HPO_Model_Github.ipynb

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    "# Preparation of the Jupyter notebook kernel\n",
    "\n",
    "conda create --name llama python=3.10\n",
    "   conda activate llama \n",
    "    pip install git+https://github.com/huggingface/transformers.git\n",
    "    pip install git+https://github.com/huggingface/peft.git\n",
    "  925  pip install git+https://github.com/huggingface/accelerate.git \n",
    "  926  pip install -q -U trl\n",
    "  927  pip install -q trl\n",
    "  928  pip install -U datasets\n",
    "  929  pip install -U bitsandbytes \n",
    "  930  pip install -U einops\n",
    "  931  pip install -U wandb\n",
    "  932  pip install --user ipykernal\n",
    "  933  pip install --user ipykernel\n",
    "  934  python -m ipykernel install --user --name=llama\n",
    "\n",
    "So now you can open jupyter notebook and select the \"llama\" kernel to run the notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6WuAlTCs2b-Y"
   },
   "source": [
    "## Setup"
   ]
  },
  {
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   "source": [
    "import pandas as pd\n",
    "import transformers\n",
    "import textwrap\n",
    "from transformers import LlamaTokenizer, LlamaForCausalLM\n",
    "import os\n",
    "import sys\n",
    "from typing import List\n",
    "\n",
    "from peft import (\n",
    "    LoraConfig,\n",
    "    get_peft_model,\n",
    "    get_peft_model_state_dict,\n",
    "    prepare_model_for_int8_training,\n",
    ")\n",
    "\n",
    "# import fire\n",
    "import torch\n",
    "from datasets import load_dataset\n",
    "import pandas as pd\n",
    "\n",
    "# import matplotlib.pyplot as plt\n",
    "# import matplotlib as mpl\n",
    "# import seaborn as sns\n",
    "# from pylab import rcParams\n",
    "import json\n",
    "\n",
    "# %matplotlib inline\n",
    "# sns.set(rc={'figure.figsize':(8, 6)})\n",
    "# sns.set(rc={'figure.dpi':100})\n",
    "# sns.set(style='white', palette='muted', font_scale=1.2)\n",
    "\n",
    "#DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#load in JSON files of your choice to normalize concepts \n",
    "test_list = load_dataset(\"json\", data_files=\"test_terms.json\")\n",
    "\n",
    "#alternatively, you can simply create an array of terms you wish to test with \n",
    "test_list = ['hearing impairment', 'hearing loss', 'hard of hearing', \"hearing impairment\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#test_list = test_list['train']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lLIr-ynuTilx"
   },
   "source": [
    "## Inference\n",
    "Code below is to load in the fine-tuned Llama2 model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_MODEL = \"directory_of_base_model\" #Llama2 base model can be downloaded from Meta for free \n",
    "lora_weights = 'directory_of_fine-tuned_lora_model' #model file is in Github under releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LlamaForCausalLM.from_pretrained(\n",
    "            BASE_MODEL,\n",
    "            load_in_8bit=False,\n",
    "            torch_dtype=torch.float16,\n",
    "            device_map = \"auto\"\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from peft import PeftModel\n",
    "model = PeftModel.from_pretrained(\n",
    "            model,\n",
    "            lora_weights,\n",
    "            torch_dtype=torch.float16,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk\n",
    "model.config.bos_token_id = 1\n",
    "model.config.eos_token_id = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   },
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   "source": [
    "from transformers import GenerationConfig\n",
    "generation_config = GenerationConfig(\n",
    "            temperature=0.1,\n",
    "            top_p=0.5,\n",
    "            do_sample=True\n",
    "#             top_k=40,\n",
    "#             num_beams=4,\n",
    "#             **kwargs,\n",
    "#             bad_words_ids = [[2]]\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code below loops through terms and inputs them into model. Model will produce an output of an HPO ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "for i in range(0, len(test_list)): #loop through all terms \n",
    "    disease_name = test_list[i] #get term name\n",
    "    prompt = f\"\"\"The Human Phenotype Ontology term {disease_name} is identified by the HPO ID HP:\"\"\"\n",
    "\n",
    "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
    "    input_ids = inputs[\"input_ids\"].to('cuda')\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        generation_output = model.generate(\n",
    "            input_ids=input_ids,\n",
    "            generation_config=generation_config,\n",
    "            return_dict_in_generate=True,\n",
    "            output_scores=True,\n",
    "            max_new_tokens=15,\n",
    "        )\n",
    "    #generation_output \n",
    "    s = generation_output.sequences[0]\n",
    "    output = tokenizer.decode(s) #decode into string \n",
    "    \n",
    "    print(f\"Output: {output}\")"
   ]
  }
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