KG_RAG/.test_notebooks/biomedgpt.ipynb

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      "/root/anaconda3/envs/kg_rag_test_2/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer\n",
    "import torch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32d27545-5454-4328-b62f-91a86c905710",
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   "source": [
    "BIOMEDGPT_MODEL_CARD = 'PharMolix/BioMedGPT-LM-7B'\n",
    "BRANCH_NAME = 'main'\n",
    "CACHE_DIR = '/data/somank/llm_data/llm_models/huggingface'\n"
   ]
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     "text": [
      "Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:19<00:00,  6.56s/it]\n",
      "/root/anaconda3/envs/kg_rag_test_2/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n",
      "/root/anaconda3/envs/kg_rag_test_2/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.9 s, sys: 26.3 s, total: 45.3 s\n",
      "Wall time: 26 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(BIOMEDGPT_MODEL_CARD,\n",
    "                                         revision=BRANCH_NAME,\n",
    "                                         cache_dir=CACHE_DIR)\n",
    "model = AutoModelForCausalLM.from_pretrained(BIOMEDGPT_MODEL_CARD,                                             \n",
    "                                    device_map='auto',\n",
    "                                    torch_dtype=torch.float16,\n",
    "                                    revision=BRANCH_NAME,\n",
    "                                    cache_dir=CACHE_DIR\n",
    "                                    )"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a28f1ce-5cc5-4a17-84d7-b0dda29815a5",
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   "source": [
    "question = '''\n",
    "For the following Question, answer if it is either True or False.\n",
    "Question:alpha-Mannosidosis associates Gene MAN2B1\n",
    "Answer:\n",
    "'''\n",
    "text = [\"Out of the given list, which Gene is associated with psoriasis and Takayasu's arteritis. Given list is: SHTN1, HLA-B,  SLC14A2,  BTBD9,  DTNB\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f59eeb37-57dd-42ae-b9ff-f8442eb613a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P1,  TNFAIP3,  TNIP1,  TNIP3,  TNFAIP2,  TNFAIP6,  TNFAIP7,  TNFAIP8,  TNFAIP9,  TNFAIP10\n"
     ]
    }
   ],
   "source": [
    "input = tokenizer(text,\n",
    "              truncation=True,\n",
    "              return_tensors=\"pt\")\n",
    "\n",
    "with torch.no_grad():\n",
    "    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
    "    output = model.generate(**input, \n",
    "                               streamer=streamer, \n",
    "                               max_new_tokens=64,\n",
    "                               temperature=0.01,\n",
    "                               do_sample=True)\n",
    "    output_text = tokenizer.decode(output[0], \n",
    "                                   skip_special_tokens=True)\n",
    "    "
   ]
  },
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   "execution_count": null,
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   "source": []
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