenizer.apply_chat_template(converted_sample,\n",
        "                                           tokenize=False,\n",
        "                                           add_generation_prompt=True)\n",
        "    return prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "id": "v8ebwqMpQl4l",
        "outputId": "7ce67c5b-6785-4789-eec8-f40ac7d24fff"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<ipython-input-14-386a3b04eadd>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "  while user_input is not \"q\" or user_input is not \"quit\":\n",
            "<ipython-input-14-386a3b04eadd>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "  while user_input is not \"q\" or user_input is not \"quit\":\n",
            "<ipython-input-14-386a3b04eadd>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "  while user_input is not \"q\" or user_input is not \"quit\":\n",
            "<ipython-input-14-386a3b04eadd>:2: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "  while user_input is not \"q\" or user_input is not \"quit\":\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter q/quit to stop:\n",
            "Enter something to ask doctor:hey\n",
            " Hello, Welcome to Chat Doctor, I don't know if for sure this is a boil or a swollen lymph node in response to a wound or infection that you have on the lower limbs. If you have no wound, then this is most likely a swollen\n",
            "Enter q/quit to stop:\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "Interrupted by user",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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        "user_input = \"\"\n",
        "while user_input is not \"q\" or user_input is not \"quit\":\n",
        "  print(\"Enter q/quit to stop:\")\n",
        "  user_input = input(\"Enter something to ask doctor:\")\n",
        "  if user_input == \"q\" or user_input == \"quit\":\n",
        "    print(\"Thank you, goodbye.\")\n",
        "    break\n",
        "  print(generate(model, tokenizer, gen_prompt(tokenizer, user_input)))"
      ]
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