Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f120c30a-d2d1-4cb1-b432-01305d2cc1bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0_To generate n-best hypothesis.ipynb    torgo.csv\n",
      "1_To generate audio_features.ipynb       torgo_test_single.ipynb\n",
      "2_finetune_whisper_on_torgo-Copy1.ipynb  Untitled.ipynb\n",
      "3_prepare_for_torgo_baseline.ipynb       \u001b[0m\u001b[01;34mwhisper-tiny\u001b[0m/\n",
      "\u001b[01;34moutput\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "173ec88f-9494-499f-951c-1b0f56720aae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvcc: NVIDIA (R) Cuda compiler driver\n",
      "Copyright (c) 2005-2022 NVIDIA Corporation\n",
      "Built on Wed_Sep_21_10:33:58_PDT_2022\n",
      "Cuda compilation tools, release 11.8, V11.8.89\n",
      "Build cuda_11.8.r11.8/compiler.31833905_0\n"
     ]
    }
   ],
   "source": [
    "!nvcc --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32c61c6a-e093-4584-82a0-c1779cf03241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA/data_preparation'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e992c6ab-73f3-435e-bcb9-d4a5e113c727",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9ae87f8-992d-4e7c-a46d-659f45911c99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/work/van-speech-nlp/jindaznb/asrenv'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.prefix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74158424-3b1e-40d8-b1a7-145b9562099e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 43281918\n"
     ]
    }
   ],
   "source": [
    "!sbatch tinf.bash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "328ed19d-bdab-4c92-b2cb-6a757b6f14c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submitted batch job 43281905\n"
     ]
    }
   ],
   "source": [
    "!sbatch ttrain.bash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c82e60be-52b5-4390-92ca-f17e5ef827c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sbatch: error: Unable to open file tdata.bash\n"
     ]
    }
   ],
   "source": [
    "!sbatch tdata.bash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "acc6de8c-7ffb-4648-b46f-59369751ff62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             JOBID PARTITION     NAME     USER ST       TIME  NODES NODELIST(REASON)\n",
      "          43281905       gpu ttrain.b zhang.ji  R      29:02      1 d1029\n",
      "          43280158       gpu sys/dash zhang.ji  R    3:09:29      1 d1007\n",
      "          43277444     short sys/dash zhang.ji  R    7:32:16      1 c0251\n"
     ]
    }
   ],
   "source": [
    "!squeue -u zhang.jinda1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "705712e0-2730-48f8-b946-3a1108c98f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!scancel 43271171"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2a40010f-c43b-4024-a1d9-5efc2788ae0d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'learning_rate' 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[25], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m     training_args_dict \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(training_args_file)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Replace the default values with the values from the command line arguments\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m training_args_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlearning_rate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mlearning_rate\u001b[49m\n\u001b[1;32m      7\u001b[0m training_args_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mper_device_train_batch_size\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m train_batch_size\n\u001b[1;32m      8\u001b[0m training_args_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mper_device_eval_batch_size\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m eval_batch_size\n",
      "\u001b[0;31mNameError\u001b[0m: name 'learning_rate' is not defined"
     ]
    }
   ],
   "source": [
    "import json\n",
    "with open('training_args.json') as training_args_file:\n",
    "    training_args_dict = json.load(training_args_file)\n",
    "\n",
    "# Replace the default values with the values from the command line arguments\n",
    "training_args_dict['learning_rate'] = learning_rate\n",
    "training_args_dict['per_device_train_batch_size'] = train_batch_size\n",
    "training_args_dict['per_device_eval_batch_size'] = eval_batch_size\n",
    "training_args_dict['seed'] = seed\n",
    "training_args_dict['gradient_accumulation_steps'] = gradient_accumulation_steps\n",
    "training_args_dict['optim'] = optimizer\n",
    "training_args_dict['lr_scheduler_type'] = lr_scheduler_type\n",
    "training_args_dict['num_train_epochs'] = num_epochs\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=model_local_path,\n",
    "    hub_model_id=repo_name,\n",
    "    **training_args_dict\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4136f33d-5fed-4657-9fd8-ae3d9f40de21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "\n",
    "def delete_files_with_prefix(prefix):\n",
    "    # Get the current working directory\n",
    "    current_dir = os.getcwd()\n",
    "\n",
    "    # Create a search pattern for files starting with the given prefix\n",
    "    search_pattern = os.path.join(current_dir, f\"{prefix}*\")\n",
    "\n",
    "    # Find all files matching the search pattern\n",
    "    files_to_delete = glob.glob(search_pattern)\n",
    "\n",
    "    # Iterate over the files and delete them\n",
    "    for file_path in files_to_delete:\n",
    "        try:\n",
    "            os.remove(file_path)\n",
    "            print(f\"Deleted: {file_path}\")\n",
    "        except Exception as e:\n",
    "            print(f\"Error deleting {file_path}: {e}\")\n",
    "\n",
    "# Example usage\n",
    "prefix = \"slurm-4271395\"  # Replace with your actual prefix\n",
    "delete_files_with_prefix(prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ccfb1eaa-f065-4353-802a-d9c130344798",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('training_args.json') as training_args_file:\n",
    "    training_args_dict = json.load(training_args_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07efbb30-44d1-4658-88e1-851357f04e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'evaluation_strategy': 'steps',\n",
       " 'per_device_train_batch_size': 4,\n",
       " 'per_device_eval_batch_size': 4,\n",
       " 'gradient_accumulation_steps': 2,\n",
       " 'eval_delay': 0,\n",
       " 'learning_rate': 0.0001,\n",
       " 'weight_decay': 0.005,\n",
       " 'adam_beta1': 0.9,\n",
       " 'adam_beta2': 0.999,\n",
       " 'adam_epsilon': 1e-08,\n",
       " 'max_grad_norm': 1.0,\n",
       " 'max_steps': -1,\n",
       " 'lr_scheduler_type': 'linear',\n",
       " 'warmup_ratio': 0.0,\n",
       " 'warmup_steps': 1000,\n",
       " 'save_strategy': 'steps',\n",
       " 'save_steps': 500,\n",
       " 'save_total_limit': 3,\n",
       " 'report_to': 'all',\n",
       " 'seed': 42,\n",
       " 'eval_steps': 1000,\n",
       " 'num_train_epochs': 20,\n",
       " 'optim': 'adamw_torch',\n",
       " 'optim_args': None,\n",
       " 'adafactor': False,\n",
       " 'group_by_length': True,\n",
       " 'length_column_name': 'length',\n",
       " 'push_to_hub': True,\n",
       " 'hub_strategy': 'every_save'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_args_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5644c86a-ced0-4449-b303-8ad46b863578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9e99efe3ae74b6f90b94d1eba63fccf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading config.json:   0%|          | 0.00/4.60k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages/transformers/models/bart/configuration_bart.py:179: UserWarning: Please make sure the config includes `forced_bos_token_id=0` in future versions. The config can simply be saved and uploaded again to be fixed.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e44814cb98f44868978b03138f402bd4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da0a162d70014a49b0f5a2691a267faa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)_tokenizer/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f914fe75db3f4adaa0f902c3fe898002",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
      "The tokenizer class you load from this checkpoint is 'RagTokenizer'. \n",
      "The class this function is called from is 'DPRQuestionEncoderTokenizer'.\n",
      "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
      "The tokenizer class you load from this checkpoint is 'RagTokenizer'. \n",
      "The class this function is called from is 'DPRQuestionEncoderTokenizerFast'.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9502aef2d8c4c43a9ed3cc3406c866d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7eb581dd90644834a967d15169d96ee1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d030c8b3d5fd4ca089170fabad878b59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "879f8379a0a243f492db1d0233d07001",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
      "The tokenizer class you load from this checkpoint is 'RagTokenizer'. \n",
      "The class this function is called from is 'BartTokenizer'.\n",
      "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. \n",
      "The tokenizer class you load from this checkpoint is 'RagTokenizer'. \n",
      "The class this function is called from is 'BartTokenizerFast'.\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "\nRagRetriever requires the faiss library but it was not found in your environment. Checkout the instructions on the\ninstallation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Load the tokenizer, retriever, and model\u001b[39;00m\n\u001b[1;32m      4\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m RagTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacebook/rag-token-nq\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m retriever \u001b[38;5;241m=\u001b[39m \u001b[43mRagRetriever\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfacebook/rag-token-nq\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_dummy_dataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m model \u001b[38;5;241m=\u001b[39m RagSequenceForGeneration\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacebook/rag-token-nq\u001b[39m\u001b[38;5;124m\"\u001b[39m, retriever\u001b[38;5;241m=\u001b[39mretriever)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Define the input query\u001b[39;00m\n",
      "File \u001b[0;32m/work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages/transformers/models/rag/retrieval_rag.py:420\u001b[0m, in \u001b[0;36mRagRetriever.from_pretrained\u001b[0;34m(cls, retriever_name_or_path, indexed_dataset, **kwargs)\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    419\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_pretrained\u001b[39m(\u001b[38;5;28mcls\u001b[39m, retriever_name_or_path, indexed_dataset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 420\u001b[0m     \u001b[43mrequires_backends\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdatasets\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfaiss\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    421\u001b[0m     config \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mor\u001b[39;00m RagConfig\u001b[38;5;241m.\u001b[39mfrom_pretrained(retriever_name_or_path, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    422\u001b[0m     rag_tokenizer \u001b[38;5;241m=\u001b[39m RagTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(retriever_name_or_path, config\u001b[38;5;241m=\u001b[39mconfig)\n",
      "File \u001b[0;32m/work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages/transformers/utils/import_utils.py:1058\u001b[0m, in \u001b[0;36mrequires_backends\u001b[0;34m(obj, backends)\u001b[0m\n\u001b[1;32m   1056\u001b[0m failed \u001b[38;5;241m=\u001b[39m [msg\u001b[38;5;241m.\u001b[39mformat(name) \u001b[38;5;28;01mfor\u001b[39;00m available, msg \u001b[38;5;129;01min\u001b[39;00m checks \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m available()]\n\u001b[1;32m   1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m failed:\n\u001b[0;32m-> 1058\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(failed))\n",
      "\u001b[0;31mImportError\u001b[0m: \nRagRetriever requires the faiss library but it was not found in your environment. Checkout the instructions on the\ninstallation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones\nthat match your environment. Please note that you may need to restart your runtime after installation.\n"
     ]
    }
   ],
   "source": [
    "from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration\n",
    "\n",
    "# Load the tokenizer, retriever, and model\n",
    "tokenizer = RagTokenizer.from_pretrained(\"facebook/rag-token-nq\")\n",
    "retriever = RagRetriever.from_pretrained(\"facebook/rag-token-nq\", use_dummy_dataset=True)\n",
    "model = RagSequenceForGeneration.from_pretrained(\"facebook/rag-token-nq\", retriever=retriever)\n",
    "\n",
    "# Define the input query\n",
    "input_query = \"What is the capital of France?\"\n",
    "\n",
    "# Tokenize the input query\n",
    "input_ids = tokenizer(input_query, return_tensors=\"pt\").input_ids\n",
    "\n",
    "# Generate the response\n",
    "output_ids = model.generate(input_ids)\n",
    "\n",
    "# Decode the generated response\n",
    "response = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]\n",
    "\n",
    "print(\"Query:\", input_query)\n",
    "print(\"Response:\", response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e4f9b16-d57e-4919-a212-d8d22ce8de49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "!cd ..\n",
    "data_path = \"Inference/gs_inferences/torgo_F01_test.pt\"\n",
    "data   = torch.load(data_path,map_location=torch.device('cpu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91d13078-9edc-486d-aaa3-052aa13bd8b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/work/van-speech-nlp/jindaznb/jslpnb/mllm_expriments/Whispering-LLaMA\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dfd300f7-58a2-46a0-b22c-02aeed1607fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "460"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "37b956b1-0fac-4d45-bd19-3e4034e9ead2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "460"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "!cd ..\n",
    "data_path = \"Inference/gs_inferences/torgo_F01_val.pt\"\n",
    "data   = torch.load(data_path,map_location=torch.device('cpu'))\n",
    "\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efe550a1-1a37-402d-b2a7-9ddf57db8d74",
   "metadata": {},
   "outputs": [],
   "source": [
    "!speaker_id='F01' && python training/WL-S-Copy1.py --lr 1e-3 \\\n",
    "    --d 1 \\\n",
    "    --pretrained_path 'weights/alpaca.pth' \\\n",
    "    --tokenizer_path 'weights/tokenizer.model' \\\n",
    "    --data 'Inference/gs_inferences/torgo_${speaker_id}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48936f7f-0977-4e21-8608-872f940f52b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ID': 'YOU0000000145_S0000030',\n",
      " 'Unnamed: 0': 0,\n",
      " 'audio_time': 1.67999267578125,\n",
      " 'category': 25,\n",
      " 'category_name': 'Entertainment',\n",
      " 'ground_truth': 'so close to my mouth.',\n",
      " 'hypothesis_1': 'its so close to my mouth',\n",
      " 'hypothesis_10': 'socialist tomorrow',\n",
      " 'hypothesis_11': 'suitable so close to my mouth',\n",
      " 'hypothesis_12': 'sılcostzamerio',\n",
      " 'hypothesis_13': 'thats so close to my mouth',\n",
      " 'hypothesis_14': None,\n",
      " 'hypothesis_15': None,\n",
      " 'hypothesis_16': None,\n",
      " 'hypothesis_17': None,\n",
      " 'hypothesis_18': None,\n",
      " 'hypothesis_19': None,\n",
      " 'hypothesis_2': 'seriously sewing',\n",
      " 'hypothesis_20': None,\n",
      " 'hypothesis_3': 'so close to mama',\n",
      " 'hypothesis_4': 'so close to my mall',\n",
      " 'hypothesis_5': 'so close to my mouth',\n",
      " 'hypothesis_6': 'so close to my mouth butt',\n",
      " 'hypothesis_7': 'so close to my mouth second up',\n",
      " 'hypothesis_8': 'so close to my mouth!',\n",
      " 'hypothesis_9': 'so close to your mouth',\n",
      " 'source': 2}\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "from pprint import pprint\n",
    "\n",
    "# Load the HyPoradise-v1-GigaSpeech dataset\n",
    "dataset = load_dataset(\"PeacefulData/HyPoradise-v1-GigaSpeech\")\n",
    "\n",
    "# Example: Print the first example from the train split\n",
    "pprint(dataset['train'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30085bff-9edb-4584-8e35-5be3a7884890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32739"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset['train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "245f4d59-bd8f-479e-9a13-b59509f351d8",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'test'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mlen\u001b[39m(\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n",
      "File \u001b[0;32m/work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages/datasets/dataset_dict.py:59\u001b[0m, in \u001b[0;36mDatasetDict.__getitem__\u001b[0;34m(self, k)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, k) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m     58\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(k, (\u001b[38;5;28mstr\u001b[39m, NamedSplit)) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 59\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     61\u001b[0m         available_suggested_splits \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     62\u001b[0m             split \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m (Split\u001b[38;5;241m.\u001b[39mTRAIN, Split\u001b[38;5;241m.\u001b[39mTEST, Split\u001b[38;5;241m.\u001b[39mVALIDATION) \u001b[38;5;28;01mif\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m     63\u001b[0m         ]\n",
      "\u001b[0;31mKeyError\u001b[0m: 'test'"
     ]
    }
   ],
   "source": [
    "len(dataset['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c118162-3812-42a9-b015-f0ef2895a866",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['Unnamed: 0', 'category_name', 'ground_truth', 'hypothesis_1', 'hypothesis_2', 'hypothesis_3', 'hypothesis_4', 'hypothesis_5', 'hypothesis_6', 'hypothesis_7', 'hypothesis_8', 'hypothesis_9', 'hypothesis_10', 'hypothesis_11', 'hypothesis_12', 'hypothesis_13', 'hypothesis_14', 'hypothesis_15', 'hypothesis_16', 'hypothesis_17', 'hypothesis_18', 'hypothesis_19', 'hypothesis_20', 'source', 'category', 'audio_time', 'ID'],\n",
       "        num_rows: 32739\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3575b5d3-b3cd-40b1-bcb7-dddc823736f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False  True  True  True  True  True  True False False False\n",
      "  True False  True  True False False False False  True False False False\n",
      " False False False False False False False False False False  True  True\n",
      "  True False  True  True False False  True False  True False False  True\n",
      "  True  True False  True  True False  True False False  True  True False\n",
      " False False  True  True False False False False False False  True False\n",
      "  True  True  True  True  True  True  True  True False  True  True False\n",
      " False False  True  True False  True  True False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False  True False  True False False False  True  True False\n",
      " False False False False False  True False False  True  True False False\n",
      "  True False False  True False False False False  True False False False\n",
      " False False False False False  True  True False False False False False\n",
      "  True  True False False False False False False False False False False\n",
      " False False False  True  True  True False False  True  True  True  True\n",
      "  True  True False  True False False False  True  True False False False\n",
      " False False  True False False  True  True  True False False False  True\n",
      "  True False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False]\n"
     ]
    }
   ],
   "source": [
    "import base64\n",
    "import numpy as np\n",
    "\n",
    "_ALIGNMENT_HEADS = {\n",
    "    \"tiny.en\": b\"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00\",\n",
    "    # Add other models as needed...\n",
    "}\n",
    "\n",
    "def get_alignment_heads(model_key):\n",
    "    if model_key not in _ALIGNMENT_HEADS:\n",
    "        raise ValueError(\"Model key not found in alignment heads dictionary\")\n",
    "    \n",
    "    encoded_string = _ALIGNMENT_HEADS[model_key]\n",
    "    # Decode the base85 string\n",
    "    decoded_bytes = base64.b85decode(encoded_string)\n",
    "    # Convert bytes to boolean array\n",
    "    bool_array = np.unpackbits(np.frombuffer(decoded_bytes, dtype=np.uint8)).astype(bool)\n",
    "    \n",
    "    return bool_array\n",
    "\n",
    "# Example usage\n",
    "model_key = \"tiny.en\"\n",
    "alignment_heads = get_alignment_heads(model_key)\n",
    "print(alignment_heads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "73839ff1-4ddc-40b1-8b09-91e6a463c6b2",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"NoneType\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHello, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      2\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43ma\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"NoneType\") to str"
     ]
    }
   ],
   "source": [
    "a = \"Hello, \"\n",
    "b = None\n",
    "\n",
    "result = a + b  # This will raise a TypeError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d09cca1-228a-4434-aae3-00ec366167a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34399674-9b84-4ce0-bd9a-e621d7efd071",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import whisper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "59f62b2e-2752-44b7-a17e-d7dd32c7fe98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting openai-whisper\n",
      "  Downloading openai-whisper-20231117.tar.gz (798 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m798.6/798.6 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: triton<3,>=2.0.0 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from openai-whisper) (2.1.0)\n",
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      "Requirement already satisfied: regex>=2022.1.18 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from tiktoken->openai-whisper) (2023.10.3)\n",
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      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (12.1.105)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (11.4.5.107)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (12.1.0.106)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (2.18.1)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from torch->openai-whisper) (12.1.105)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->openai-whisper) (12.4.127)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken->openai-whisper) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken->openai-whisper) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken->openai-whisper) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from requests>=2.26.0->tiktoken->openai-whisper) (2024.2.2)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from jinja2->torch->openai-whisper) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /work/van-speech-nlp/jindaznb/mmenv/lib/python3.9/site-packages (from sympy->torch->openai-whisper) (1.3.0)\n",
      "Building wheels for collected packages: openai-whisper\n",
      "  Building wheel for openai-whisper (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for openai-whisper: filename=openai_whisper-20231117-py3-none-any.whl size=801358 sha256=995b9db7711ce59e938a8c81e0fd0de2acb56fb588accc01dcfeb1a1ffdc6999\n",
      "  Stored in directory: /home/zhang.jinda1/.cache/pip/wheels/f5/77/96/4bb7b94449a47b726127100ad66bd72cba123fb4d0a8948473\n",
      "Successfully built openai-whisper\n",
      "Installing collected packages: openai-whisper\n",
      "Successfully installed openai-whisper-20231117\n"
     ]
    }
   ],
   "source": [
    "!pip install -U openai-whisper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "293195c3-f26f-4e75-96ee-80d16391e6a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import whisper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "751f2403-d3a6-4e62-a81f-43fce450009a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = whisper.load_model(\"large-v3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffcec749-9ed5-4bc1-b77e-1f1ae987c334",
   "metadata": {},
   "outputs": [],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b056be0-4da6-48e7-84f1-f207ae14b3d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load a Pre-Trained Checkpoint\n",
    "from transformers import WhisperForConditionalGeneration\n",
    "model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-tiny\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd8f6800-bd2b-4aaa-a51e-83f11aad440c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model.encoder.conv1.weight Parameter containing:\n",
      "tensor([[[-7.5645e-03, -7.5073e-03, -7.2098e-03],\n",
      "         [ 2.4460e-02,  1.7090e-02,  1.2566e-02],\n",
      "         [ 1.7639e-02,  1.3382e-02,  7.1297e-03],\n",
      "         ...,\n",
      "         [-4.5654e-02, -2.0615e-02, -1.8799e-02],\n",
      "         [-5.6152e-02, -3.0167e-02, -2.2278e-02],\n",
      "         [-9.1309e-02, -7.4463e-02, -6.0150e-02]],\n",
      "\n",
      "        [[ 1.9836e-03,  4.9531e-05,  8.9417e-03],\n",
      "         [ 3.6182e-03, -4.6921e-03,  7.0953e-03],\n",
      "         [ 1.3229e-02,  2.4738e-03,  1.4656e-02],\n",
      "         ...,\n",
      "         [ 1.0986e-02,  1.0757e-02, -6.6795e-03],\n",
      "         [ 7.4883e-03,  4.8370e-03, -1.3214e-02],\n",
      "         [ 2.4628e-02,  2.2186e-02,  2.5005e-03]],\n",
      "\n",
      "        [[ 2.1042e-02,  2.7985e-02,  3.2715e-02],\n",
      "         [-6.1737e-02, -7.9346e-02, -9.3628e-02],\n",
      "         [-2.3315e-02, -5.3467e-02, -5.6549e-02],\n",
      "         ...,\n",
      "         [-9.7942e-04, -8.1940e-03, -2.5139e-03],\n",
      "         [ 2.8801e-03, -4.2114e-03, -3.9139e-03],\n",
      "         [-9.0561e-03, -1.6479e-02, -7.9575e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.2102e-03,  6.6614e-04,  8.5688e-04],\n",
      "         [ 5.4665e-03,  8.7509e-03,  2.8877e-03],\n",
      "         [-7.7820e-03, -8.9493e-03, -1.0811e-02],\n",
      "         ...,\n",
      "         [-7.2718e-05, -3.3021e-04, -3.4356e-04],\n",
      "         [-4.9782e-03, -4.1771e-03, -2.1133e-03],\n",
      "         [ 1.2493e-03, -1.8320e-03, -4.9553e-03]],\n",
      "\n",
      "        [[-8.1024e-03, -1.2863e-02, -1.5129e-02],\n",
      "         [ 2.5139e-03,  1.2131e-03, -3.1719e-03],\n",
      "         [ 1.3000e-02,  1.4793e-02, -1.2589e-02],\n",
      "         ...,\n",
      "         [ 1.6907e-01, -2.1472e-01,  1.7609e-02],\n",
      "         [ 1.7908e-01, -1.8530e-01,  4.5013e-02],\n",
      "         [ 1.8347e-01, -2.7637e-01, -1.4488e-02]],\n",
      "\n",
      "        [[ 1.7776e-02,  9.4986e-03,  9.1648e-04],\n",
      "         [-5.1918e-03, -5.5275e-03, -1.7715e-02],\n",
      "         [-6.2561e-03, -7.9346e-03, -2.0325e-02],\n",
      "         ...,\n",
      "         [-1.6937e-02, -1.2215e-02, -7.4501e-03],\n",
      "         [ 2.3880e-03,  7.5150e-03,  6.7368e-03],\n",
      "         [-3.4180e-02,  4.1604e-04,  2.2171e-02]]])\n",
      "model.encoder.conv1.bias Parameter containing:\n",
      "tensor([-7.1387e-01, -6.8115e-01,  1.2305e-01, -3.7754e+00, -1.4954e-01,\n",
      "        -1.4551e-01, -1.8047e+00, -4.3701e-01,  1.8469e-01,  1.3965e-01,\n",
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      "        -4.8340e-02, -1.5906e-01,  1.8494e-02, -8.5693e-02, -1.8203e+00,\n",
      "        -2.1973e+00, -2.6807e-01,  2.7490e-01, -2.1448e-01,  1.5625e-01,\n",
      "        -1.2085e-01, -3.5133e-03,  3.8879e-02, -5.8936e-01,  7.0007e-02,\n",
      "        -2.1838e-01, -4.7539e+00,  2.7808e-01, -3.8848e+00, -1.8702e-03,\n",
      "        -5.2441e-01,  4.2432e-01,  4.3018e-01,  2.9761e-01,  4.7876e-01,\n",
      "        -2.3743e-02, -4.1479e-01, -8.7646e-02, -1.7078e-01, -1.3159e-01,\n",
      "        -3.7012e-01, -1.0098e+00,  1.2306e-02, -1.9561e+00, -1.0913e-01,\n",
      "         5.0537e-02, -2.5928e-01, -9.0088e-02,  4.1235e-01, -4.1089e-01,\n",
      "         1.9861e-01, -2.5604e-02, -8.2373e-01, -4.7510e-01, -3.3008e-01,\n",
      "        -7.8003e-02, -1.4441e-01, -6.3037e-01, -1.6089e-01, -7.7783e-01,\n",
      "        -1.3760e+00, -4.7632e-01,  4.4507e-01,  1.2549e-01, -1.8262e-01,\n",
      "        -1.1787e-02, -1.0439e+00, -1.7505e-01,  1.4795e-01, -5.4291e-02,\n",
      "        -8.2617e-01, -1.0498e+00, -1.1104e+00, -5.6885e-02, -1.1060e-01,\n",
      "         7.1228e-02,  1.8469e-01,  4.1138e-01,  1.2512e-01, -5.4150e-01,\n",
      "        -1.7798e-01,  4.2505e-01, -1.7993e-01, -1.1398e-02,  1.0986e-01,\n",
      "        -4.8462e-01, -3.3447e-01,  1.5549e-02,  3.3691e-01, -9.6631e-01,\n",
      "        -2.2363e-01, -1.8213e+00, -2.5488e-01, -3.8770e-01, -2.3975e-01,\n",
      "         4.5654e-02, -1.4624e-01,  1.7261e-01,  5.3070e-02,  1.3904e-01,\n",
      "        -1.8530e-01,  5.5713e-01, -5.1709e-01, -5.4932e-01, -3.3652e+00,\n",
      "         1.8652e-01,  1.2070e-02, -3.5205e-01, -5.7324e-01, -1.4130e-02,\n",
      "         6.7261e-02, -6.2598e-01, -5.9766e-01, -2.2919e-02,  6.3293e-02,\n",
      "         2.9449e-02, -2.3425e-01,  6.9031e-02, -3.7158e-01,  2.7603e-02,\n",
      "        -3.8457e+00,  2.2314e-01, -2.3303e-01,  2.1082e-01, -4.3701e-02,\n",
      "         4.1699e-01, -1.1982e+00, -1.2500e+00,  4.7729e-01, -1.4160e+00,\n",
      "        -4.9365e-01,  2.2864e-01, -1.2408e-01, -1.7148e+00, -1.1836e+00,\n",
      "         6.4575e-02,  2.8394e-01, -1.4248e+00, -7.5439e-02, -2.0862e-01,\n",
      "        -2.8000e-02, -6.1798e-02, -2.1973e-02, -2.4219e-01,  1.2372e-01,\n",
      "         2.1057e-01,  9.5166e-01, -2.9551e+00, -6.4697e-02, -8.7012e-01,\n",
      "        -2.1802e-01,  9.4971e-02,  1.1646e-01, -9.8975e-01, -1.2000e-01,\n",
      "         8.6304e-02,  3.4332e-02, -1.1292e-01,  1.4001e-01, -1.9299e-01,\n",
      "        -3.8379e-01,  1.7059e-02, -2.1855e+00, -9.3262e-01, -1.5465e-02,\n",
      "        -1.1884e-01, -2.3975e-01,  6.5723e-01, -1.4270e-01, -3.9551e-01,\n",
      "         8.1253e-03, -2.3462e-01, -9.8267e-02, -1.0342e+00, -2.4133e-01,\n",
      "         2.0276e-01, -1.0762e+00, -2.6953e-01,  1.5051e-01, -1.7883e-01,\n",
      "        -3.6896e-02,  3.9648e-01, -1.3745e-01, -8.8477e-01, -3.1097e-02,\n",
      "         1.0376e-02, -4.1455e-01,  1.2659e-01, -1.0615e+00, -2.8259e-02,\n",
      "        -2.8564e-01, -4.4653e-01,  3.1403e-02, -4.7638e-02, -2.3206e-01,\n",
      "         1.7761e-01, -6.3965e-02, -1.2207e-01,  4.2175e-02, -5.3076e-01,\n",
      "         3.3203e-02, -1.8347e-01,  1.3574e-01, -2.0654e-01,  6.9824e-02,\n",
      "        -5.9143e-02, -2.3413e-01, -1.3721e-01, -8.1848e-02, -2.3071e-01,\n",
      "         2.0691e-01, -1.1353e-02,  2.5513e-01, -7.5293e-01, -1.1017e-02,\n",
      "         8.6853e-02, -7.0312e-02, -1.3086e-01, -2.2485e-01,  1.0703e+00,\n",
      "         9.5581e-02,  5.0537e-01, -1.3123e-01, -2.4023e-01, -3.2837e-01,\n",
      "        -1.3271e+00, -8.4839e-02,  1.4832e-01, -2.5220e-01, -2.6880e-01,\n",
      "        -4.2227e+00, -1.0974e-01, -1.3379e-01,  1.4351e-02, -2.3730e-01,\n",
      "        -1.6467e-01,  8.4229e-02,  1.3147e-01,  1.6797e-01, -3.0640e-02,\n",
      "         2.0618e-01,  8.3862e-02, -3.1250e-01,  1.0513e-02,  5.6061e-02,\n",
      "        -1.3757e-01, -2.1790e-01, -1.0322e+00, -1.9189e-01, -2.0422e-01,\n",
      "        -6.0205e-01, -8.3594e-01, -3.0332e+00,  1.6321e-01, -2.2681e-01,\n",
      "         2.3413e-01,  4.5433e-03, -1.6821e-01, -4.5929e-02,  1.0614e-01,\n",
      "        -1.1172e+00, -5.8685e-02, -1.8994e-01,  2.4207e-01, -4.3320e+00,\n",
      "        -1.1943e+00, -2.7856e-01, -7.0435e-02, -1.0596e+00, -1.5466e-01,\n",
      "        -7.4280e-02, -2.4460e-02, -1.7139e-01, -3.0645e+00, -1.8359e-01,\n",
      "        -9.5032e-02,  1.9336e-01, -8.2275e-02, -5.1914e+00,  6.0028e-02,\n",
      "        -3.2129e-01, -1.9824e+00,  1.1023e-01, -1.0645e-01, -2.9037e-02,\n",
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      "        -4.5410e-01, -3.5083e-01, -1.8311e-01, -3.1299e-01])\n",
      "model.encoder.layers.0.self_attn_layer_norm.weight Parameter containing:\n",
      "tensor([ 1.7432e+00,  7.7148e+00,  1.8486e+00,  2.0527e+00,  2.0312e+00,\n",
      "         6.1670e-01,  1.8857e+00,  1.8496e+00,  1.1025e+00,  1.7939e+00,\n",
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      "         8.0109e-04,  1.6328e+00,  5.4297e-01,  1.8721e+00,  5.7422e-01,\n",
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      "         1.8398e+00,  2.0879e+00,  6.0352e-01,  3.4902e+00,  2.7051e+00,\n",
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      "         1.6816e+00, -1.3292e-05,  2.6426e+00,  2.4512e+00,  1.6348e+00,\n",
      "         2.2383e+00,  2.6387e+00,  3.3125e+00,  3.3535e+00,  3.6211e+00,\n",
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      "         7.0352e+00,  2.7109e+00,  2.6367e+00,  3.1504e+00,  2.7266e+00,\n",
      "         2.5430e+00,  2.3828e+00,  9.1211e-01,  2.5840e+00,  2.8477e+00,\n",
      "         2.5137e+00,  1.4404e+00,  4.5654e-01,  2.8398e+00,  3.3145e+00,\n",
      "         3.4609e+00,  3.4316e+00,  2.9316e+00,  2.5625e+00,  4.2422e+00,\n",
      "         6.5508e+00,  2.8672e+00,  2.5371e+00,  2.4863e+00,  1.1318e+00,\n",
      "         5.5078e-01,  2.3438e+00,  2.9648e+00,  2.4961e+00,  2.0137e+00,\n",
      "         2.2656e+00,  3.0801e+00,  3.1113e+00,  2.9883e+00])\n",
      "model.encoder.layers.0.self_attn_layer_norm.bias Parameter containing:\n",
      "tensor([ 7.1973e-01,  3.1738e+00,  8.7646e-01,  1.0010e+00,  7.1094e-01,\n",
      "         6.4941e-02,  8.4082e-01,  9.1504e-01,  4.2285e-01,  7.1924e-01,\n",
      "         2.7441e-01,  7.4072e-01,  7.4805e-01,  3.3374e-01, -1.2097e-01,\n",
      "         3.3472e-01,  4.3555e-01,  1.1689e+00,  6.9971e-01,  9.4238e-01,\n",
      "         3.8916e-01,  2.3889e-01,  2.9370e-01, -4.8804e-01,  2.7871e+00,\n",
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      "        -6.4087e-02, -3.2471e-02, -1.4435e-02,  4.4189e-02])\n",
      "model.encoder.layers.0.final_layer_norm.weight Parameter containing:\n",
      "tensor([ 6.5771e-01,  1.3096e+00,  1.4004e+00,  9.0820e-01,  1.2246e+00,\n",
      "         1.0977e+00,  1.4111e+00,  1.1201e+00,  1.5635e+00,  9.5508e-01,\n",
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      "         2.3379e+00,  1.6836e+00,  2.4980e+00,  2.2793e+00,  2.0625e+00,\n",
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      "         2.4375e+00,  2.7109e+00,  3.2402e+00,  2.7754e+00,  3.5977e+00,\n",
      "         2.6914e+00,  1.9531e+00,  2.9316e+00,  2.9551e+00,  2.2324e+00,\n",
      "         2.1836e+00,  2.5684e+00,  1.7959e+00,  2.9766e+00,  2.6934e+00,\n",
      "         2.9355e+00,  1.8955e+00,  1.8115e+00,  2.8281e+00])\n",
      "model.encoder.layers.0.final_layer_norm.bias Parameter containing:\n",
      "tensor([-1.2415e-01,  4.3140e-01, -2.0251e-01, -4.0723e-01, -1.9031e-01,\n",
      "         2.5342e-01,  2.0325e-01,  4.6875e-02,  5.1807e-01, -3.0029e-02,\n",
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      "         1.8860e-01, -7.4341e-02,  3.9154e-02,  1.9775e-01])\n",
      "model.encoder.layers.1.self_attn.out_proj.weight Parameter containing:\n",
      "tensor([[ 0.0159,  0.0091,  0.0128,  ..., -0.0059, -0.0140,  0.0018],\n",
      "        [ 0.0435, -0.0445,  0.0082,  ..., -0.0575, -0.0271, -0.0101],\n",
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      "        ...,\n",
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      "        [-0.0108,  0.0138, -0.0024,  ..., -0.0084, -0.0168, -0.0018]])\n",
      "model.encoder.layers.1.self_attn.out_proj.bias Parameter containing:\n",
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      "        -2.4817e-01, -1.5869e-01, -6.1941e-04, -1.1157e-01])\n",
      "model.encoder.layers.1.self_attn_layer_norm.weight Parameter containing:\n",
      "tensor([0.4429, 0.6558, 0.9897, 0.6309, 0.9189, 0.6812, 1.2061, 0.6074, 1.1328,\n",
      "        0.6094, 1.1641, 0.6299, 1.2969, 1.2705, 0.9297, 1.2627, 0.6108, 0.8657,\n",
      "        0.9590, 0.8232, 1.1426, 1.8271, 1.6445, 1.7002, 1.8945, 1.8174, 1.5508,\n",
      "        1.5498, 1.8682, 1.8281, 0.0269, 0.1274, 1.9180, 0.0321, 1.1357, 1.4883,\n",
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      "        1.3711, 1.4805, 2.0742, 1.6582, 1.8574, 1.4258, 1.1045, 1.5166, 1.3975,\n",
      "        1.0400, 1.2793, 1.6973, 1.7080, 1.6855, 0.8232, 1.6572, 1.6855, 1.6201,\n",
      "        1.9014, 2.1387, 1.2109, 1.8281, 1.5596, 1.6484, 1.8662, 1.7578, 1.6494,\n",
      "        1.1533, 1.8584, 1.6504, 1.6875, 1.9512, 1.1465, 1.2402, 1.7852, 1.7002,\n",
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      "        1.6719, 1.9082, 1.9453, 1.8330, 1.8027, 0.9438, 1.7803, 1.6699, 1.8330,\n",
      "        2.2090, 2.0781, 1.7988, 1.8428, 1.7734, 1.9287, 0.4895, 1.8887, 2.0488,\n",
      "        1.6484, 2.2266, 1.6143, 1.0391, 1.8350, 1.3682, 1.1172, 1.7812, 2.0000,\n",
      "        1.8330, 2.2227, 2.3672, 1.2783, 1.6016, 1.6309, 2.6367, 1.9297, 0.3552,\n",
      "        1.7959, 1.7617, 1.9463, 1.8535, 1.9229, 2.0586, 2.0234, 1.3125, 1.6963,\n",
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      "        1.8232, 1.8574, 1.6025, 1.6719, 1.6025, 2.0137, 1.4248, 2.4219, 2.9512,\n",
      "        1.4785, 2.3691, 2.0820, 2.1562, 2.2402, 2.0156, 1.7988, 2.5371, 1.8223,\n",
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      "        2.0312, 1.6357, 2.7422, 2.0137, 1.9658, 1.7871, 1.7959, 2.6973, 2.2148,\n",
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      "        0.0445, 1.4707, 1.3506, 1.2285, 0.5762, 1.1270, 1.9307, 0.8589, 1.6465,\n",
      "        1.6807, 1.9736, 1.5391, 1.5889, 1.5752, 2.0332, 1.4111, 1.9189, 1.7080,\n",
      "        1.5498, 1.4648, 1.3438, 1.3984, 1.1230, 1.5244, 1.5039, 1.7588, 1.6289,\n",
      "        1.7041, 1.3730, 1.5967, 1.5771, 1.2100, 1.9590, 1.2939, 1.5635, 2.0176,\n",
      "        1.7178, 1.7207, 1.6328, 1.8418, 1.8838, 1.1504, 1.9053, 1.8828, 1.3574,\n",
      "        1.4824, 1.6523, 1.7842, 1.1543, 1.2158, 0.3479, 0.6274, 1.1318, 1.8711,\n",
      "        1.3291, 1.5273, 1.8838, 1.5488, 1.5684, 1.2471, 1.7061, 1.9844, 1.7188,\n",
      "        1.8428, 2.2539, 1.3145, 1.7109, 2.1367, 0.8579, 1.8926, 1.7754, 1.1309,\n",
      "        2.2285, 1.8662, 1.4473, 1.6904, 1.7178, 1.8350, 1.4424, 1.4590, 1.9453,\n",
      "        1.8496, 1.7998, 2.1074, 2.5117, 1.8564, 1.6602, 1.6025, 1.5527, 2.2070,\n",
      "        1.4336, 1.4395, 1.8438, 1.7178, 2.0645, 1.7031, 2.1973, 2.4688, 1.3311,\n",
      "        1.7383, 2.2168, 1.3496, 1.4434, 2.1250, 1.8936, 2.0957, 2.0332, 1.2090,\n",
      "        1.3379, 1.9590, 2.1035, 1.3115, 2.5684, 1.8496, 1.8281, 1.2900, 2.3340,\n",
      "        2.7773, 2.0977, 1.9258, 2.1270, 2.9043, 1.0488, 1.0840, 1.9736, 1.5410,\n",
      "        2.1445, 2.5703, 2.5137, 2.9980, 2.1074, 1.5293, 2.2871, 2.5996, 2.3164,\n",
      "        2.3223, 2.2383, 0.7979, 1.9990, 1.1416, 1.6895, 2.3477, 2.0098, 2.3887,\n",
      "        2.5840, 1.9668, 2.3906, 2.6133, 2.5098, 2.2305, 1.4980, 2.3145, 1.7354,\n",
      "        2.3789, 2.3203, 2.7402, 2.3008, 1.5801, 2.6328])\n",
      "model.encoder.layers.1.self_attn_layer_norm.bias Parameter containing:\n",
      "tensor([-4.7290e-01, -3.4009e-01,  1.5649e-01, -4.9341e-01, -2.2815e-01,\n",
      "         4.8535e-01, -3.9868e-01,  1.3760e+00,  3.3789e-01,  3.7573e-01,\n",
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      "        -8.4521e-01,  4.2725e-01, -7.0679e-02, -3.8721e-01,  1.2286e-01,\n",
      "         1.1914e-01,  2.8882e-01, -4.0131e-02, -2.5635e-01,  1.1993e-01,\n",
      "        -2.8629e-03, -2.1805e-02, -1.1023e-01, -4.4212e-03,  9.5642e-02,\n",
      "        -4.2847e-02,  2.1265e-01, -5.4150e-01, -4.8804e-01,  6.7480e-01,\n",
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      "model.encoder.layers.1.final_layer_norm.weight Parameter containing:\n",
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      "         1.5850e+00,  1.5088e+00,  1.1289e+00,  1.6621e+00,  1.2500e+00,\n",
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      "         2.2422e+00,  2.1172e+00,  2.2188e+00,  2.2578e+00,  2.2637e+00,\n",
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      "         2.4277e+00,  2.1035e+00,  2.1094e+00,  2.5293e+00,  2.0566e+00,\n",
      "         2.2559e+00,  2.3418e+00,  3.5059e+00,  2.3066e+00,  2.1836e+00,\n",
      "         2.0625e+00,  3.2344e+00,  1.8662e+00,  2.3926e+00,  4.4043e-01,\n",
      "         2.2559e+00,  2.4219e+00,  2.0566e+00,  2.2188e+00,  2.7500e+00,\n",
      "         2.5234e+00,  2.5898e+00,  2.1289e+00,  2.2480e+00,  2.9219e+00,\n",
      "         3.4883e+00,  2.4883e+00,  2.2383e+00,  2.3906e+00,  2.2871e+00,\n",
      "         2.1973e+00,  2.2070e+00,  2.1035e+00,  2.6914e+00])\n",
      "model.encoder.layers.1.final_layer_norm.bias Parameter containing:\n",
      "tensor([ 1.5479e-01,  9.0869e-01, -1.2164e-01, -7.8552e-02, -5.2826e-02,\n",
      "         6.9189e-01, -4.7632e-01, -4.0796e-01, -2.8369e-01, -1.7041e-01,\n",
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      "         2.9639e-01,  1.2676e+00, -3.4790e-02, -1.0803e-01, -4.0771e-01,\n",
      "         2.3694e-01, -6.2561e-02, -1.0010e+00,  6.9873e-01,  2.1777e+00,\n",
      "        -3.5986e-01,  1.3984e+00, -9.9805e-01, -1.1406e+00, -5.1483e-02,\n",
      "         8.2458e-02, -5.8545e-01,  1.4075e-01, -1.8525e+00,  2.5137e+00,\n",
      "        -2.3223e+00,  6.5967e-01,  8.4900e-02, -1.3771e-02, -7.4414e-01,\n",
      "        -1.6182e+00, -1.4531e+00,  5.1465e-01,  3.0957e-01,  8.7500e-01,\n",
      "         5.3955e-01, -5.3125e-01,  1.7471e+00, -5.2155e-02,  1.9141e-01,\n",
      "        -2.1875e+00, -6.5234e-01,  2.0918e+00, -6.0498e-01,  1.1855e+00,\n",
      "        -6.5137e-01, -4.7656e-01, -1.0322e+00, -1.4697e-01,  1.5166e+00,\n",
      "        -1.2725e+00, -6.8359e-01,  2.9541e-01,  4.6069e-01,  4.6631e-02,\n",
      "         2.3496e+00,  8.1885e-01, -1.4824e+00,  1.2705e+00, -4.3604e-01,\n",
      "         5.3418e-01,  1.2018e-01,  7.8809e-01, -1.4639e+00,  5.1758e-01,\n",
      "        -4.1875e+00, -8.5840e-01, -4.9438e-01, -2.2617e+00,  2.1758e+00,\n",
      "         1.5264e+00,  6.2891e-01, -1.2236e+00,  4.0308e-01])\n",
      "model.encoder.layers.2.self_attn.k_proj.weight Parameter containing:\n",
      "tensor([[-0.0139, -0.0319,  0.0205,  ..., -0.0089, -0.0120, -0.0079],\n",
      "        [ 0.0061,  0.0374,  0.0062,  ..., -0.0059, -0.0089,  0.0091],\n",
      "        [ 0.0478,  0.0173,  0.0141,  ...,  0.0081, -0.0223,  0.0105],\n",
      "        ...,\n",
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      "        -2.5238e-02,  2.7051e-01,  1.2720e-01, -2.5610e-01,  3.0786e-01,\n",
      "        -1.4880e-01, -4.2749e-01,  3.4058e-01,  8.4000e-03,  1.7163e-01,\n",
      "         3.0472e-02, -4.1534e-02,  2.0117e-01,  1.9775e-01, -2.3669e-01,\n",
      "        -4.2285e-01, -1.1383e-01,  5.3310e-04,  2.2937e-01,  7.5134e-02,\n",
      "         7.9773e-02, -2.1338e-01, -1.4587e-01, -1.3379e-01,  7.5073e-02,\n",
      "         2.1082e-01, -1.7627e-01,  3.5187e-02, -1.2524e-01,  1.7371e-01,\n",
      "        -2.2888e-01, -1.1017e-01, -2.6123e-01,  1.3757e-01, -5.2399e-02,\n",
      "        -3.5431e-02,  2.3059e-01, -1.4185e-01,  6.9397e-02,  3.3630e-02,\n",
      "        -7.4463e-02, -4.4937e-03,  1.4087e-01,  3.7170e-02, -1.2238e-01,\n",
      "         1.0234e+00, -1.3147e-01, -4.9438e-02, -1.1072e-01, -5.6671e-02,\n",
      "         2.2546e-01,  3.1281e-02,  1.7578e-01,  1.0492e-01,  1.8823e-01,\n",
      "         2.1472e-01, -1.2402e-01, -1.4099e-01, -2.3413e-01, -2.9932e-01,\n",
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      "        -1.1511e-01,  1.0498e-01, -7.6904e-02,  1.6296e-01,  3.7378e-01,\n",
      "        -1.2091e-01,  1.6309e-01,  1.6968e-01,  8.1177e-03, -7.2388e-02,\n",
      "         1.7490e+00,  2.9150e-01, -2.1130e-01,  1.1063e-02,  4.9591e-02,\n",
      "         2.3590e-02,  2.0645e-02,  4.2023e-02,  4.7559e-01, -1.7224e-01,\n",
      "         1.5649e-01,  1.2549e-01,  1.0004e-01, -4.7803e-01, -2.3059e-01,\n",
      "         3.2349e-01, -1.8359e-01,  1.8994e-01, -1.3525e-01, -2.7734e-01,\n",
      "        -3.1616e-02,  8.3374e-02, -6.9519e-02,  2.0630e-01,  3.5449e-01,\n",
      "         1.1401e-01, -3.8269e-02,  4.9095e-03,  1.0278e-01,  1.2140e-01,\n",
      "        -1.2891e-01, -1.0986e-01,  5.3467e-02, -8.9172e-02,  9.2957e-02,\n",
      "         2.5342e-01,  2.4146e-01,  3.4058e-01, -1.8823e-01,  8.9478e-02,\n",
      "         7.8796e-02, -3.3228e-01, -4.0649e-02,  2.6929e-01, -5.5511e-02,\n",
      "        -9.8511e-02, -1.9263e-01,  3.3472e-01,  8.1329e-03, -2.1912e-02,\n",
      "         1.8555e-01,  2.7759e-01,  1.4297e-02,  1.9019e-01,  2.2827e-02,\n",
      "         7.1045e-02,  2.2522e-02,  1.4917e-01,  2.8061e-02, -3.5156e-02,\n",
      "        -3.7964e-02, -1.3318e-01,  4.6533e-01, -6.6467e-02, -3.7695e-01,\n",
      "        -3.9215e-02, -1.5083e-02,  1.3684e-01, -5.6250e-01, -7.7246e-01,\n",
      "        -2.1667e-01,  3.0200e-01,  8.9905e-02,  3.0249e-01,  1.9604e-01,\n",
      "        -1.3647e-01,  5.3749e-03,  1.2244e-01, -1.3062e-01,  8.3313e-02,\n",
      "         1.3989e-01, -1.9080e-01, -3.3545e-01, -1.0730e-01,  6.3867e-01,\n",
      "        -3.5840e-01,  2.9663e-01,  1.9629e-01,  2.8857e-01,  1.2042e-01,\n",
      "         6.3965e-02,  1.6541e-01, -1.9983e-01,  3.8696e-02, -3.0029e-01,\n",
      "        -2.0142e-01, -2.3755e-01,  6.5231e-03, -1.3562e-01,  1.7053e-01,\n",
      "        -5.3027e-01,  5.0201e-03, -3.2764e-01, -3.4546e-01,  5.3894e-02,\n",
      "         4.2310e-01, -2.1716e-01, -4.0479e-01,  2.3792e-01, -1.9458e-01,\n",
      "         1.9983e-01,  1.3940e-01, -1.7371e-01, -4.3115e-01,  1.3513e-01,\n",
      "        -3.3691e-01, -2.6660e-01,  1.4297e-02,  1.7776e-02,  3.4985e-01,\n",
      "        -6.8726e-02, -8.9722e-02, -6.3944e-04,  1.1639e-01, -4.6509e-02,\n",
      "        -1.9739e-01, -5.7465e-02, -1.1627e-01,  2.3792e-01,  6.3086e-01,\n",
      "        -2.1375e-01, -2.4341e-01,  1.1371e-01,  4.6387e-01, -1.8274e-01,\n",
      "         2.7939e-02, -1.5186e-01, -2.0386e-01, -3.3960e-01,  2.4951e-01,\n",
      "         5.3406e-02,  1.0126e-01, -2.8052e-01, -1.2903e-01, -1.9897e-01,\n",
      "         1.1951e-01,  1.8591e-01,  3.7262e-02,  5.9967e-03,  4.3774e-01,\n",
      "        -1.6858e-01,  9.5996e-01,  4.0253e-02,  1.9739e-01,  3.1281e-02,\n",
      "         1.4636e-01, -1.7014e-02, -3.3398e-01, -4.6661e-02,  2.8101e-01,\n",
      "        -2.2595e-01,  2.5537e-01,  3.3844e-02,  2.7222e-01,  4.4098e-02,\n",
      "         5.6580e-02,  1.7685e-02,  4.4556e-01, -6.4404e-01,  2.0435e-01,\n",
      "         1.2378e-01, -2.0850e-01,  5.3558e-02,  1.3354e-01, -4.1650e-01,\n",
      "        -2.8149e-01,  8.4229e-02,  3.0737e-01,  2.1375e-01,  4.4464e-02,\n",
      "        -2.7344e-02, -1.6467e-01,  7.7393e-02, -1.5027e-01, -1.1243e-01,\n",
      "        -3.1201e-01, -3.1104e-01,  3.0228e-02,  1.7822e-01, -6.5430e-02,\n",
      "         2.6685e-01,  1.5381e-01, -1.5588e-01, -3.4729e-02,  4.4250e-02,\n",
      "         2.1814e-01,  2.0471e-01,  2.6465e-01,  3.9819e-01, -1.1517e-01,\n",
      "         5.9204e-02,  2.1863e-01,  3.2031e-01,  1.5088e-01,  1.5869e-02,\n",
      "         2.7856e-01,  3.7158e-01,  6.2012e-01,  2.8101e-01,  1.9092e-01,\n",
      "         2.1912e-01, -1.8835e-01,  6.7871e-02, -6.6101e-02,  9.4757e-03,\n",
      "        -2.4707e-01,  2.5049e-01, -6.3232e-02, -2.7527e-02,  1.1890e-01,\n",
      "         7.7209e-02, -2.1082e-01,  2.2546e-01,  1.3538e-01, -5.8533e-02,\n",
      "        -4.5105e-02,  9.3140e-02,  9.9976e-02, -1.0620e-01, -1.0443e-01,\n",
      "         1.2769e-01, -4.7461e-01,  1.7822e-02,  1.5515e-01,  1.2622e-01,\n",
      "         2.2995e-02,  4.8401e-02, -1.6357e-01,  3.1055e-01, -1.6968e-01,\n",
      "         5.9662e-03,  7.1533e-02, -1.8359e-01, -2.7222e-01, -2.2327e-01,\n",
      "        -1.0034e-01,  4.3628e-01, -2.5659e-01, -1.1279e-01,  1.5210e-01,\n",
      "        -4.2578e+00, -1.8066e-01, -2.2656e-01, -7.9590e-02,  2.8706e-03,\n",
      "        -1.1513e-02,  8.7402e-02,  5.6305e-02,  9.3384e-02])\n",
      "model.encoder.layers.2.self_attn_layer_norm.weight Parameter containing:\n",
      "tensor([0.8530, 0.9399, 1.2295, 1.0039, 1.2139, 1.4102, 1.0566, 0.8745, 1.1582,\n",
      "        0.9790, 1.6621, 1.0254, 0.9604, 1.2402, 1.0811, 1.1006, 0.9727, 1.4004,\n",
      "        1.2881, 1.0557, 1.4932, 1.4844, 1.6016, 1.4863, 2.0762, 1.9092, 1.4785,\n",
      "        1.6152, 1.7051, 1.7988, 7.0234, 1.0322, 1.8594, 1.5068, 1.4863, 1.6660,\n",
      "        1.3848, 1.7070, 2.2070, 2.1934, 1.3789, 1.6250, 1.0439, 1.8574, 1.9805,\n",
      "        1.8779, 1.6680, 2.2715, 1.8154, 1.8457, 1.3369, 1.7109, 1.8203, 2.0059,\n",
      "        1.2900, 1.3770, 1.2412, 1.7119, 1.6143, 1.1943, 1.8701, 1.9443, 1.7764,\n",
      "        1.8701, 1.9121, 1.5732, 1.8711, 1.6709, 1.8408, 2.0918, 2.2637, 1.5195,\n",
      "        1.6436, 2.3516, 2.2891, 2.1172, 2.4336, 3.4453, 1.1631, 1.8770, 2.1699,\n",
      "        1.4209, 2.0781, 2.0254, 1.4434, 3.5234, 1.9209, 1.9268, 1.4639, 1.9102,\n",
      "        1.9912, 1.9961, 1.2666, 1.6221, 2.2305, 1.3281, 2.2793, 2.1523, 2.0566,\n",
      "        1.9834, 1.8662, 2.0137, 1.6943, 3.5195, 2.2812, 0.6528, 2.1152, 2.0820,\n",
      "        2.2266, 1.7529, 1.6426, 1.3027, 1.5898, 1.5771, 3.0488, 2.1113, 2.7871,\n",
      "        2.0508, 2.5430, 2.3867, 2.6016, 1.6543, 2.0371, 2.4395, 1.5771, 0.9658,\n",
      "        1.9219, 1.5703, 1.1592, 1.8711, 1.2422, 1.5645, 1.5957, 1.1924, 1.7148,\n",
      "        1.8428, 2.3926, 1.9297, 3.5801, 1.6865, 1.7217, 1.2529, 2.6426, 1.9102,\n",
      "        1.7471, 2.1152, 1.7979, 1.7656, 1.4160, 1.6943, 1.6074, 2.0898, 1.8350,\n",
      "        1.7441, 2.1465, 1.6992, 1.4590, 1.8887, 2.0059, 1.4512, 2.0176, 2.5449,\n",
      "        1.6172, 2.5000, 2.1602, 2.0312, 2.3223, 1.7764, 1.6016, 2.5449, 1.6514,\n",
      "        1.5430, 2.1230, 2.3066, 2.0000, 1.5342, 1.9150, 2.8809, 1.1465, 2.4160,\n",
      "        2.1094, 1.6357, 1.6963, 1.8105, 1.7422, 1.7363, 2.2246, 2.2363, 2.7422,\n",
      "        1.6797, 2.1719, 2.0254, 0.9058, 0.8428, 1.0654, 1.0176, 0.8765, 1.2529,\n",
      "        1.0586, 1.3408, 1.4092, 0.9077, 1.4854, 1.1025, 1.3203, 1.5791, 1.1748,\n",
      "        1.0576, 1.2812, 2.5254, 1.0283, 1.0996, 1.1152, 1.3945, 1.5127, 1.3018,\n",
      "        1.8291, 2.1582, 1.2236, 1.6758, 1.8906, 1.7188, 7.0586, 1.0342, 2.0840,\n",
      "        1.3467, 1.8184, 1.3613, 1.3379, 0.8804, 1.4873, 2.0977, 1.1953, 1.4180,\n",
      "        1.4385, 1.8945, 1.8125, 1.6484, 1.7559, 2.6270, 1.8857, 2.4570, 1.8115,\n",
      "        1.4990, 1.4346, 1.8125, 1.8965, 1.3125, 1.4678, 1.7598, 2.4395, 1.6250,\n",
      "        1.9492, 1.2363, 1.1299, 1.6611, 1.1611, 1.6533, 1.4473, 1.4424, 1.8125,\n",
      "        1.4316, 2.2051, 1.6133, 2.0996, 1.7080, 1.8721, 2.1602, 2.0742, 1.6211,\n",
      "        1.3701, 1.7988, 2.0137, 3.3750, 1.7334, 0.9185, 0.7651, 1.7959, 1.4580,\n",
      "        1.7598, 1.6660, 1.8525, 1.6484, 2.0742, 1.2051, 2.1797, 1.6943, 1.3799,\n",
      "        2.1191, 2.1797, 2.7617, 2.0078, 1.8008, 0.7715, 2.2344, 1.4912, 1.5234,\n",
      "        2.2617, 1.9023, 1.5498, 1.6748, 1.8389, 1.5977, 2.0664, 1.6621, 2.2207,\n",
      "        1.7529, 1.5908, 2.2715, 1.5000, 3.0566, 1.2695, 1.4326, 2.0566, 2.7695,\n",
      "        1.6494, 1.9590, 1.8359, 2.3535, 2.2051, 1.8086, 2.6699, 2.4961, 1.6045,\n",
      "        1.6807, 2.2695, 1.7646, 1.9492, 1.7607, 2.0645, 2.0488, 2.1934, 1.4697,\n",
      "        1.3311, 1.4541, 1.9707, 2.1484, 4.1406, 2.0371, 1.8555, 1.5176, 1.8047,\n",
      "        2.2402, 2.2891, 1.7578, 2.1543, 1.5352, 1.4541, 0.8774, 2.3906, 2.0098,\n",
      "        2.4141, 2.1816, 2.1973, 2.4473, 2.1445, 2.1250, 3.3242, 2.5742, 1.8145,\n",
      "        2.1855, 2.8184, 1.1855, 2.7324, 3.0566, 1.2373, 1.9688, 1.5234, 1.9238,\n",
      "        3.1445, 2.0410, 2.6387, 2.2715, 2.2773, 2.5645, 1.0527, 1.9229, 2.1445,\n",
      "        1.6240, 1.9775, 1.8594, 2.3516, 1.8457, 3.3594])\n",
      "model.encoder.layers.2.self_attn_layer_norm.bias Parameter containing:\n",
      "tensor([-4.3701e-02,  9.0210e-02,  4.0375e-02,  4.2773e-01, -2.3047e-01,\n",
      "        -1.2482e-01, -1.0195e+00,  1.1143e+00, -1.8481e-01,  1.1127e-01,\n",
      "        -2.5830e-01,  2.3267e-01,  5.6543e-01,  6.0205e-01, -2.7148e-01,\n",
      "        -1.2305e-01,  2.2363e-01, -6.9883e+00, -1.2286e-01,  3.8354e-01,\n",
      "        -6.0596e-01,  9.4385e-01,  5.6396e-01, -3.9209e-01, -1.0229e-01,\n",
      "         4.3530e-01,  4.4946e-01, -5.2856e-02, -7.8467e-01, -9.0271e-02,\n",
      "        -1.1096e-01, -5.1904e-01, -1.0732e+00, -3.0054e-01, -4.8645e-02,\n",
      "         6.8787e-02, -6.4453e-01, -5.4834e-01,  8.0505e-02,  3.4961e-01,\n",
      "        -9.5032e-02, -3.0444e-01, -2.8589e-01,  2.0044e-01, -1.4331e-01,\n",
      "        -5.0830e-01, -1.1934e+00,  5.0049e-01,  2.2839e-01,  5.5859e-01,\n",
      "         2.0349e-01, -3.2617e-01,  8.9209e-01, -3.9331e-01, -6.2866e-02,\n",
      "         8.6816e-01, -7.8906e-01, -5.4883e-01, -9.5996e-01,  1.7053e-01,\n",
      "         3.3765e-01,  1.7493e-01, -2.7542e-02, -5.3711e-02, -6.5430e-02,\n",
      "        -1.5635e+00,  2.4280e-01, -6.9763e-02,  3.3228e-01,  4.7681e-01,\n",
      "        -1.3513e-01,  1.4551e-01, -1.0291e-01,  3.6353e-01,  1.6638e-01,\n",
      "        -2.9614e-01, -2.2021e-01,  1.1285e-01,  3.2495e-01,  2.9224e-01,\n",
      "         4.5105e-02, -1.9226e-01, -2.2412e-01,  6.0400e-01, -4.9780e-01,\n",
      "        -9.6436e-03, -3.1836e-01, -3.3691e-01,  8.1592e-01,  5.8057e-01,\n",
      "         1.3535e+00,  1.3794e-01,  1.6572e+00, -7.7246e-01, -1.5112e-01,\n",
      "         2.5898e+00,  6.6699e-01,  6.8115e-02,  3.5547e-01,  8.4668e-01,\n",
      "        -4.1290e-02,  6.6406e-01,  5.0488e-01,  2.3206e-01,  1.7224e-01,\n",
      "         1.4062e-01,  2.0776e-01, -8.9905e-02, -5.5176e-01, -1.2764e+00,\n",
      "         3.3057e-01,  1.3438e+00,  1.4124e-01, -7.5000e-01,  2.5833e-02,\n",
      "         5.3125e-01,  4.5190e-01, -2.6147e-01,  3.4375e-01, -4.1748e-01,\n",
      "        -3.2495e-01,  2.9517e-01,  2.9688e-01,  4.3488e-02, -1.5635e+00,\n",
      "         8.1934e-01,  6.0400e-01, -6.7334e-01,  2.0935e-01,  3.4180e-01,\n",
      "         3.1174e-02,  1.3789e+00,  4.2969e-01,  1.5352e+00,  4.1431e-01,\n",
      "         4.2993e-01, -3.7817e-01, -2.3450e-01,  8.2581e-02,  6.8750e-01,\n",
      "        -1.8079e-01,  6.3818e-01, -2.0667e-01,  6.3135e-01,  2.6343e-01,\n",
      "         3.4595e-01, -5.0720e-02, -1.4294e-01,  1.6543e+00,  8.3594e-01,\n",
      "         1.0312e+00, -8.1238e-02,  3.6792e-01,  2.0142e-01, -3.4424e-01,\n",
      "         4.7681e-01,  8.8574e-01,  4.7607e-01,  5.6641e-01,  9.7656e-01,\n",
      "         7.2217e-01,  1.0199e-01,  1.5547e+00,  3.0594e-02,  3.6255e-01,\n",
      "         2.3669e-01, -2.6147e-01, -1.1758e+00,  1.2080e+00,  1.4001e-01,\n",
      "        -1.1113e+00, -6.5369e-02, -1.0175e-01,  1.4539e-01, -4.4019e-01,\n",
      "         1.8604e-01,  4.2432e-01, -1.6504e-01,  1.0400e+00,  3.5083e-01,\n",
      "        -1.0215e+00, -5.6122e-02, -5.5615e-01, -7.9980e-01,  1.1445e+00,\n",
      "        -8.4131e-01,  2.2546e-01, -3.9648e-01, -1.6651e-03, -1.6973e+00,\n",
      "        -2.1924e-01,  5.5957e-01, -1.3953e-01, -8.1445e-01, -3.1177e-01,\n",
      "        -2.2046e-01, -2.7368e-01,  1.1094e+00,  6.7090e-01, -3.4009e-01,\n",
      "         3.7061e-01,  5.9277e-01, -1.5320e-01,  1.2001e-02, -9.9561e-01,\n",
      "        -6.0303e-02,  1.0168e-01, -6.3086e-01,  2.6758e-01,  7.9395e-01,\n",
      "        -4.6948e-01,  5.3955e-01, -1.9287e-01,  2.5659e-01,  6.2744e-01,\n",
      "         3.5156e-02, -6.3379e-01, -9.1675e-02, -1.1902e-01,  3.0127e-01,\n",
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      "        -7.4890e-02,  4.1650e-01, -1.9424e-02,  3.4277e-01])\n",
      "model.encoder.layers.2.final_layer_norm.weight Parameter containing:\n",
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      "        3.4824e+00, 3.1875e+00, 3.1719e+00, 3.1660e+00, 3.0742e+00, 3.0879e+00])\n",
      "model.encoder.layers.2.final_layer_norm.bias Parameter containing:\n",
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      "         1.4980e+00, -2.4341e-01, -1.6885e+00, -5.8936e-01])\n",
      "model.encoder.layers.3.self_attn.k_proj.weight Parameter containing:\n",
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      "model.encoder.layers.3.self_attn.v_proj.weight Parameter containing:\n",
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      "model.encoder.layers.3.self_attn.v_proj.bias Parameter containing:\n",
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      "        -1.2128e-01,  8.6914e-02, -8.7830e-02, -3.6907e-03])\n",
      "model.encoder.layers.3.self_attn_layer_norm.weight Parameter containing:\n",
      "tensor([1.2715, 2.0801, 1.8135, 1.5029, 1.8037, 2.0820, 1.4648, 1.3320, 1.7900,\n",
      "        2.1914, 1.7930, 1.8057, 1.9160, 1.7656, 1.9258, 1.4775, 1.5850, 2.2695,\n",
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      "        2.0762, 2.8672, 2.4102, 2.5742, 2.1289, 2.0137, 2.6113, 2.3555, 1.8398,\n",
      "        2.1230, 1.9932, 2.1797, 2.2031, 2.4297, 2.4512, 2.4980, 1.8281, 2.7305,\n",
      "        1.8301, 1.8301, 2.4395, 2.0293, 2.7598, 2.3125, 2.5254, 2.8418, 1.8008,\n",
      "        2.3281, 2.8535, 2.7969, 2.2695, 1.8223, 2.2578, 1.2061, 2.9707, 2.3633,\n",
      "        2.4141, 2.7090, 2.0840, 2.5879, 2.6973, 2.1992, 3.2227, 2.7676, 2.1094,\n",
      "        2.4570, 3.0020, 2.6738, 2.6035, 5.2070, 2.0215, 2.3945, 2.1660, 2.2324,\n",
      "        2.4668, 2.5312, 2.8750, 2.1445, 2.5918, 2.1582, 2.3223, 2.7812, 2.4160,\n",
      "        1.9678, 2.5742, 2.2441, 2.4258, 2.6621, 2.6387])\n",
      "model.encoder.layers.3.self_attn_layer_norm.bias Parameter containing:\n",
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      "        -6.5979e-02,  9.6143e-01, -7.5928e-01,  4.5361e-01,  1.1631e+00,\n",
      "         4.1504e-01,  1.3562e-01,  1.5176e+00, -6.8457e-01, -1.1406e+00,\n",
      "        -7.2500e+00, -5.2783e-01,  7.7783e-01, -3.9375e+00,  7.1240e-01,\n",
      "         9.0918e-01, -4.5923e-01, -1.0098e+00, -9.1992e-01])\n",
      "model.encoder.layers.3.fc1.weight Parameter containing:\n",
      "tensor([[ 8.1940e-03, -5.0293e-02,  5.9113e-02,  ...,  5.7745e-04,\n",
      "          1.8204e-02,  1.1505e-02],\n",
      "        [ 1.0192e-05, -8.9264e-03,  3.4424e-02,  ..., -1.4687e-02,\n",
      "          1.2283e-02,  2.8152e-03],\n",
      "        [-2.9724e-02, -1.0017e-02, -1.5747e-02,  ..., -5.2307e-02,\n",
      "         -8.0442e-04,  1.9623e-02],\n",
      "        ...,\n",
      "        [ 2.8976e-02, -8.3130e-02,  4.2969e-02,  ...,  6.2752e-03,\n",
      "         -6.1750e-05, -6.1417e-03],\n",
      "        [ 1.2062e-02, -4.5746e-02, -1.3008e-02,  ...,  8.2825e-02,\n",
      "         -2.8458e-02,  7.1716e-02],\n",
      "        [ 2.3346e-03, -3.2227e-02,  1.1803e-02,  ..., -1.4200e-03,\n",
      "          4.7112e-03,  5.5466e-03]])\n",
      "model.encoder.layers.3.fc1.bias Parameter containing:\n",
      "tensor([-3.7500, -2.1816, -3.3770,  ..., -3.0449, -2.7930, -2.1172])\n",
      "model.encoder.layers.3.fc2.weight Parameter containing:\n",
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      "model.encoder.layers.3.fc2.bias Parameter containing:\n",
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      "        -2.2891e+00,  1.3047e+00,  1.6084e+00,  1.6416e+00])\n",
      "model.encoder.layers.3.final_layer_norm.weight Parameter containing:\n",
      "tensor([ 9.8516e+00,  1.1117e+01,  1.0008e+01,  1.0391e+01,  9.8516e+00,\n",
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      "         1.4031e+01,  1.1570e+01,  1.2258e+01,  1.3609e+01,  1.3977e+01,\n",
      "         2.3965e+00,  1.3008e+01,  1.2641e+01,  1.0766e+01,  1.2758e+01,\n",
      "         1.2367e+01,  1.2805e+01,  1.0945e+01,  1.2391e+01])\n",
      "model.encoder.layers.3.final_layer_norm.bias Parameter containing:\n",
      "tensor([ 9.7363e-01,  9.9766e+00, -1.2754e+00,  3.5684e+00,  1.3389e+00,\n",
      "         8.3359e+00, -5.3750e+00, -9.7969e+00, -3.5645e+00, -1.1852e+01,\n",
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      "        -3.7930e+00,  1.2939e+00,  3.7695e+00, -1.1273e-01,  1.1973e+00,\n",
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      "        -1.3418e+00, -1.7090e+00, -2.3359e+00, -3.5605e+00, -4.2148e+00,\n",
      "        -1.1768e+00, -7.1367e+00, -6.0508e+00, -1.1436e+00,  7.0557e-01,\n",
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      "         9.6924e-02, -6.5508e+00,  6.1484e+00,  9.7217e-01, -9.3125e+00,\n",
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      "         1.7949e+00, -6.5625e+00, -4.3203e+00, -9.5996e-01,  4.7998e-01,\n",
      "         6.8896e-01,  1.9209e+00,  3.5938e+00,  8.1562e+00,  7.2305e+00,\n",
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      "        -9.1797e+00,  5.5156e+00,  3.2812e+00,  2.0137e+00,  4.0117e+00,\n",
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      "         3.3047e+00, -1.2549e+00, -4.1321e-02,  4.7969e+00,  2.0723e+00,\n",
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      "        -3.4727e+00,  2.5469e+00, -4.4023e+00, -1.5029e+00, -2.8340e+00,\n",
      "         2.0156e+00,  2.8320e+00, -3.3262e+00,  3.3652e+00,  4.4805e+00,\n",
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      "        -2.0430e+00, -1.6138e-01, -4.7734e+00, -1.6279e+00, -2.1387e+00,\n",
      "         6.5479e-01,  7.1992e+00, -1.6592e+00, -9.1719e+00,  1.1633e+01,\n",
      "         8.5625e+00, -2.0332e+00,  4.2344e+00,  4.2344e+00, -2.3359e+00,\n",
      "         2.2012e+00,  4.1641e+00, -2.2344e+00, -9.1602e-01, -4.4336e+00,\n",
      "        -6.8311e-01,  1.6543e+00, -4.7510e-01,  1.0469e+00, -1.4746e-01,\n",
      "         1.3926e+00,  2.3672e+00, -8.5352e-01,  5.0703e+00,  1.8496e+00,\n",
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      "         4.0352e+00, -2.6250e+00, -7.5039e+00,  4.4570e+00,  4.1875e+00,\n",
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      "        -7.6406e+00, -3.5996e+00, -1.1953e+01, -4.9336e+00, -1.5586e+00,\n",
      "        -3.8892e-01, -1.4624e-01, -3.4180e+00,  2.2305e+00, -7.3008e+00,\n",
      "         1.6553e-01, -6.1992e+00, -3.0293e+00,  2.4043e+00,  2.1191e+00,\n",
      "        -4.9268e-01, -7.3633e+00,  7.7383e+00,  2.6348e+00, -2.2266e+00,\n",
      "        -6.5508e+00,  4.5156e+00,  4.6211e+00,  5.3594e+00,  3.8105e+00,\n",
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      "        -9.9609e+00, -7.8359e+00, -6.8828e+00, -2.9492e+00,  3.3672e+00,\n",
      "         3.4219e+00, -1.0156e+00, -4.3408e-01,  3.8379e+00, -8.8525e-01,\n",
      "         4.1641e+00, -3.4546e-01, -2.1716e-01,  3.3594e-01, -1.9453e+00,\n",
      "        -5.4004e-01,  1.5635e+00,  3.9883e+00, -4.4102e+00, -2.6699e+00,\n",
      "         1.0771e+00, -4.5000e+00, -3.0391e+00, -4.7695e+00, -7.4102e+00,\n",
      "        -9.5032e-02, -6.0156e+00, -3.4141e+00, -3.8262e+00, -1.9688e+00,\n",
      "        -1.2793e+00,  5.2344e-01, -2.1521e-01, -4.9146e-01, -2.6094e+00,\n",
      "         2.5371e+00,  6.0625e+00, -6.2422e+00, -2.3633e+00,  2.5488e+00,\n",
      "         9.9902e-01, -3.7402e+00,  2.5625e+00,  2.3169e-01, -5.5039e+00,\n",
      "         9.7188e+00, -1.8809e+00, -1.0654e+00, -2.6719e+00,  1.4541e+00,\n",
      "         1.0068e+00, -4.5820e+00, -4.8945e+00, -3.7988e+00,  3.1836e-01,\n",
      "        -2.9082e+00, -6.8203e+00, -5.3281e+00, -7.2109e+00,  4.7422e+00,\n",
      "        -5.6914e+00,  6.1875e+00,  1.4668e+00, -1.0449e+00, -2.8418e+00,\n",
      "         4.3672e+00, -2.2168e+00,  1.0010e+00,  8.9697e-01,  2.2969e+00,\n",
      "        -4.2500e+00,  8.0312e+00,  8.3438e+00, -4.3711e+00, -3.3809e+00,\n",
      "         1.9629e+00,  3.1934e+00,  7.7686e-01,  1.8018e+00,  7.6055e+00,\n",
      "        -4.9072e-02,  5.4531e+00, -3.1104e-01,  1.2461e+00,  3.8691e+00,\n",
      "        -2.2266e+00,  2.0645e+00,  1.0102e+01, -4.5586e+00,  2.6588e-03,\n",
      "         8.1094e+00, -6.7920e-01, -3.7910e+00,  2.6484e+00,  4.3091e-01,\n",
      "         2.4062e+00, -2.0547e+00,  4.8203e+00, -1.2324e+00, -2.1250e+00,\n",
      "         7.8906e-01, -2.7783e-01,  2.9219e+00, -3.1523e+00, -1.0516e-01,\n",
      "         6.8047e+00, -4.1328e+00, -5.6211e+00, -5.4805e+00])\n",
      "model.encoder.layer_norm.weight Parameter containing:\n",
      "tensor([1.1807, 1.3760, 1.2461, 1.2412, 1.2871, 1.8799, 1.1787, 1.0176, 1.3965,\n",
      "        1.4307, 1.3545, 1.4131, 1.2891, 1.3496, 1.3916, 1.2168, 1.2734, 2.2656,\n",
      "        1.4219, 1.4834, 1.2725, 1.4111, 1.3535, 1.4834, 1.5049, 1.3457, 1.4531,\n",
      "        1.4180, 1.2373, 1.4883, 1.5615, 1.3408, 1.4756, 1.3262, 1.3750, 1.4131,\n",
      "        1.2197, 1.6055, 1.4932, 1.4932, 1.2881, 1.5400, 1.3027, 1.2822, 1.3896,\n",
      "        1.4854, 1.5215, 1.4033, 1.3965, 1.4736, 1.5225, 1.1182, 1.2090, 1.5449,\n",
      "        1.4678, 1.3105, 1.5205, 1.4385, 1.2705, 1.4424, 1.5039, 1.5068, 1.4854,\n",
      "        1.5957, 1.2852, 1.2402, 1.4805, 1.4346, 1.5889, 1.2207, 1.5215, 0.9653,\n",
      "        1.9160, 1.9004, 1.6182, 1.5020, 1.4258, 3.5723, 1.1738, 1.6465, 1.4805,\n",
      "        1.4258, 2.2793, 1.8975, 1.5557, 2.9395, 1.5859, 1.9492, 1.3750, 1.4707,\n",
      "        1.2529, 2.0098, 1.3906, 1.4961, 1.5029, 1.6895, 1.2383, 1.4521, 1.7451,\n",
      "        1.3535, 1.4082, 1.6113, 1.5537, 2.9629, 1.5049, 1.1016, 1.5186, 1.6182,\n",
      "        1.8643, 1.4844, 1.5303, 1.6641, 1.7471, 1.3389, 3.7266, 1.5078, 1.5928,\n",
      "        1.5322, 1.7168, 1.2500, 1.5469, 1.5029, 1.4346, 1.6279, 1.3408, 1.0400,\n",
      "        1.4824, 1.5566, 0.9355, 1.5996, 1.0391, 1.2754, 1.4639, 1.2959, 1.4365,\n",
      "        1.5361, 1.3086, 1.4785, 1.4346, 1.4570, 1.1328, 1.1934, 1.4795, 1.4238,\n",
      "        1.1162, 1.5303, 1.2998, 1.4316, 1.2891, 1.5459, 1.4385, 1.5195, 1.5898,\n",
      "        1.1953, 1.5137, 1.2969, 1.5635, 1.3818, 1.6494, 1.2500, 1.6865, 1.5664,\n",
      "        1.4131, 1.6094, 1.6582, 1.4189, 1.2090, 1.5645, 1.4414, 1.5576, 1.4053,\n",
      "        1.4775, 1.5957, 1.6299, 1.4717, 1.4668, 1.3096, 1.4541, 1.5010, 1.3574,\n",
      "        1.6074, 1.6279, 1.1777, 1.3477, 1.3545, 1.5146, 1.5615, 1.3457, 1.6943,\n",
      "        1.5391, 1.6230, 2.0137, 1.2266, 0.9473, 0.7192, 1.3174, 1.2607, 1.3018,\n",
      "        1.3047, 1.2871, 1.4336, 1.2578, 1.2773, 1.3516, 1.2920, 1.4189, 1.3066,\n",
      "        1.3359, 1.1572, 3.4219, 1.2773, 1.3457, 1.3389, 1.1240, 1.4121, 1.3193,\n",
      "        1.1367, 1.4385, 1.3340, 1.3193, 1.2432, 1.4395, 1.6377, 1.1680, 1.5176,\n",
      "        1.3408, 1.4023, 1.2588, 1.2207, 1.4141, 2.0898, 1.3809, 1.3916, 1.4131,\n",
      "        1.2793, 1.2568, 1.2666, 1.4697, 1.5020, 1.5439, 1.4668, 1.2861, 1.3223,\n",
      "        1.4697, 1.3076, 1.3398, 1.2354, 1.1357, 1.2236, 1.4502, 1.5234, 1.3662,\n",
      "        1.4531, 1.3799, 0.9941, 1.5693, 1.4199, 1.4551, 1.9082, 1.4688, 1.5400,\n",
      "        1.3340, 1.7783, 1.4033, 1.5898, 1.2998, 1.6396, 1.5557, 1.3662, 1.4658,\n",
      "        1.3682, 1.7158, 1.3262, 3.6270, 1.5654, 1.4170, 1.5166, 1.8652, 1.3594,\n",
      "        1.9521, 1.4590, 1.4844, 1.6016, 1.4121, 1.4297, 1.4102, 1.3604, 1.4082,\n",
      "        1.6377, 1.4033, 3.4043, 1.5283, 1.4922, 1.3809, 1.4883, 1.4775, 2.0488,\n",
      "        1.4189, 1.3516, 1.4717, 1.6426, 1.2783, 1.4521, 1.4004, 1.3584, 1.5986,\n",
      "        1.5898, 1.4189, 1.4883, 1.3867, 1.5352, 1.0732, 1.2070, 1.5518, 1.5391,\n",
      "        1.5186, 1.9990, 1.4561, 1.6016, 1.3408, 1.1064, 1.6299, 1.6621, 1.4277,\n",
      "        1.5166, 1.6006, 1.3809, 1.2637, 1.4014, 1.4873, 1.5088, 1.2529, 1.6719,\n",
      "        1.2646, 1.2607, 1.2959, 1.3145, 1.5752, 1.5049, 1.4893, 2.0352, 1.5527,\n",
      "        1.4863, 1.5752, 1.4658, 1.3975, 1.1377, 1.5225, 1.0547, 1.6172, 1.4453,\n",
      "        1.3145, 1.5400, 1.5752, 1.2881, 1.6533, 1.4932, 1.7607, 1.5918, 1.2627,\n",
      "        1.3535, 1.5918, 1.7080, 1.4541, 3.6836, 1.2549, 1.6514, 1.4316, 1.3867,\n",
      "        1.6465, 1.5430, 1.5850, 1.4619, 1.6201, 1.4600, 0.7275, 1.6455, 1.4326,\n",
      "        1.3457, 1.5234, 1.2900, 1.4971, 1.3604, 1.3984])\n",
      "model.encoder.layer_norm.bias Parameter containing:\n",
      "tensor([ 3.1470e-01,  4.5532e-01, -4.0625e-01,  3.6938e-01,  3.8550e-01,\n",
      "        -1.6174e-01, -7.9932e-01,  5.0586e-01, -2.2009e-01,  2.1082e-01,\n",
      "        -4.4824e-01,  1.6052e-01,  1.9238e-01,  2.2021e-01, -1.3049e-01,\n",
      "        -1.9275e-01,  1.9004e+00, -7.7734e+00, -2.2632e-01, -2.4512e-01,\n",
      "        -6.0205e-01,  4.9097e-01,  4.6387e-01,  1.5900e-02,  2.4017e-02,\n",
      "         5.4395e-01,  1.3977e-01,  2.9565e-01, -6.6260e-01, -1.7542e-01,\n",
      "        -2.0294e-02, -2.5415e-01, -4.3335e-01, -1.2396e-01, -3.7183e-01,\n",
      "        -7.8064e-02, -4.2554e-01, -1.2891e-01,  1.3660e-01,  3.3472e-01,\n",
      "        -8.8196e-02,  6.3210e-03, -1.7798e-01,  4.6997e-01, -3.7231e-01,\n",
      "         3.0249e-01, -1.0547e-01,  5.4590e-01,  5.8746e-02, -4.1595e-02,\n",
      "        -3.0029e-02, -9.2285e-01,  9.4287e-01,  2.3376e-01, -1.1145e-01,\n",
      "         7.2412e-01,  1.5335e-02, -2.9125e-03, -4.5825e-01, -9.9792e-02,\n",
      "         2.7612e-01,  1.8079e-01, -3.4863e-01, -2.6245e-01, -4.9878e-01,\n",
      "        -1.0508e+00,  1.6748e-01, -3.1738e-01, -1.5736e-04,  9.8047e-01,\n",
      "        -3.8672e-01,  7.6709e-01, -2.1985e-01,  7.2852e-01,  2.8687e-03,\n",
      "        -2.2827e-01, -4.8535e-01,  3.3179e-01,  5.1367e-01,  4.7211e-02,\n",
      "         5.9375e-01, -1.2474e-02, -3.6206e-01,  3.0542e-01, -1.7505e-01,\n",
      "         1.1267e-01,  6.5369e-02, -1.4465e-01,  1.2676e+00,  5.0537e-01,\n",
      "         2.2754e+00,  2.9810e-01,  5.7227e-01, -4.3555e-01,  3.8989e-01,\n",
      "         2.6543e+00,  1.2178e+00, -2.8784e-01,  5.2832e-01,  5.3125e-01,\n",
      "        -3.9819e-01,  1.3586e-01,  1.1267e-01,  5.3906e-01, -5.9180e-01,\n",
      "         2.2498e-01, -6.8506e-01,  2.9199e-01,  1.1957e-01, -3.5303e-01,\n",
      "         1.4905e-01, -2.5293e-01,  9.3408e-01, -1.6680e+00, -2.8101e-01,\n",
      "        -2.8223e-01,  3.5718e-01, -1.1475e-01,  7.9269e-03, -1.2852e+00,\n",
      "         6.6699e-01,  1.1511e-01,  4.7314e-01,  2.0276e-01, -4.7144e-01,\n",
      "         7.5195e-01, -4.2310e-01,  3.5913e-01, -5.3955e-01, -5.5054e-02,\n",
      "        -6.6895e-01,  6.8848e-01,  1.5845e-01,  4.0356e-01,  7.5830e-01,\n",
      "         3.7842e-01, -7.7686e-01, -2.8198e-01,  1.8545e+00,  4.8096e-01,\n",
      "        -4.4775e-01,  1.5059e+00,  3.5327e-01,  4.5020e-01,  1.2549e+00,\n",
      "         3.1592e-01, -6.4355e-01, -4.5898e-01,  1.3242e+00,  3.0591e-01,\n",
      "         5.2295e-01,  1.6418e-02, -1.4782e-03,  1.2275e+00,  1.2070e-02,\n",
      "         9.1162e-01,  1.9458e-01,  5.6787e-01,  1.6382e-01,  5.5371e-01,\n",
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      "       requires_grad=True)\n",
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      "       requires_grad=True)\n",
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      "        -5.4092e-03,  1.6028e-01, -3.0469e-01, -1.6113e-01],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.0.self_attn.out_proj.weight Parameter containing:\n",
      "tensor([[ 9.5749e-03,  3.9612e-02,  7.1167e-02,  ...,  7.4883e-03,\n",
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      "         -6.5994e-03,  1.4252e-02],\n",
      "        ...,\n",
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      "          7.3318e-03, -2.0721e-02]], requires_grad=True)\n",
      "model.decoder.layers.0.self_attn.out_proj.bias Parameter containing:\n",
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      "        -8.7219e-02,  6.0699e-02,  2.3743e-01,  9.6970e-03, -1.1896e-01,\n",
      "         1.5112e-01, -1.2091e-01, -3.4009e-01,  1.6589e-01, -6.9458e-02,\n",
      "         1.7262e-03, -3.2928e-02,  1.6077e-01,  2.6886e-02, -2.9755e-02,\n",
      "         3.9825e-02, -5.2246e-02,  7.4463e-02, -1.0185e-02],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.0.self_attn_layer_norm.weight Parameter containing:\n",
      "tensor([0.6514, 0.8286, 1.6230, 1.1455, 0.9736, 0.7349, 1.3271, 0.7651, 0.7690,\n",
      "        1.2686, 1.3037, 1.3340, 1.0146, 0.9883, 0.6904, 1.3633, 1.1621, 0.7476,\n",
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      "        1.2637, 0.6621, 1.1553, 0.8188, 1.1299, 1.2061, 1.3623, 0.9844, 1.1074,\n",
      "        1.0752, 1.0723, 0.9907, 1.1328, 0.8696, 1.1387, 1.4844, 0.7661, 1.2119,\n",
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      "        1.0820, 1.1211, 1.0693, 1.5068, 1.5693, 1.5117, 0.7295, 0.8550, 1.0967,\n",
      "        0.8491, 0.8232, 0.8623, 1.1660, 1.0918, 0.8882, 1.4541, 0.9175, 1.2188,\n",
      "        1.0283, 1.5635, 0.8979, 0.7798, 1.0967, 0.7729, 0.6250, 1.1465, 1.0830,\n",
      "        1.2949, 1.3486, 1.1680, 0.8931, 1.3291, 1.1777, 1.1152, 1.2217, 1.5088,\n",
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      "        1.1348, 0.8721, 1.5625, 0.9893, 0.8364, 1.5332, 1.2881, 1.6104, 1.3457,\n",
      "        1.5625, 0.9014, 1.2676, 1.6367, 0.8535, 0.9092, 1.5664, 0.7700, 1.1699,\n",
      "        1.0977, 1.3916, 1.2217, 1.6729, 0.7788, 0.7754, 0.9180, 0.6592, 0.9570,\n",
      "        1.4502, 0.8359, 0.6362, 0.8916, 0.6772, 0.8892, 0.7822, 0.9189, 1.5713,\n",
      "        0.9399, 1.1201, 0.7627, 0.7710, 0.5176, 1.5811, 0.9976, 1.1143, 0.7075,\n",
      "        0.9292, 1.0625, 1.1875, 0.8921, 1.3857, 1.4414, 0.9780, 1.1221, 1.0918,\n",
      "        1.1660, 0.7896, 0.5713, 1.4121, 1.4043, 0.3821, 1.1025, 0.8813, 1.3701,\n",
      "        1.4580, 0.5708, 0.8286, 1.0420, 0.2983, 1.0547, 0.6826, 0.6221, 1.1670,\n",
      "        0.8662, 0.9536, 1.1074, 1.4014, 1.0361, 0.7422, 1.0020, 0.9492, 0.8188,\n",
      "        0.9067, 1.3135, 1.0830, 0.6362, 0.7563, 1.1992, 1.6152, 1.1055, 1.0547,\n",
      "        1.0723, 0.9707, 1.0869, 1.6875, 1.0020, 1.2324, 0.9604, 0.8940, 0.6191,\n",
      "        0.9912, 0.9707, 1.1221, 0.8574, 1.1182, 0.6719], requires_grad=True)\n",
      "model.decoder.layers.0.self_attn_layer_norm.bias Parameter containing:\n",
      "tensor([-1.4026e-01,  2.9297e-01,  2.3560e-01, -2.1042e-02, -4.3976e-02,\n",
      "         3.9722e-01, -4.4800e-01,  1.7334e-02, -2.8345e-01, -1.7712e-01,\n",
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      "        -3.6206e-01,  1.5283e-01,  5.3345e-02,  1.3477e-01,  7.9285e-02,\n",
      "         2.6562e-01,  1.7395e-01, -8.7988e-01,  2.2009e-01,  2.0410e-01,\n",
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      "         3.3740e-01,  4.0918e-01,  6.1963e-01,  3.8745e-01,  9.6558e-02,\n",
      "        -1.1731e-01, -8.1665e-02, -3.1567e-01, -5.3162e-02, -1.5991e-02,\n",
      "         2.9175e-01,  2.0752e-01,  4.4883e+00, -7.5989e-02],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.0.encoder_attn.k_proj.weight Parameter containing:\n",
      "tensor([[ 0.0391,  0.0090,  0.0156,  ...,  0.0150, -0.0095, -0.0084],\n",
      "        [ 0.0118, -0.0202,  0.0285,  ..., -0.0403, -0.0184,  0.0111],\n",
      "        [-0.0094, -0.0546,  0.0273,  ...,  0.0273, -0.0148, -0.0074],\n",
      "        ...,\n",
      "        [ 0.0047, -0.0494, -0.0288,  ..., -0.0075, -0.0143, -0.0101],\n",
      "        [-0.0218,  0.0115,  0.0090,  ..., -0.0206,  0.0026,  0.0237],\n",
      "        [ 0.0753, -0.0091, -0.0590,  ..., -0.0332,  0.0245,  0.0048]])\n",
      "model.decoder.layers.0.encoder_attn.v_proj.weight Parameter containing:\n",
      "tensor([[-1.6375e-03,  1.1414e-02,  1.9104e-02,  ...,  2.2552e-02,\n",
      "         -1.6449e-02,  3.2043e-02],\n",
      "        [-1.0598e-04, -2.1469e-02,  1.1337e-02,  ..., -3.9215e-03,\n",
      "          2.5650e-02, -2.2171e-02],\n",
      "        [-6.9199e-03, -8.1711e-03,  1.5991e-02,  ..., -2.3010e-02,\n",
      "         -7.1754e-03,  5.9448e-02],\n",
      "        ...,\n",
      "        [-4.2267e-02, -7.8087e-03, -1.4534e-02,  ..., -7.2670e-03,\n",
      "         -5.3227e-05, -1.6212e-03],\n",
      "        [ 7.2388e-02,  1.7258e-02, -1.3329e-02,  ...,  2.1866e-02,\n",
      "          4.8943e-03,  9.0866e-03],\n",
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      "         -5.0735e-03,  4.0665e-03]])\n",
      "model.decoder.layers.0.encoder_attn.v_proj.bias Parameter containing:\n",
      "tensor([ 3.5591e-03, -3.2940e-03, -3.4943e-02,  4.0207e-03,  1.4400e-03,\n",
      "        -5.4588e-03,  1.0852e-01, -2.4948e-03, -4.4556e-03, -1.0391e-02,\n",
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      "        -1.2244e-01, -1.2123e-02, -1.8387e-03,  1.0422e-02,  1.1873e-03,\n",
      "        -9.5291e-03,  2.8753e-04, -1.3256e-03,  7.1907e-03, -8.3694e-03,\n",
      "         8.9407e-04, -1.2627e-02,  3.0518e-01,  1.8330e-03,  1.3298e-02,\n",
      "        -3.9948e-02, -1.0223e-02, -1.0748e-03, -6.4888e-03, -8.8928e-02,\n",
      "         4.0527e-02, -8.5602e-03,  2.5845e-03,  8.3389e-03,  9.2602e-04,\n",
      "         4.3964e-04,  7.0763e-03,  7.6828e-03, -3.8696e-02, -7.9823e-04,\n",
      "        -9.9277e-04,  3.4389e-03,  8.7585e-03, -2.2907e-03, -4.2450e-02,\n",
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      "        -3.7781e-02,  1.7969e-01,  2.0828e-02, -1.4210e-03])\n",
      "model.decoder.layers.0.encoder_attn_layer_norm.weight Parameter containing:\n",
      "tensor([ 1.1924e+00,  2.3867e+00,  1.4688e+00,  1.1045e+00,  1.9736e+00,\n",
      "         1.0713e+00,  7.0898e-01,  2.8281e+00,  1.2227e+00,  9.6777e-01,\n",
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      "         1.3857e+00,  2.4102e+00,  1.3525e+00,  1.0684e+00,  2.5449e+00,\n",
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      "         1.5801e+00,  1.7061e+00,  9.2822e-01,  3.0371e+00,  1.7900e+00,\n",
      "         1.7461e+00,  2.6113e+00,  1.7373e+00,  1.5771e+00,  1.9326e+00,\n",
      "         8.3008e-01,  1.5654e+00,  2.7324e+00,  5.2490e-01,  2.8477e+00,\n",
      "         1.1953e+00,  2.1055e+00,  1.5840e+00,  1.5420e+00,  1.2363e+00,\n",
      "         1.3867e+00,  1.8838e+00,  1.2734e+00,  1.1572e+00,  2.0625e+00,\n",
      "         2.0352e+00,  1.7041e+00, -7.2900e-01,  2.4414e+00,  1.9814e+00,\n",
      "        -1.3877e+00,  2.7129e+00,  3.2012e+00,  1.9697e+00])\n",
      "model.decoder.layers.0.encoder_attn_layer_norm.bias Parameter containing:\n",
      "tensor([ 1.1853e-01,  5.7959e-01, -1.7688e-01,  4.5435e-01, -2.9297e-01,\n",
      "         5.6494e-01,  9.7504e-03, -3.2642e-01, -9.9411e-03, -1.0811e+00,\n",
      "         5.9570e-01, -3.7378e-01, -9.9670e-02,  9.9902e-01,  1.9196e-02,\n",
      "         3.2715e-01, -1.4661e-01, -4.6118e-01, -1.2559e+00, -1.4905e-01,\n",
      "        -1.5271e-01,  2.0215e-01,  1.7297e-04,  6.9092e-01,  1.8286e-01,\n",
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      "        -5.0354e-02,  4.5508e-01,  1.7419e-01,  8.9233e-02])\n",
      "model.decoder.layers.0.fc1.weight Parameter containing:\n",
      "tensor([[-0.0133,  0.0460,  0.0252,  ...,  0.0027,  0.0507,  0.0404],\n",
      "        [ 0.0327, -0.0287,  0.0008,  ..., -0.0591,  0.0056, -0.0308],\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.0.fc1.bias Parameter containing:\n",
      "tensor([-3.7012, -3.1035, -2.4688,  ..., -3.6367, -2.0098, -2.6973],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.0.fc2.weight Parameter containing:\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.0.fc2.bias Parameter containing:\n",
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      "         1.8347e-01, -1.5637e-01,  2.4695e-01,  2.5406e-02],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.0.final_layer_norm.weight Parameter containing:\n",
      "tensor([1.3906, 1.0977, 2.2129, 1.5898, 1.2520, 1.1094, 1.2783, 1.2510, 1.2529,\n",
      "        1.3760, 2.2324, 1.9912, 1.7783, 1.8066, 1.2949, 1.9951, 1.6973, 1.8281,\n",
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      "        1.7500, 1.3477, 1.7256, 1.2480, 1.0215, 1.6680, 1.7568, 1.7539, 1.8252,\n",
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      "        1.8994, 1.6123, 1.7236, 1.6143, 1.6055, 1.3857, 2.0371, 1.2432, 1.5723,\n",
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      "        1.8330, 1.9443, 1.6514, 1.4961, 1.8398, 1.1592, 1.5195, 1.1621, 1.9355,\n",
      "        1.6045, 1.1055, 1.4590, 1.4170, 2.1719, 1.9590, 1.4170, 1.4209, 1.5850,\n",
      "        1.4512, 1.3418, 1.6484, 1.7012, 1.6836, 1.2148, 1.8789, 1.8311, 1.5020,\n",
      "        1.4199, 1.7207, 1.5811, 1.1953, 1.5654, 1.6260, 1.8115, 1.8115, 1.4434,\n",
      "        1.2275, 1.8711, 1.6816, 0.6426, 1.5684, 1.7803, 1.8857, 1.2783, 1.3438,\n",
      "        1.6924, 1.7705, 1.5908, 1.2881, 2.1992, 1.8623], requires_grad=True)\n",
      "model.decoder.layers.0.final_layer_norm.bias Parameter containing:\n",
      "tensor([ 6.8555e-01, -6.6895e-02,  3.8574e-02, -4.5807e-02, -8.1689e-01,\n",
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      "        -9.7900e-02,  6.3232e-01, -2.5073e-01,  5.2783e-01,  6.6357e-01,\n",
      "         4.9365e-01, -6.8652e-01, -1.1359e-01,  2.9028e-01],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.1.self_attn.k_proj.weight Parameter containing:\n",
      "tensor([[ 0.0450, -0.0004,  0.0359,  ..., -0.0123, -0.0347, -0.0065],\n",
      "        [-0.0263, -0.0052,  0.0058,  ...,  0.0117, -0.0327, -0.0048],\n",
      "        [-0.0134,  0.0067, -0.0140,  ..., -0.0428, -0.0601,  0.0013],\n",
      "        ...,\n",
      "        [-0.0165, -0.0107, -0.0022,  ...,  0.0041, -0.0015, -0.0140],\n",
      "        [ 0.0237,  0.0035,  0.1064,  ..., -0.0013, -0.0166, -0.0170],\n",
      "        [-0.0660, -0.0165,  0.1035,  ...,  0.0681, -0.0313, -0.0192]],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.1.self_attn.v_proj.weight Parameter containing:\n",
      "tensor([[ 0.0158, -0.0158,  0.0189,  ...,  0.0109, -0.0295,  0.0021],\n",
      "        [-0.0130,  0.0263, -0.0359,  ..., -0.0247,  0.0333,  0.0176],\n",
      "        [-0.0143,  0.0025,  0.0518,  ..., -0.0233,  0.0089, -0.0022],\n",
      "        ...,\n",
      "        [-0.0184,  0.0027, -0.0130,  ..., -0.0299, -0.0076,  0.0070],\n",
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      "        [-0.0042, -0.0034, -0.0152,  ..., -0.0222, -0.0010,  0.0047]],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.1.self_attn.v_proj.bias Parameter containing:\n",
      "tensor([-3.9978e-02,  1.1421e-02,  8.4534e-03, -4.3427e-02,  8.2092e-03,\n",
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      "       requires_grad=True)\n",
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      "       requires_grad=True)\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.1.self_attn.out_proj.weight Parameter containing:\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.1.self_attn.out_proj.bias Parameter containing:\n",
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      "        -5.1727e-02,  8.5907e-03, -3.0350e-02,  1.2512e-02, -4.3915e-02,\n",
      "         1.1627e-01, -1.4412e-02, -9.5215e-02,  1.0065e-01, -6.8359e-02,\n",
      "        -6.2752e-03,  6.4087e-02, -1.2657e-02, -6.9763e-02, -3.9734e-02,\n",
      "         4.9835e-02,  4.8859e-02, -1.5045e-02, -2.7985e-02, -1.2337e-02,\n",
      "        -7.7271e-02, -7.3975e-02, -4.7119e-02, -2.4582e-02,  3.0579e-02,\n",
      "        -1.0284e-02, -4.0985e-02,  2.2491e-02, -2.8564e-02, -5.7983e-02,\n",
      "         1.2433e-01, -2.6318e-01,  1.2085e-02,  8.7500e-01,  6.1760e-03,\n",
      "        -2.9144e-02,  3.9795e-02, -8.7891e-02, -1.2030e-01, -8.0261e-02,\n",
      "         3.5000e-04, -3.4424e-02,  1.5976e-02, -1.1664e-01, -1.0925e-02,\n",
      "        -5.1727e-03,  1.7319e-02, -7.8857e-02,  2.6679e-04,  1.0223e-01,\n",
      "         1.2622e-01, -3.3356e-02,  1.8707e-02,  2.0752e-02,  5.0774e-03,\n",
      "        -1.6272e-01, -1.5796e-01,  8.7814e-03, -2.5497e-02, -1.5984e-03,\n",
      "         1.4351e-02,  1.5955e-01, -2.4963e-01,  1.0577e-01, -2.3102e-02,\n",
      "        -1.4763e-02,  6.0272e-02,  5.7602e-03, -6.4182e-04, -6.5689e-03,\n",
      "        -1.6541e-02, -4.3610e-02, -3.8330e-02,  2.2568e-02,  3.3508e-02,\n",
      "         4.2999e-02,  1.7197e-02,  4.5380e-02, -5.1880e-02,  4.6082e-02,\n",
      "         1.1774e-01,  3.6865e-02, -3.5248e-03, -3.6804e-02, -3.7823e-03,\n",
      "        -4.0619e-02,  7.2815e-02, -1.8799e-02, -6.1836e-03,  7.3425e-02,\n",
      "        -5.7106e-03, -1.4038e-01, -6.7932e-02,  6.9160e-03,  3.3264e-02,\n",
      "        -4.8920e-02, -4.9866e-02, -1.2030e-01, -2.5345e-02, -4.2816e-02,\n",
      "        -1.4636e-01,  2.6306e-02,  1.1212e-01,  3.6285e-02,  2.9272e-01,\n",
      "        -5.0476e-02, -4.1168e-02, -8.4167e-02, -1.7029e-01, -1.2903e-01,\n",
      "        -6.7078e-02, -8.6792e-02, -2.6413e-02, -2.3422e-02,  1.6052e-02,\n",
      "        -2.8793e-02, -2.9434e-02, -1.7303e-02, -8.5083e-02,  3.9673e-02,\n",
      "         2.9739e-02, -4.2534e-03,  5.3680e-02,  1.2398e-02, -1.1890e-01,\n",
      "        -1.3695e-02, -5.4321e-02, -4.8096e-02, -4.8218e-02, -5.2691e-04,\n",
      "         5.8655e-02,  2.7441e-01,  9.3994e-02,  1.3321e-02, -1.1151e-01,\n",
      "        -1.8494e-02,  5.7129e-02,  1.0590e-02,  1.5541e-02,  1.7365e-02,\n",
      "        -2.6917e-02, -4.6234e-03,  1.1609e-01,  2.9922e-02,  8.2275e-02,\n",
      "         2.6587e-01, -1.0358e-01, -8.3008e-02, -2.7893e-02,  2.8046e-02,\n",
      "        -2.1899e-01, -5.5969e-02, -1.6724e-02, -1.9202e-01, -1.4832e-01,\n",
      "         8.6784e-05,  4.1473e-02,  2.3438e-02, -1.4862e-02,  5.1331e-02,\n",
      "        -3.4241e-02,  7.5867e-02, -4.2633e-02, -3.0136e-02, -4.6082e-02,\n",
      "         5.1300e-02, -3.5095e-03,  1.3245e-02, -5.3589e-02,  5.3070e-02,\n",
      "         7.2784e-03, -1.2360e-01,  7.1030e-03,  2.5650e-02, -6.3049e-02,\n",
      "         5.9692e-02,  9.9182e-02, -2.5040e-02,  6.0310e-03, -6.9763e-02,\n",
      "         2.2339e-02,  6.6528e-02,  2.5635e-02,  2.3041e-02,  1.0071e-02,\n",
      "         2.8595e-02,  2.7618e-02, -7.0312e-02, -2.4090e-03, -6.0638e-02,\n",
      "        -5.8929e-02,  7.4341e-02, -3.3569e-02, -2.8854e-02, -4.5776e-02,\n",
      "         3.3875e-02, -2.4475e-02,  3.2867e-02, -5.1904e-01, -2.0520e-01,\n",
      "        -5.1855e-01, -9.9976e-02,  4.1229e-02,  6.6589e-02,  8.0566e-02,\n",
      "        -8.9233e-02,  5.3520e-03, -2.3877e-01, -1.3647e-01, -8.4839e-02,\n",
      "        -6.7566e-02,  1.3351e-03,  9.1125e-02,  1.7746e-02,  8.3679e-02,\n",
      "        -8.2642e-02, -3.1769e-02,  3.1311e-02,  3.4241e-02,  5.4779e-02,\n",
      "        -6.6650e-02,  7.5073e-02,  2.3285e-02, -1.2292e-01,  1.0254e-01,\n",
      "         4.2456e-01, -2.3407e-02, -1.2866e-01,  8.1726e-02, -4.7379e-03,\n",
      "        -1.4648e-02, -2.9099e-02, -7.8735e-02, -5.3436e-02, -4.2206e-02,\n",
      "         8.6975e-02, -6.3354e-02,  2.2049e-02, -1.6101e-01,  8.3801e-02,\n",
      "        -6.4209e-02,  2.8586e-04, -1.0388e-01, -1.4221e-01, -5.8655e-02,\n",
      "        -5.1575e-03, -2.5558e-02, -1.6785e-02, -8.1421e-02,  2.6566e-02,\n",
      "        -2.2314e-01,  2.5749e-03,  3.7048e-02,  8.5373e-03,  1.1633e-01,\n",
      "         4.5166e-03, -3.3844e-02, -1.1963e-01, -5.8075e-02, -9.1553e-02,\n",
      "         2.0996e-01,  3.4912e-02,  6.5674e-02,  8.8037e-01, -4.2633e-02,\n",
      "         1.0144e-01,  2.7435e-02,  6.8665e-02,  2.3529e-02, -3.5065e-02,\n",
      "         1.7114e-01, -6.4392e-02,  1.0406e-01, -4.6539e-02, -3.7415e-02,\n",
      "        -1.1963e-02,  3.1036e-02, -5.7411e-03, -1.2952e-01,  5.6122e-02,\n",
      "        -6.4758e-02,  1.3418e-03, -8.1970e-02, -1.3806e-01, -7.8003e-02,\n",
      "        -2.8336e-02, -2.7557e-02, -4.1748e-02,  8.6853e-02, -8.2153e-02,\n",
      "         1.1102e-01, -5.9509e-02,  5.0354e-02,  1.9913e-02,  7.4501e-03,\n",
      "         8.3008e-02,  3.0777e-02, -2.1210e-03, -1.7715e-02,  1.9272e-02,\n",
      "        -5.1453e-02, -1.8478e-02,  6.0455e-02, -1.7627e-01, -9.9548e-02,\n",
      "         4.2053e-02, -2.1240e-02, -5.0690e-02,  1.2917e-02,  2.1759e-02,\n",
      "        -2.6947e-02,  2.0767e-02, -1.0582e-02, -6.4926e-03,  1.7303e-02,\n",
      "        -1.5796e-01, -3.7659e-02, -1.8604e-01,  1.2683e-01, -4.1534e-02,\n",
      "         1.6101e-01, -2.9480e-02,  5.9814e-01, -1.0034e-01, -3.1799e-02,\n",
      "         5.5817e-02,  7.5758e-05,  3.9612e-02, -2.4368e-02,  9.1431e-02,\n",
      "         4.4159e-02,  9.7351e-02,  9.1357e-01, -2.7374e-02,  3.7354e-02,\n",
      "         7.2670e-03, -6.8054e-02,  3.9276e-02, -2.0447e-02,  1.0811e-02,\n",
      "        -1.4038e-01, -2.1839e-03, -2.8000e-02, -3.3478e-02],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.1.self_attn_layer_norm.weight Parameter containing:\n",
      "tensor([0.8242, 1.1602, 1.9619, 1.0869, 0.9424, 0.8857, 0.9810, 1.0430, 0.7856,\n",
      "        1.0518, 1.7070, 1.5361, 1.9102, 1.4434, 1.0176, 1.7119, 1.1445, 1.3115,\n",
      "        0.8994, 1.2256, 1.4297, 1.0752, 0.8516, 1.0576, 1.3330, 1.0693, 1.4521,\n",
      "        1.5459, 1.0557, 0.9629, 1.5352, 1.2148, 0.8452, 1.2871, 0.8042, 1.3965,\n",
      "        0.9277, 1.0654, 1.0654, 0.9194, 1.4297, 0.8340, 1.4238, 1.3184, 1.6953,\n",
      "        0.9805, 1.0859, 1.0508, 0.8188, 0.7671, 1.1260, 0.3054, 1.4023, 1.6602,\n",
      "        1.6201, 0.9404, 1.3311, 1.3896, 1.5732, 1.1797, 0.8403, 0.9497, 0.9199,\n",
      "        1.5312, 1.4434, 0.8345, 1.6787, 1.8096, 1.5605, 1.6670, 1.0684, 0.6309,\n",
      "        0.9048, 1.1025, 1.0322, 1.0508, 1.2129, 1.0908, 0.8379, 1.4668, 1.3291,\n",
      "        1.6426, 1.0811, 0.9272, 1.1865, 1.0762, 0.6880, 1.0938, 1.1133, 0.9390,\n",
      "        1.0215, 1.4902, 2.2656, 1.1963, 0.9707, 0.8101, 0.7031, 0.9531, 1.2041,\n",
      "        1.2881, 1.2920, 0.7251, 0.4468, 1.0420, 0.7734, 0.9600, 1.0508, 0.7847,\n",
      "        0.6885, 0.7842, 1.3359, 1.5498, 0.9077, 1.6582, 0.8687, 0.7104, 1.7754,\n",
      "        1.0205, 0.8013, 1.8096, 0.5332, 1.9551, 1.2080, 1.1357, 1.1475, 1.4707,\n",
      "        1.0723, 1.0166, 1.3975, 1.1201, 1.1309, 0.6992, 1.4277, 1.0117, 1.5254,\n",
      "        1.3125, 1.2461, 1.0801, 1.1025, 0.9722, 0.6733, 1.3027, 1.0830, 0.8784,\n",
      "        0.8828, 1.4277, 0.8711, 1.3115, 0.7354, 1.0264, 1.4834, 1.3496, 0.9819,\n",
      "        0.6431, 1.5234, 1.2598, 0.8184, 1.2656, 0.7100, 1.3652, 1.1113, 0.8096,\n",
      "        1.5479, 1.2900, 1.2627, 1.2754, 1.3398, 0.9707, 1.6807, 1.0713, 1.4932,\n",
      "        3.4121, 1.2070, 1.4072, 3.0234, 1.3105, 1.3740, 1.0430, 0.9399, 1.3750,\n",
      "        1.0117, 0.9136, 0.8770, 0.9604, 1.0107, 0.5845, 0.6167, 1.0000, 1.3271,\n",
      "        1.2314, 0.6948, 0.8027, 1.6699, 0.8452, 1.1826, 0.9111, 1.2461, 1.3857,\n",
      "        1.3984, 1.7188, 0.9990, 0.9512, 1.1650, 0.8335, 0.9023, 0.9551, 0.9233,\n",
      "        1.8555, 1.1289, 0.9507, 0.9888, 0.8745, 1.4268, 0.6929, 1.3008, 0.8232,\n",
      "        0.9619, 1.1533, 0.9482, 1.8975, 1.4238, 1.3672, 0.7095, 1.5439, 0.9697,\n",
      "        0.6313, 1.2354, 1.0469, 1.8086, 1.4199, 0.7856, 1.1865, 1.2246, 1.4932,\n",
      "        1.1904, 1.0381, 2.7461, 0.9038, 1.1240, 0.5659, 0.4585, 0.9268, 1.3711,\n",
      "        1.2783, 1.7314, 1.2539, 0.9966, 1.3818, 1.5459, 1.3350, 1.1270, 1.5742,\n",
      "        0.9863, 1.0039, 1.1084, 1.2607, 1.2305, 1.3291, 1.1621, 0.9189, 1.3125,\n",
      "        1.2148, 0.8662, 0.6270, 0.6445, 0.8438, 0.6479, 0.6392, 1.5146, 1.1406,\n",
      "        1.1436, 1.1475, 1.1523, 1.4736, 1.5371, 0.7798, 1.2236, 0.5898, 0.9590,\n",
      "        1.2090, 1.5244, 1.3027, 0.8237, 0.7246, 1.6494, 0.9180, 1.5518, 1.6016,\n",
      "        1.3428, 1.6670, 1.0225, 1.2217, 1.1533, 0.8862, 1.5498, 1.0381, 0.9868,\n",
      "        0.7305, 1.3506, 1.4736, 0.3999, 0.8706, 0.7617, 4.6953, 0.8979, 1.1553,\n",
      "        1.5107, 1.4951, 0.8003, 1.2119, 0.7075, 1.5869, 1.2549, 0.9165, 1.8525,\n",
      "        0.8979, 0.7783, 0.7632, 0.9028, 0.6860, 1.4521, 0.8271, 1.4609, 0.8818,\n",
      "        1.5967, 1.3145, 1.3809, 1.4229, 1.4854, 1.5615, 1.0645, 0.9438, 0.9263,\n",
      "        1.3750, 1.4043, 0.9897, 0.9375, 1.2676, 0.6748, 1.6162, 0.7900, 1.4941,\n",
      "        0.9199, 0.6714, 0.8633, 0.9805, 1.2588, 1.3311, 0.7373, 0.9053, 1.1084,\n",
      "        0.9893, 0.6499, 1.2725, 1.2012, 1.1289, 1.1680, 0.8604, 1.3506, 0.9810,\n",
      "        0.7983, 1.8027, 0.3467, 0.5557, 1.0762, 1.0234, 1.6377, 1.2607, 0.7773,\n",
      "        0.7422, 1.3408, 1.1113, 0.6318, 1.1504, 1.1328, 1.1006, 0.8906, 0.7129,\n",
      "        1.0986, 1.2236, 0.8970, 1.3896, 1.3398, 1.0078], requires_grad=True)\n",
      "model.decoder.layers.1.self_attn_layer_norm.bias Parameter containing:\n",
      "tensor([-7.2021e-02,  1.3159e-01, -2.6465e-01,  1.2360e-01, -6.3354e-02,\n",
      "        -1.1151e-01, -5.6104e-01, -8.8318e-02, -9.1736e-02, -1.0278e-01,\n",
      "        -2.3267e-01,  1.4172e-01, -4.3243e-02,  2.3743e-01,  1.6187e-01,\n",
      "         1.3062e-01,  5.3101e-02, -1.5344e-01,  1.2769e-01, -1.9971e-01,\n",
      "        -2.6465e-01, -6.5231e-03, -5.4321e-02,  9.8206e-02,  3.9307e-01,\n",
      "         5.8777e-02,  2.9736e-01, -8.8989e-02,  3.9856e-02,  3.3569e-02,\n",
      "         1.7163e-01, -2.2314e-01, -5.4736e-01, -2.9968e-02, -2.1851e-01,\n",
      "         7.3730e-02, -2.1606e-01, -1.6571e-02,  2.1667e-02, -2.2659e-02,\n",
      "        -1.7029e-01, -1.1066e-01,  9.0881e-02,  2.2339e-01, -1.4441e-01,\n",
      "         3.2617e-01,  1.2428e-02, -3.0957e-01,  1.2292e-01, -1.7822e-01,\n",
      "        -1.6931e-01,  7.7148e-02,  9.1248e-02, -1.4331e-01,  2.8782e-03,\n",
      "         1.2604e-02,  2.4341e-01,  3.7903e-02, -1.8250e-01, -1.4868e-01,\n",
      "        -5.2032e-02, -1.2305e-01, -3.0502e-02, -1.0236e-01,  1.8140e-01,\n",
      "         6.4697e-02,  4.8096e-02, -8.7463e-02, -7.7148e-02, -3.3008e-01,\n",
      "         1.1633e-01, -5.9619e-01,  5.4108e-02,  4.0703e+00,  6.5184e-04,\n",
      "        -1.9702e-01, -9.0714e-03, -2.5366e-01, -8.5144e-02, -1.5063e-01,\n",
      "         1.9934e-01, -1.9238e-01, -1.1719e-02, -7.7576e-02, -1.7224e-01,\n",
      "        -9.3140e-02,  8.0872e-02, -2.3645e-01, -2.4561e-01,  4.9609e-01,\n",
      "        -8.3740e-02, -1.8604e-01, -4.7754e-01, -6.1432e-02, -2.1997e-01,\n",
      "        -4.0723e-01, -5.4321e-02, -1.7334e-01, -5.0476e-02, -7.5562e-02,\n",
      "        -1.7700e-02,  7.1143e-01, -1.9666e-01,  4.9652e-02, -3.4363e-02,\n",
      "        -4.9164e-02,  2.0996e-02, -1.1267e-01, -4.6606e-01, -8.9905e-02,\n",
      "        -2.2720e-02, -2.8101e-01,  6.2378e-02,  1.2732e-01,  2.3022e-01,\n",
      "         5.6885e-02, -9.8816e-02,  7.4036e-02, -3.5095e-02,  1.6821e-01,\n",
      "         2.3059e-01, -2.5299e-02,  9.5886e-02, -2.6099e-01,  9.8511e-02,\n",
      "        -5.2795e-02,  2.1021e-01,  2.4445e-02,  4.7150e-02,  3.1689e-01,\n",
      "         1.6711e-01, -5.7715e-01, -2.3254e-01, -7.9102e-02,  1.2988e-01,\n",
      "         8.5571e-02, -1.9641e-01, -4.0503e-01, -1.0968e-01, -2.9639e-01,\n",
      "         8.5693e-02, -4.4678e-02,  3.0716e-02, -2.4988e-01,  3.8550e-01,\n",
      "        -6.1310e-02, -1.2988e-01, -5.3711e-02, -3.9160e-01, -5.3662e-01,\n",
      "         1.9562e-02, -2.9468e-01, -1.0840e-01,  6.5674e-02, -3.3691e-02,\n",
      "         6.7520e-03, -2.3486e-01, -1.1957e-01, -2.2925e-01,  2.3376e-02,\n",
      "        -2.8540e-01,  2.4377e-01, -2.8271e-01,  3.4912e-01, -6.2866e-02,\n",
      "        -1.2744e-01,  1.7487e-02, -2.7539e-01,  3.4851e-02,  7.5989e-02,\n",
      "        -8.5876e-02, -7.5989e-02, -1.7712e-01, -2.8687e-03, -7.0801e-01,\n",
      "        -8.4717e-02,  1.3708e-01, -2.8992e-02,  1.4417e-01,  8.3740e-02,\n",
      "        -1.1310e-01, -1.3647e-01,  3.5693e-01, -2.8760e-01,  3.8110e-01,\n",
      "         1.0376e-01, -2.1741e-01, -9.4421e-02, -3.6285e-02, -1.9934e-01,\n",
      "         2.5156e+00, -7.0984e-02, -1.9617e-01, -4.2554e-01, -1.1664e-01,\n",
      "        -4.9316e-02,  1.6333e-01, -8.5022e-02,  2.3727e-02,  4.6570e-02,\n",
      "        -1.4587e-01,  2.3816e-01, -1.5771e-01,  4.5386e-01, -2.3938e-01,\n",
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      "       requires_grad=True)\n",
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      "model.decoder.layers.1.encoder_attn.q_proj.weight Parameter containing:\n",
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      "model.decoder.layers.1.encoder_attn.out_proj.weight Parameter containing:\n",
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      "model.decoder.layers.1.encoder_attn.out_proj.bias Parameter containing:\n",
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      "         2.7344e-02,  7.4219e-02, -1.8286e-01, -8.4839e-02])\n",
      "model.decoder.layers.1.encoder_attn_layer_norm.weight Parameter containing:\n",
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      "         1.8223e+00,  4.4492e+00,  2.2930e+00,  2.0410e+00])\n",
      "model.decoder.layers.1.encoder_attn_layer_norm.bias Parameter containing:\n",
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      "         9.6558e-02,  4.0820e-01, -7.3303e-02,  1.5320e-01])\n",
      "model.decoder.layers.1.fc1.weight Parameter containing:\n",
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      "        [-0.0154,  0.0202, -0.0198,  ...,  0.0220,  0.0253, -0.0121],\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.1.fc1.bias Parameter containing:\n",
      "tensor([-1.2871, -1.7393, -2.5293,  ..., -1.1855, -2.6309, -1.9062],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.1.fc2.weight Parameter containing:\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.1.fc2.bias Parameter containing:\n",
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      "        -1.1162e-02,  5.0018e-02,  1.3232e-01,  5.1331e-02, -1.3489e-01,\n",
      "        -4.2938e-02,  1.0727e-02,  1.0941e-02, -1.6687e-01, -2.3987e-02,\n",
      "        -1.8509e-02,  9.9670e-02,  1.3237e-02, -1.8787e-01, -1.7029e-01,\n",
      "        -6.1859e-02,  5.2521e-02,  1.3855e-01, -5.9998e-02, -1.2999e-03,\n",
      "        -2.9778e-04, -5.3741e-02, -9.0820e-02, -2.1317e-02,  2.7808e-01,\n",
      "        -1.0406e-01, -5.7983e-02,  1.3184e-01,  1.5552e-01, -3.1769e-02,\n",
      "        -1.6565e-01,  7.7820e-02, -6.4774e-03,  8.3557e-02,  5.9601e-02,\n",
      "         2.4681e-03,  1.6772e-01, -1.8143e-02,  8.1970e-02,  7.5256e-02,\n",
      "        -1.4453e-01, -6.0596e-01,  3.1689e-01, -1.8738e-01,  1.0095e-01,\n",
      "        -5.8838e-02, -5.3009e-02,  6.8787e-02,  4.4287e-01,  2.5513e-01,\n",
      "        -6.6895e-02,  1.0229e-01,  2.9083e-02, -1.1090e-01, -2.3590e-02,\n",
      "        -1.5857e-01, -5.5511e-02, -1.5454e-01,  1.9684e-02, -2.1095e-03,\n",
      "        -9.6680e-02, -1.1841e-01, -3.2166e-02, -1.7310e-01,  1.7261e-01,\n",
      "         2.9465e-02,  1.0980e-01, -2.1533e-01,  1.6382e-01, -7.8354e-03,\n",
      "        -2.3529e-02,  1.9873e-01,  2.7612e-01,  3.1555e-02,  9.1248e-02,\n",
      "        -5.3711e-02,  1.4026e-01,  2.7832e-01, -7.3669e-02, -3.5004e-02,\n",
      "         4.4067e-02,  1.6418e-02,  5.0293e-02,  5.2979e-02, -2.5439e-01,\n",
      "         1.3489e-01,  1.6797e-01,  9.8999e-02,  1.4929e-01,  1.1017e-01,\n",
      "         1.6174e-01,  4.1962e-02,  8.7219e-02,  6.1218e-02, -3.8666e-02,\n",
      "        -9.4299e-02,  4.4586e-02,  6.5735e-02,  4.5868e-02,  1.5088e-01,\n",
      "         6.6101e-02,  5.6854e-02,  1.3135e-01,  1.8372e-01,  2.1912e-01,\n",
      "        -5.4565e-02, -1.1206e-01, -4.1504e-02,  1.5564e-01, -6.3721e-02,\n",
      "         2.6733e-02, -4.4238e-01,  2.0068e-01,  1.5137e-01, -1.5955e-01,\n",
      "         4.8584e-02, -7.2632e-02,  1.0608e-01, -3.7933e-02, -1.8872e-01,\n",
      "        -4.4678e-02,  1.4917e-01, -9.8267e-02,  2.1228e-01,  2.1179e-02,\n",
      "        -8.4900e-02,  7.0251e-02, -5.8624e-02, -6.4453e-02, -2.0950e-02,\n",
      "        -7.8506e-03,  1.0083e-01, -5.1941e-02,  2.1631e-01,  2.1216e-01,\n",
      "         6.1951e-02, -1.7224e-01,  1.3318e-01,  3.1525e-02,  1.1212e-01,\n",
      "        -1.9638e-02, -8.0627e-02,  9.8083e-02, -4.0137e-01,  4.1046e-02,\n",
      "         2.3120e-01, -1.1334e-01, -6.4011e-03,  2.1484e-01,  3.0615e-01,\n",
      "         9.0393e-02,  8.7219e-02,  1.3562e-01,  1.9702e-01,  9.1125e-02,\n",
      "         1.5662e-01, -3.4473e-01,  4.9500e-02, -7.2241e-04,  3.7292e-02,\n",
      "         8.8379e-02, -5.2094e-02, -5.1941e-02, -1.7310e-01, -2.2186e-02,\n",
      "        -4.6417e-02,  5.7861e-02,  2.1683e-02, -5.4413e-02,  1.0016e-01,\n",
      "         1.8079e-01,  1.0522e-01,  6.3232e-02,  4.4830e-02, -1.1055e-02,\n",
      "        -9.2407e-02, -7.5684e-02, -6.2195e-02, -9.3115e-01,  2.1814e-01,\n",
      "         2.5830e-01,  1.4941e-01, -8.4351e-02,  2.6904e-01, -6.0211e-02,\n",
      "         9.6863e-02,  1.6882e-01,  8.8928e-02,  1.9440e-02,  1.0840e-01,\n",
      "         6.6528e-02, -6.5735e-02, -6.1401e-02, -2.2629e-02,  1.2622e-01,\n",
      "        -2.2858e-02,  2.6831e-01,  2.9037e-02,  2.6031e-02,  6.0333e-02,\n",
      "        -3.9139e-03, -2.2522e-01,  2.1589e-04,  3.2978e-03, -1.6931e-01,\n",
      "        -3.8428e-01, -1.5234e-01,  1.7456e-01, -1.9867e-02, -3.7170e-02,\n",
      "         1.2439e-01, -1.4087e-01,  2.3560e-01,  9.9304e-02,  4.1077e-02,\n",
      "        -6.2683e-02,  7.8186e-02, -9.6619e-02,  1.2384e-01,  1.8237e-01,\n",
      "        -6.8665e-02, -3.8300e-02, -7.8308e-02, -1.9238e-01,  3.0542e-01,\n",
      "        -2.9266e-02,  8.3252e-02, -7.1899e-02,  2.3486e-01,  1.5587e-02,\n",
      "        -8.7341e-02,  6.2752e-03, -1.3013e-01,  4.4067e-02, -5.5542e-02,\n",
      "        -6.1951e-02,  4.5509e-03,  1.9128e-01, -1.4844e-01,  2.4939e-01,\n",
      "         3.1006e-01, -9.7534e-02, -1.0693e-01,  3.4839e-01, -3.2806e-02,\n",
      "         4.8971e-04, -5.6061e-02,  1.7151e-01, -5.2917e-02,  1.3281e-01,\n",
      "        -1.8201e-01,  5.8563e-02, -8.5999e-02,  1.6809e-01, -2.6505e-02,\n",
      "         8.3069e-02,  2.9694e-02, -1.6101e-01,  1.9641e-01, -6.1707e-02,\n",
      "        -5.5542e-02,  1.0382e-01,  5.0293e-02, -8.9966e-02, -7.1533e-02,\n",
      "         3.2715e-02,  8.5022e-02,  1.4404e-02,  2.6154e-02,  5.2612e-02,\n",
      "        -3.5376e-01, -2.8839e-02, -5.0812e-02,  1.8481e-01, -3.1464e-02,\n",
      "         6.2744e-02, -2.5421e-02,  4.8065e-02, -1.1359e-01,  1.2988e-01,\n",
      "         1.3904e-01, -7.0312e-02,  6.5186e-02,  4.6387e-02,  3.2935e-01,\n",
      "        -5.1697e-02,  5.9814e-02, -1.4502e-01, -2.1301e-02,  1.3618e-02,\n",
      "         1.0022e-01, -5.4779e-02, -1.6028e-01, -1.0693e-01, -9.9411e-03,\n",
      "         6.9031e-02,  3.1948e-03,  1.5967e-01, -7.4829e-02,  1.4258e-01,\n",
      "         4.1626e-02, -1.1334e-01, -8.2764e-01,  1.5222e-01,  8.4045e-02,\n",
      "        -9.7046e-02, -8.2092e-02,  1.3649e-02,  6.5674e-02, -1.3110e-01,\n",
      "        -1.4978e-01, -9.6321e-05, -3.5195e+00, -6.2408e-02, -9.0881e-02,\n",
      "         8.4778e-02,  5.3497e-02,  1.4807e-01, -1.1377e-01,  5.3497e-02,\n",
      "         8.4351e-02,  1.0345e-02, -9.1492e-02,  2.0276e-01],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.1.final_layer_norm.weight Parameter containing:\n",
      "tensor([1.6963, 1.3467, 2.1172, 1.8438, 1.4678, 1.5420, 1.5371, 1.3525, 1.4951,\n",
      "        1.6055, 1.8828, 1.7344, 1.4180, 1.6621, 1.4287, 1.8018, 1.8418, 1.9023,\n",
      "        4.3594, 1.8672, 1.6797, 1.3867, 1.5557, 1.3877, 1.7754, 1.7412, 1.8770,\n",
      "        1.9668, 1.8203, 1.7783, 1.7773, 1.7656, 1.9375, 1.1416, 1.6182, 1.8604,\n",
      "        1.7656, 1.3242, 1.7520, 1.2686, 1.9199, 1.5957, 1.8262, 1.5586, 1.6855,\n",
      "        1.9238, 1.3516, 1.7432, 1.5479, 1.4355, 1.6602, 2.2773, 1.5908, 1.7480,\n",
      "        1.5000, 1.6738, 1.7451, 1.7188, 1.6699, 1.4824, 1.6641, 1.1064, 1.6768,\n",
      "        2.0898, 1.8906, 1.5654, 1.8125, 1.3887, 1.9854, 1.9629, 1.7305, 1.5166,\n",
      "        1.4453, 3.1953, 1.6309, 1.7578, 1.9922, 1.6025, 1.6553, 1.8730, 1.9355,\n",
      "        1.7520, 1.4854, 1.6387, 1.6289, 1.7539, 1.5068, 1.7725, 1.7627, 1.8623,\n",
      "        1.8799, 1.7002, 2.6660, 1.2598, 1.6943, 1.5518, 1.4199, 1.7051, 1.4141,\n",
      "        1.7637, 1.9023, 1.3223, 2.0664, 1.7256, 1.5840, 1.9619, 1.6982, 1.5449,\n",
      "        1.4580, 1.3955, 1.4648, 2.0195, 1.6797, 0.7222, 1.6982, 1.5303, 1.9043,\n",
      "        1.3730, 1.4834, 1.7100, 1.3438, 1.4717, 1.8213, 1.7090, 1.7852, 1.7109,\n",
      "        2.5410, 1.6875, 1.9541, 1.8594, 1.7998, 1.5762, 1.5576, 1.7236, 1.6875,\n",
      "        1.6377, 1.8203, 1.5762, 1.8174, 1.8896, 1.5488, 2.0605, 1.5928, 1.6631,\n",
      "        1.6211, 1.7676, 1.6123, 1.9053, 1.6445, 1.7158, 1.8750, 1.8477, 1.6914,\n",
      "        1.9854, 1.9736, 1.7314, 1.6758, 1.5576, 1.5771, 1.5088, 1.6094, 1.7461,\n",
      "        1.8477, 2.4141, 1.8438, 1.9385, 1.5547, 1.6553, 1.6523, 1.7402, 1.7725,\n",
      "        0.3701, 1.6943, 2.1504, 0.8618, 1.7373, 1.8877, 1.5996, 1.5791, 1.9814,\n",
      "        1.6250, 1.5820, 1.8438, 1.4932, 2.0020, 1.4238, 1.3740, 1.7686, 1.6826,\n",
      "        1.8613, 1.4141, 1.5498, 1.7451, 1.6865, 2.0547, 1.5186, 1.8662, 1.8896,\n",
      "        1.8174, 1.8008, 1.5479, 1.4385, 1.7725, 1.5430, 1.7461, 1.7637, 1.3018,\n",
      "        1.4023, 1.5615, 1.5693, 1.5176, 1.4980, 1.2832, 1.5537, 1.8174, 1.6719,\n",
      "        1.8027, 1.8223, 1.4385, 1.7520, 2.2656, 1.9023, 1.5879, 1.6240, 1.7393,\n",
      "        1.7510, 1.6660, 1.6572, 1.0371, 1.2490, 1.4756, 1.6836, 1.8301, 1.5703,\n",
      "        1.4111, 1.6152, 1.5908, 1.7109, 1.8350, 1.5303, 1.9980, 1.5469, 2.0078,\n",
      "        1.7939, 2.0020, 1.9668, 1.7080, 1.6816, 1.7852, 2.0977, 1.5254, 1.8281,\n",
      "        1.8691, 1.7861, 1.6270, 2.0332, 1.8164, 1.5059, 1.8418, 1.6484, 1.9404,\n",
      "        1.9336, 1.5186, 1.4600, 1.4053, 1.7051, 1.5098, 2.0586, 1.8008, 1.4658,\n",
      "        1.6934, 1.6475, 1.7832, 1.8174, 1.6758, 2.2402, 1.6699, 1.6016, 1.7031,\n",
      "        1.9150, 1.8330, 1.9863, 1.6504, 1.8574, 1.9883, 1.4814, 1.9043, 1.7979,\n",
      "        1.7598, 1.5107, 1.7695, 1.8086, 1.7676, 1.7227, 2.1211, 1.2334, 1.7988,\n",
      "        1.6680, 1.9131, 1.6572, 2.0078, 1.5488, 1.5127, 0.6113, 1.7178, 1.7129,\n",
      "        1.6475, 1.7900, 1.5322, 1.7783, 1.5459, 1.8477, 1.5088, 1.5645, 1.5195,\n",
      "        1.7314, 1.6270, 1.5479, 1.6338, 1.4473, 2.0996, 1.5410, 1.9131, 1.5420,\n",
      "        1.7695, 1.6738, 1.3926, 1.8418, 1.8789, 1.7490, 1.8262, 1.7539, 1.6660,\n",
      "        1.9219, 1.9082, 1.8047, 1.6504, 1.9229, 1.4229, 1.4346, 1.5508, 1.6904,\n",
      "        1.8604, 1.4209, 1.7432, 1.5488, 2.0625, 1.7861, 1.7607, 1.5615, 1.6650,\n",
      "        1.8945, 1.6279, 1.6025, 1.8252, 1.6270, 1.5127, 1.9678, 1.5049, 1.7188,\n",
      "        1.5684, 1.6680, 1.4678, 1.3975, 1.5703, 1.7422, 1.7617, 1.8174, 1.6631,\n",
      "        1.4971, 1.6523, 1.8984, 1.0068, 1.7734, 1.7324, 1.9707, 1.6680, 1.5801,\n",
      "        1.5762, 1.8418, 1.8623, 1.2100, 2.4609, 1.8701], requires_grad=True)\n",
      "model.decoder.layers.1.final_layer_norm.bias Parameter containing:\n",
      "tensor([ 2.2903e-02, -1.0620e-01, -1.6980e-01, -2.4805e-01, -3.0981e-01,\n",
      "        -1.7285e-01, -1.5820e+00, -2.8101e-01,  6.6895e-02,  4.6265e-01,\n",
      "        -7.9443e-01,  4.1382e-01,  2.2217e-01,  5.5908e-01,  3.7598e-01,\n",
      "         6.1621e-01,  1.0822e-01, -8.9453e-01, -3.4102e+00,  2.9712e-01,\n",
      "        -3.8110e-01,  1.0309e-01, -3.7988e-01,  3.5083e-01, -3.8062e-01,\n",
      "         2.0728e-01, -1.3013e-01, -4.1968e-01, -2.8638e-01,  6.9922e-01,\n",
      "         3.1055e-01, -4.9487e-01,  1.0312e+00, -2.0532e-01,  1.0025e-02,\n",
      "         4.0009e-02,  1.4902e+00,  7.9041e-02,  3.1689e-01,  2.3828e-01,\n",
      "         3.9990e-01,  4.3335e-01,  6.1475e-01,  3.0835e-01,  3.0713e-01,\n",
      "        -2.4695e-01, -4.2065e-01, -5.7220e-02, -5.6348e-01,  4.3457e-01,\n",
      "         1.3940e-01, -7.4023e-01, -5.3223e-01,  1.9702e-01, -2.1765e-01,\n",
      "        -5.2832e-01, -7.8809e-01,  5.4810e-02, -5.5078e-01, -8.2031e-02,\n",
      "        -4.1699e-01, -7.7515e-02,  5.0732e-01,  1.8225e-01,  2.4084e-01,\n",
      "        -5.1346e-03,  4.1553e-01,  5.6641e-02,  1.0101e-02, -1.3623e-01,\n",
      "        -1.5271e-01, -4.3262e-01, -1.2842e-01,  1.2275e+00, -4.6045e-01,\n",
      "        -1.8420e-01,  3.7158e-01,  4.7607e-01, -9.2346e-02, -2.0496e-01,\n",
      "        -3.6792e-01, -4.4922e-01, -1.4580e-02, -7.0068e-02,  7.8174e-01,\n",
      "         2.8351e-02, -2.6660e-01,  1.6199e-01,  5.6445e-01, -3.0688e-01,\n",
      "        -3.0103e-01,  1.9397e-01,  2.9443e-01,  3.0103e-01,  4.1382e-01,\n",
      "         1.0919e-01,  3.8452e-01,  6.8945e-01,  3.4302e-01,  3.7183e-01,\n",
      "        -7.5830e-01, -1.7070e+00,  1.1951e-01, -1.0443e-01,  3.4485e-02,\n",
      "        -3.8062e-01,  1.1658e-02,  1.7969e-01,  1.1797e+00,  5.4736e-01,\n",
      "        -3.4082e-01, -2.1570e-01, -1.8274e-01, -1.9714e-01, -1.5833e-01,\n",
      "        -5.2246e-01, -1.6077e-01, -5.4541e-01,  1.0583e-01, -4.2578e-01,\n",
      "        -6.0742e-01, -4.3091e-01, -4.2432e-01, -4.1650e-01,  2.9468e-01,\n",
      "        -4.4739e-02, -8.2471e-01, -4.8560e-01,  1.2805e-01, -2.0905e-02,\n",
      "        -2.3657e-01,  7.6904e-01,  4.7461e-01, -1.4062e-01,  1.4001e-01,\n",
      "        -6.5137e-01,  2.9370e-01,  6.4697e-01, -3.7573e-01, -6.5527e-01,\n",
      "         6.6895e-02,  2.7863e-02,  2.5244e-01,  1.8570e-02, -5.3027e-01,\n",
      "         2.9785e-01,  6.3770e-01, -4.6387e-01,  8.2275e-01, -7.7332e-02,\n",
      "         4.1772e-01,  3.8062e-01,  4.1675e-01,  7.9834e-01, -3.4149e-02,\n",
      "        -5.8398e-01,  2.2314e-01,  1.7871e-01, -8.7952e-02,  2.1802e-01,\n",
      "         5.6641e-01,  3.4180e-01,  4.3774e-01, -9.5062e-03,  6.8018e-01,\n",
      "         1.5466e-01, -3.7427e-01,  3.0566e-01, -1.6760e-01, -5.2637e-01,\n",
      "        -3.4546e-01,  1.6787e+00,  5.8350e-01,  1.8298e-01, -1.0547e-01,\n",
      "         1.8402e-02, -7.1960e-02,  4.2383e-01,  4.4617e-02, -6.3330e-01,\n",
      "        -3.5645e-01,  6.2598e-01, -1.1367e+00,  4.3921e-01, -6.4844e-01,\n",
      "        -2.7759e-01,  2.8174e-01, -3.0200e-01, -2.7075e-01,  2.4780e-02,\n",
      "        -2.1797e+00,  1.0535e-01, -2.4219e-01,  5.4004e-01,  1.3965e+00,\n",
      "         2.0239e-01, -2.8931e-01,  4.4800e-01, -1.0748e-01,  3.1738e-01,\n",
      "        -2.3828e-01, -2.1997e-01,  1.6586e-02, -1.7900e+00,  1.8377e-03,\n",
      "         7.3975e-02, -3.1641e-01,  1.9678e-01,  1.5271e-01,  3.6035e-01,\n",
      "         6.5857e-02,  4.3457e-01,  4.6783e-02,  5.7764e-01, -1.1554e-01,\n",
      "         1.2756e-01, -9.4727e-01, -6.4758e-02,  2.5909e-02, -1.8433e-01,\n",
      "         2.5293e-01, -4.8279e-02, -4.6045e-01, -4.6436e-01,  2.6392e-01,\n",
      "         1.2915e-01,  3.7524e-01,  1.8448e-02, -1.8091e-01,  8.8135e-02,\n",
      "         5.7861e-01,  5.3125e-01,  1.9958e-01, -2.6099e-01,  1.6101e-01,\n",
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      "         8.1543e-02,  1.2610e-01, -1.9180e-02,  3.3130e-01, -2.2900e-01,\n",
      "        -8.6670e-02,  4.2261e-01,  2.6099e-01, -8.8806e-03,  1.7615e-01,\n",
      "        -1.9424e-02,  2.7496e-02,  4.1351e-03, -7.0251e-02,  1.1084e-01,\n",
      "         7.6660e-01, -1.1151e-01, -1.2494e-01,  3.0014e-02],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.2.self_attn.out_proj.weight Parameter containing:\n",
      "tensor([[-3.1114e-04, -7.4005e-03,  8.0764e-05,  ..., -1.7014e-02,\n",
      "         -2.0584e-02,  3.3142e-02],\n",
      "        [-3.3173e-02, -9.4910e-03, -9.2773e-03,  ...,  4.1885e-03,\n",
      "          1.5007e-02,  1.8330e-03],\n",
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      "         -4.4586e-02, -3.3020e-02],\n",
      "        ...,\n",
      "        [ 1.3371e-03,  1.6117e-03, -1.1520e-02,  ...,  1.3527e-02,\n",
      "         -2.1912e-02, -5.4626e-02],\n",
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      "         -1.0181e-01, -1.0490e-02],\n",
      "        [ 3.9124e-04,  2.1255e-02, -7.4196e-03,  ...,  6.2523e-03,\n",
      "          7.7400e-03, -6.5918e-02]], requires_grad=True)\n",
      "model.decoder.layers.2.self_attn.out_proj.bias Parameter containing:\n",
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      "         5.7983e-02,  1.4343e-01,  8.3374e-02, -1.1829e-01, -2.1191e-01,\n",
      "        -1.6953e-02, -7.8064e-02, -4.2389e-02,  5.6076e-03, -2.4536e-02,\n",
      "        -2.2903e-02,  2.0410e-01, -1.3794e-01, -4.9561e-02,  1.1743e-01,\n",
      "        -1.1810e-01, -4.3610e-02, -1.7529e-01,  5.1384e-03, -1.7017e-01,\n",
      "         6.5674e-02,  2.2598e-02,  8.0908e-01, -1.9287e-01, -9.0088e-02,\n",
      "         3.8696e-01, -1.5271e-01,  5.9082e-02, -1.2012e-01,  3.2257e-02,\n",
      "        -1.7529e-01,  8.2245e-03,  3.0957e+00, -7.8003e-02, -9.1614e-02,\n",
      "         1.0657e-01, -1.3696e-01,  1.3110e-01, -1.3403e-01,  5.2612e-02,\n",
      "        -1.2695e-02,  1.0109e-02,  1.0254e-01,  6.4575e-02],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.2.self_attn_layer_norm.weight Parameter containing:\n",
      "tensor([0.9277, 1.9434, 1.5840, 1.0830, 1.1309, 0.9473, 1.2227, 1.4209, 0.9170,\n",
      "        1.1533, 1.7461, 1.4873, 1.5322, 1.3789, 1.3154, 1.5889, 1.2617, 1.3135,\n",
      "        1.8115, 1.1992, 1.4297, 1.4297, 0.8354, 1.5889, 1.4893, 1.0908, 1.3730,\n",
      "        1.4209, 1.1270, 1.0186, 1.5986, 1.1201, 1.3320, 2.7637, 0.9253, 1.2773,\n",
      "        1.0557, 1.3457, 1.3066, 1.9033, 1.5264, 1.0293, 1.3018, 1.6592, 1.4365,\n",
      "        1.1270, 1.4805, 1.1836, 1.0703, 1.5674, 1.2021, 0.4348, 1.3438, 1.6963,\n",
      "        1.9961, 1.1221, 1.4082, 1.5146, 1.4678, 1.4326, 1.0254, 2.0059, 0.9312,\n",
      "        1.4268, 1.3311, 1.0879, 1.4619, 1.5254, 1.5449, 1.5449, 1.1523, 0.9224,\n",
      "        1.3496, 0.5967, 1.2480, 1.1338, 1.1904, 1.1963, 0.8960, 1.4482, 1.3438,\n",
      "        1.7520, 1.3164, 1.0801, 1.2393, 1.3164, 1.1123, 1.2354, 1.2197, 1.3223,\n",
      "        1.1240, 1.6416, 1.7236, 2.2051, 1.0312, 1.0645, 0.9385, 1.1396, 1.5596,\n",
      "        1.3555, 1.2900, 1.0293, 0.4846, 1.0732, 0.9233, 0.9761, 1.1475, 0.9824,\n",
      "        0.9766, 1.0420, 1.9033, 1.5459, 1.0234, 2.8105, 1.1377, 1.0850, 1.5723,\n",
      "        1.4600, 1.0146, 1.4990, 0.9443, 1.8047, 1.3252, 1.4688, 1.2314, 1.3369,\n",
      "        2.0840, 1.0547, 1.3174, 1.1328, 1.3213, 0.9194, 1.5088, 1.0781, 1.4893,\n",
      "        1.5332, 1.2021, 1.2871, 1.2090, 1.0400, 0.8804, 1.4941, 1.1768, 1.0645,\n",
      "        1.0078, 1.4248, 0.9570, 1.1787, 1.0508, 1.1357, 1.5283, 1.5488, 1.2383,\n",
      "        1.0693, 1.3994, 1.3027, 1.0215, 1.6777, 0.9404, 1.6553, 1.1904, 0.9746,\n",
      "        1.4814, 1.1826, 1.3408, 1.2881, 1.8486, 1.1553, 1.5732, 1.0430, 1.3594,\n",
      "        3.7422, 1.3428, 1.3779, 2.0977, 1.2910, 1.3398, 1.1416, 1.2490, 1.2656,\n",
      "        1.1172, 1.0576, 1.2080, 1.0312, 0.9424, 0.8608, 0.7930, 1.0830, 1.5420,\n",
      "        1.4336, 0.6045, 1.0146, 1.6504, 1.0479, 1.2197, 0.9141, 1.2783, 1.4355,\n",
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      "        2.1426, 1.4512, 1.1260, 1.4990, 1.3076, 1.9707, 0.8652, 1.3154, 0.9346,\n",
      "        1.1270, 1.2998, 1.5518, 1.5742, 1.5156, 1.3271, 0.9351, 1.3418, 1.2217,\n",
      "        0.7622, 1.6104, 1.1328, 3.2988, 2.1543, 0.9473, 1.2021, 1.2822, 2.1152,\n",
      "        1.6699, 1.1631, 2.4473, 1.1357, 1.4570, 0.9683, 0.5767, 0.9678, 1.4775,\n",
      "        1.2021, 1.4521, 1.5322, 1.1025, 1.4199, 1.4639, 1.2734, 1.4316, 1.3867,\n",
      "        1.0908, 1.1543, 1.2031, 1.2363, 1.4277, 1.7549, 1.2295, 1.1650, 1.3652,\n",
      "        1.2559, 0.8892, 0.8452, 0.8638, 0.9297, 0.9834, 1.0205, 1.4111, 1.9111,\n",
      "        1.1689, 1.1855, 1.2969, 1.8389, 1.3564, 1.1621, 1.5928, 0.8447, 1.1641,\n",
      "        1.3057, 1.4277, 1.4121, 0.8906, 1.0234, 1.5176, 1.3506, 1.4561, 1.4277,\n",
      "        1.3135, 1.7969, 1.1758, 1.2432, 1.2021, 1.1143, 1.5410, 1.7129, 1.0449,\n",
      "        1.0703, 1.3389, 1.6191, 0.5239, 0.9502, 0.8945, 5.4336, 1.2627, 1.2598,\n",
      "        1.3135, 1.4160, 0.8281, 1.4443, 0.9126, 1.4639, 1.9268, 0.9966, 1.6367,\n",
      "        1.0127, 1.0264, 0.9312, 1.2119, 0.9131, 1.4971, 0.9268, 1.5703, 0.9678,\n",
      "        1.5566, 1.4102, 1.4512, 1.3789, 1.3887, 1.5371, 1.1895, 1.0850, 1.1582,\n",
      "        1.2900, 1.7617, 0.9624, 1.1963, 1.2832, 0.8906, 1.7490, 0.9517, 1.3623,\n",
      "        1.1182, 0.7271, 1.0029, 1.3398, 1.4336, 1.3076, 0.9741, 0.8657, 1.1611,\n",
      "        0.9795, 1.0811, 1.4521, 1.2959, 1.1270, 1.5879, 1.1709, 1.6016, 1.0811,\n",
      "        1.0479, 1.6436, 0.4907, 0.9009, 1.7539, 0.9482, 1.5713, 1.3633, 1.0449,\n",
      "        1.1279, 1.1338, 1.3955, 0.5879, 1.1836, 1.1738, 1.2012, 1.1523, 0.8223,\n",
      "        1.3418, 1.2900, 1.1348, 1.8730, 1.8701, 1.1719], requires_grad=True)\n",
      "model.decoder.layers.2.self_attn_layer_norm.bias Parameter containing:\n",
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      "        -2.2009e-01,  2.6459e-02, -1.1517e-01, -9.3506e-02],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.2.encoder_attn.k_proj.weight Parameter containing:\n",
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      "        [ 0.0351, -0.0015,  0.0073,  ..., -0.0156, -0.0711, -0.0253],\n",
      "        [ 0.0229,  0.0862, -0.0495,  ..., -0.0062, -0.0919,  0.0370],\n",
      "        ...,\n",
      "        [ 0.0199,  0.0082,  0.0133,  ..., -0.0172,  0.0249, -0.0259],\n",
      "        [ 0.0040,  0.0237, -0.0285,  ..., -0.0083, -0.0044, -0.0173],\n",
      "        [-0.0064,  0.0160,  0.0015,  ..., -0.0001, -0.0219,  0.0335]])\n",
      "model.decoder.layers.2.encoder_attn.v_proj.weight Parameter containing:\n",
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      "        ...,\n",
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      "        [-0.0108,  0.0039,  0.0121,  ..., -0.0153, -0.0050, -0.0051]])\n",
      "model.decoder.layers.2.encoder_attn.v_proj.bias Parameter containing:\n",
      "tensor([ 8.8263e-04, -4.4891e-02,  6.4545e-03,  5.7220e-03,  2.7710e-02,\n",
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      "        -7.3059e-02,  1.0638e-01, -2.5436e-02,  3.4790e-01,  5.9326e-01,\n",
      "         1.3562e-01,  8.9355e-02, -7.1899e-02,  6.6528e-02, -4.6973e-01,\n",
      "        -2.8397e-02,  7.2815e-02,  1.2732e-01,  1.7126e-01, -1.8494e-01,\n",
      "        -2.7173e-01,  1.4551e-01,  3.3545e-01, -8.5388e-02, -2.5391e-01,\n",
      "        -1.5173e-01, -2.2021e-01,  5.2986e-03,  9.6008e-02, -3.0981e-01,\n",
      "         1.6040e-01, -1.2244e-01, -4.6216e-01,  5.3174e-01,  7.9956e-02,\n",
      "         6.0272e-03, -3.0151e-01, -2.8906e-01,  3.4790e-01, -1.2830e-01,\n",
      "        -1.5283e-01,  2.0972e-01,  3.0054e-01,  5.3482e-03, -1.6711e-01,\n",
      "        -3.8770e-01, -1.6211e-01, -1.6223e-01,  1.4145e-02, -1.8511e-03,\n",
      "        -1.6541e-01,  3.3722e-02,  1.6162e-01, -5.2063e-02, -5.5603e-02,\n",
      "         5.4382e-02, -1.2781e-01,  1.7712e-01, -2.3117e-02, -2.0190e-01,\n",
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      "        -2.4768e-01,  7.8979e-02,  5.5328e-02,  8.5205e-01, -6.3965e-02,\n",
      "         4.3726e-01,  9.1431e-02,  2.1350e-01, -4.5776e-02, -3.0884e-01,\n",
      "        -1.6699e-01, -3.1470e-01, -4.9219e-01,  1.5793e-02, -1.1823e-01,\n",
      "         3.6804e-02, -7.2205e-02,  1.0114e-01, -3.0322e-01, -3.3691e-01,\n",
      "         9.0637e-02,  1.9556e-01, -1.8323e-01, -2.1790e-01, -4.9561e-02,\n",
      "         1.5662e-01, -7.3624e-04,  9.3689e-02, -2.1790e-02,  4.9622e-02,\n",
      "         2.5098e-01,  5.4901e-02,  7.6477e-02, -9.8633e-02,  2.5903e-01,\n",
      "         2.2278e-02,  3.0005e-01,  1.7029e-01,  1.2482e-02,  5.4596e-02,\n",
      "        -1.0333e-01,  2.3523e-01, -1.2030e-01, -1.2671e-01,  5.9131e-01,\n",
      "        -4.5441e-02, -2.3804e-01, -5.7526e-02, -2.9810e-01,  2.2797e-02,\n",
      "         3.2983e-01,  1.1786e-01, -1.5442e-01, -3.3301e-01,  2.5244e-01,\n",
      "         1.0028e-01, -4.9609e-01,  2.5562e-01,  2.9678e-02, -3.3325e-01,\n",
      "         1.8433e-01,  1.0785e-01,  3.5400e-02,  2.3315e-01, -2.3340e-01,\n",
      "        -5.6592e-01,  2.2131e-01,  1.8054e-01, -5.5420e-01, -1.3351e-02,\n",
      "         8.8959e-03,  1.0333e-01, -6.4209e-02,  1.5979e-01, -1.0510e-01,\n",
      "         2.2180e-01, -8.6914e-02,  5.6396e-01, -9.2163e-02, -2.0789e-01,\n",
      "         7.4585e-02,  4.8676e-02,  2.4402e-01, -1.0109e-02, -7.1289e-02,\n",
      "        -2.0178e-01,  2.9190e-02, -1.4145e-02,  3.0243e-02,  5.8887e-01,\n",
      "        -3.1250e-01,  1.2622e-01,  1.1908e-01,  5.7800e-02,  8.4839e-02,\n",
      "         2.6904e-01,  3.0542e-01,  1.6931e-01, -1.9519e-01,  4.4653e-01,\n",
      "        -8.7891e-02,  9.6191e-02, -4.3140e-01,  1.7017e-01, -2.1582e-01,\n",
      "         2.6260e-02, -9.9915e-02,  6.9519e-02,  7.5928e-02, -2.3022e-01,\n",
      "         2.3767e-01,  1.1517e-01,  1.8896e-01,  1.1249e-01, -2.2614e-02,\n",
      "         9.3140e-02,  6.2744e-02,  1.7383e-01,  1.1230e-01, -1.9638e-02,\n",
      "         1.5930e-01, -1.1713e-01,  1.2524e-01,  4.9683e-02, -2.6245e-01,\n",
      "        -7.5928e-02,  1.2073e-01,  2.4292e-01,  6.8115e-02,  2.2351e-01,\n",
      "         5.2299e-03,  1.0052e-01, -5.6152e-02,  6.8481e-02,  4.1534e-02,\n",
      "        -6.8359e-02, -3.6084e-01,  3.6914e-01,  2.8955e-01,  4.1687e-02,\n",
      "        -3.0762e-01, -2.6001e-02, -1.9910e-01, -2.3438e-02, -1.7737e-01,\n",
      "        -2.5195e-01,  3.2196e-02, -8.1177e-03, -8.8196e-02])\n",
      "model.decoder.layers.2.encoder_attn_layer_norm.weight Parameter containing:\n",
      "tensor([1.6533, 5.0938, 1.6572, 1.3057, 2.2637, 1.1846, 1.4521, 4.4570, 1.5566,\n",
      "        1.3574, 1.8574, 2.3652, 2.5293, 2.3398, 4.1250, 2.2188, 2.1914, 2.0195,\n",
      "        2.6816, 1.4238, 2.3613, 4.1719, 1.3203, 4.3672, 2.8477, 1.5303, 2.1230,\n",
      "        2.0449, 1.8242, 3.2988, 2.2148, 1.7275, 2.2559, 6.8867, 1.1191, 1.6660,\n",
      "        1.8213, 4.0391, 2.7324, 5.4336, 1.9814, 1.5049, 1.5469, 3.9824, 2.2910,\n",
      "        1.5771, 4.8867, 1.7021, 1.8203, 3.3848, 1.8535, 0.9033, 2.2578, 2.8184,\n",
      "        4.5156, 2.4199, 1.7002, 2.8047, 2.6309, 3.4160, 1.8691, 6.0195, 1.4453,\n",
      "        1.6865, 1.7676, 2.0977, 2.3125, 2.3125, 1.8701, 1.9092, 1.7334, 1.7393,\n",
      "        3.3574, 1.8066, 2.0312, 1.7949, 1.6191, 1.8164, 1.3418, 2.2949, 1.5557,\n",
      "        3.3418, 3.4492, 2.2480, 1.9072, 2.4414, 2.0625, 2.3359, 1.8525, 2.5312,\n",
      "        1.2861, 3.4883, 1.8877, 5.8633, 1.5273, 1.6514, 1.4375, 1.2568, 3.8965,\n",
      "        1.6953, 1.8330, 1.6094, 1.2871, 1.6738, 1.6289, 1.6543, 1.6738, 2.0234,\n",
      "        1.6133, 2.9570, 4.9766, 1.6094, 1.7412, 8.1016, 2.0059, 1.2725, 1.6396,\n",
      "        4.0508, 1.0391, 2.3457, 1.0332, 2.8672, 2.3809, 3.0410, 1.8008, 1.9795,\n",
      "        2.3125, 1.4590, 1.4844, 1.6748, 2.4004, 1.4404, 2.1562, 1.7246, 1.9346,\n",
      "        2.8926, 1.6426, 2.7891, 1.8730, 1.3672, 1.1270, 2.3008, 2.1035, 1.2832,\n",
      "        1.6592, 2.5801, 1.5078, 1.3291, 1.7314, 1.5674, 2.4238, 2.4922, 2.1855,\n",
      "        2.1309, 1.7002, 2.0684, 1.8125, 5.0195, 1.3301, 3.5703, 1.5088, 1.1865,\n",
      "        1.9980, 1.6572, 1.9834, 1.6641, 4.4570, 2.7461, 3.5078, 1.4541, 2.1250,\n",
      "        1.9580, 1.8682, 1.5898, 5.7148, 2.7578, 1.3975, 1.7842, 3.8184, 1.6836,\n",
      "        1.3740, 2.2109, 1.9150, 1.3096, 1.4980, 1.3408, 1.0488, 1.7158, 3.4492,\n",
      "        1.8682, 1.6943, 1.3564, 2.1348, 1.7461, 1.8857, 1.2012, 2.0625, 2.0781,\n",
      "        2.0020, 2.2539, 2.7949, 5.3086, 1.5771, 1.7969, 3.0449, 1.5674, 5.0039,\n",
      "        4.9727, 2.8828, 2.3926, 3.3066, 2.7441, 5.5078, 1.3193, 1.4922, 2.0605,\n",
      "        1.6289, 2.1055, 3.7324, 1.4658, 1.6279, 1.8398, 1.4561, 1.4785, 1.6621,\n",
      "        1.1338, 3.0254, 1.8027, 8.3047, 5.9219, 1.8994, 1.6357, 2.4570, 4.9062,\n",
      "        4.2148, 1.2988, 6.9609, 1.9023, 3.3340, 1.6943, 1.3789, 1.5410, 1.7891,\n",
      "        1.6074, 1.4873, 2.7383, 1.7373, 2.4395, 2.1113, 1.6523, 3.2090, 1.7676,\n",
      "        1.7646, 1.5537, 1.8760, 1.6777, 2.5254, 3.5625, 1.9336, 3.1074, 1.8359,\n",
      "        1.5371, 1.4336, 1.3623, 1.4141, 2.1797, 1.7725, 1.8213, 1.7715, 4.7266,\n",
      "        2.3789, 2.1348, 2.0488, 3.5762, 1.7578, 2.0723, 3.6582, 1.3936, 1.7109,\n",
      "        1.9326, 1.8994, 1.7549, 1.5801, 1.4180, 1.8027, 2.5742, 2.1777, 1.7588,\n",
      "        1.7803, 3.3652, 1.4521, 1.4922, 1.4746, 1.8691, 1.5859, 5.2344, 1.7480,\n",
      "        1.7266, 1.7041, 2.5488, 1.0996, 1.2490, 1.3818, 2.3477, 1.9365, 1.8154,\n",
      "        1.3848, 2.3203, 1.3779, 2.2773, 1.2676, 1.9404, 4.8477, 1.8848, 2.7754,\n",
      "        1.8281, 1.7148, 1.3984, 2.7812, 1.1816, 1.6094, 1.2236, 2.1465, 1.2617,\n",
      "        2.6016, 2.0566, 3.5469, 1.8281, 1.4912, 1.6572, 2.0039, 2.0586, 1.9883,\n",
      "        1.6748, 2.7246, 1.6465, 2.4531, 1.8467, 1.1309, 4.2578, 1.3213, 1.4912,\n",
      "        1.4629, 1.1924, 2.4883, 2.7695, 2.5469, 2.0645, 1.8379, 1.2803, 1.6602,\n",
      "        1.7451, 1.1895, 2.8125, 1.6797, 2.3848, 4.8359, 1.8057, 3.0586, 2.7266,\n",
      "        1.4883, 2.8965, 1.5928, 1.0684, 4.1680, 1.3213, 1.6992, 2.1289, 1.5322,\n",
      "        2.1016, 1.4785, 2.6172, 0.9341, 1.6064, 2.1289, 1.7832, 1.9453, 1.1104,\n",
      "        2.6680, 2.4258, 1.7031, 5.1641, 2.2988, 1.6338])\n",
      "model.decoder.layers.2.encoder_attn_layer_norm.bias Parameter containing:\n",
      "tensor([-1.3977e-01,  3.0811e-01, -3.0396e-01, -5.9753e-02,  1.6016e-01,\n",
      "        -7.8662e-01, -1.8389e+00, -3.1152e-01,  2.8687e-01,  1.8079e-01,\n",
      "        -2.7881e-01,  1.2903e-01, -2.4805e-01, -4.0161e-01,  2.5439e-01,\n",
      "         1.6516e-01,  4.2267e-02, -1.3086e-01,  1.3447e+00, -3.8062e-01,\n",
      "        -6.1084e-01, -3.6548e-01, -4.0479e-01,  3.0200e-01,  1.3989e-01,\n",
      "         5.6738e-01,  3.5229e-01,  2.7612e-01, -1.1670e-01, -3.5596e-01,\n",
      "         1.7542e-01, -5.0079e-02, -1.4368e-01,  9.9716e-03, -2.1759e-02,\n",
      "        -1.4026e-01,  5.5273e-01,  9.6924e-01,  5.6055e-01, -1.0083e-01,\n",
      "         2.0728e-01, -1.8237e-01,  1.8933e-01,  2.9321e-01,  8.4351e-02,\n",
      "         3.0811e-01,  2.1802e-01, -2.7786e-02, -4.0894e-02,  4.7729e-01,\n",
      "        -2.9956e-01, -5.5481e-02,  3.5913e-01, -2.5195e-01,  3.0591e-01,\n",
      "        -1.4636e-01, -6.4209e-02,  6.1670e-01,  2.6416e-01, -2.1326e-01,\n",
      "        -4.8804e-01, -1.7236e-01,  5.9717e-01, -1.5762e-02,  6.3770e-01,\n",
      "         2.8027e-01, -1.2390e-01,  1.0815e-01,  1.0608e-01,  5.3516e-01,\n",
      "        -6.2500e-01, -3.3960e-01, -1.4246e-01,  8.7988e-01, -6.8359e-01,\n",
      "         2.6685e-01,  2.8198e-01, -1.0767e-01,  4.6533e-01, -6.3293e-02,\n",
      "        -8.6865e-01, -1.8408e-01, -2.0093e-01, -1.0596e-01, -4.7827e-01,\n",
      "        -2.9663e-01,  1.7004e-01,  1.1707e-01, -2.6367e-01, -4.2432e-01,\n",
      "         4.7925e-01,  3.2031e-01,  1.8066e-01,  5.5756e-02, -1.3611e-01,\n",
      "         5.3760e-01,  3.3130e-01,  6.6699e-01,  5.1147e-02, -2.3706e-01,\n",
      "        -5.4443e-01, -1.4121e+00,  1.2463e-01, -1.7896e-01,  2.9083e-02,\n",
      "         2.3193e-01,  4.7974e-01, -6.2805e-02, -3.3887e-01, -6.3184e-01,\n",
      "         1.3281e-01,  3.8916e-01,  4.6338e-01, -2.3889e-01,  3.1738e-01,\n",
      "        -2.7075e-01, -1.5930e-01,  1.4148e-01, -1.8213e-01,  1.5306e-03,\n",
      "        -3.1519e-01,  3.6523e-01, -5.9277e-01, -2.7661e-01,  2.4994e-02,\n",
      "        -8.3313e-02, -3.9795e-01,  5.8105e-01,  5.3528e-02,  1.2610e-01,\n",
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      "        -1.0535e-01,  3.0518e-01, -4.3188e-01, -1.8701e-01, -6.6040e-02,\n",
      "         3.7402e-01,  3.6548e-01, -2.3547e-01, -7.6758e-01, -4.4849e-01,\n",
      "        -4.4824e-01,  1.0400e-01,  4.9097e-01,  2.4829e-01,  3.3887e-01,\n",
      "         2.5391e-01,  1.6370e-01,  4.7089e-02,  2.4915e-01, -2.0178e-01,\n",
      "         3.5797e-02,  1.6003e-01, -3.2397e-01, -1.7273e-01, -4.9048e-01,\n",
      "        -3.8623e-01, -1.5405e-01, -4.3726e-01,  2.5781e-01, -6.5063e-02,\n",
      "        -2.2192e-01,  6.7566e-02,  4.5190e-01, -3.3594e-01,  4.4141e-01,\n",
      "        -3.7170e-02,  7.0459e-01, -3.7280e-01, -5.3955e-01,  5.2051e-01,\n",
      "        -7.7637e-02,  1.6199e-01, -9.5337e-02, -5.2588e-01, -4.6899e-01,\n",
      "         3.0151e-01,  2.1411e-01, -2.4438e-01, -8.7036e-02, -6.3362e-03,\n",
      "        -2.7283e-02, -1.4519e-02,  7.3828e-01, -2.1240e-01,  2.6440e-01,\n",
      "         2.7466e-01,  8.2910e-01, -8.0566e-01,  2.1411e-01,  6.5186e-01,\n",
      "        -3.0502e-02,  1.6248e-01,  6.6650e-02, -6.0760e-02,  4.5563e-02,\n",
      "        -3.2593e-01,  2.2461e-01, -3.0884e-01,  1.8359e-01, -5.8105e-01,\n",
      "        -3.4155e-01,  4.0161e-01,  2.7515e-01, -5.6885e-01, -7.9688e-01,\n",
      "         7.0947e-01,  2.0288e-01, -6.6504e-01, -5.1367e-01, -1.9318e-02,\n",
      "         3.1104e-01,  9.9243e-02,  7.2327e-02,  9.9976e-02, -3.7085e-01,\n",
      "        -1.4587e-01,  2.9639e-01,  1.6223e-01,  3.1909e-01,  1.5894e-01,\n",
      "        -4.5093e-01,  4.0332e-01, -1.8628e-01, -1.7981e-01,  1.0002e-02,\n",
      "         3.4637e-02, -1.6016e-01, -4.1260e-01, -5.6348e-01, -4.2145e-02,\n",
      "        -1.2073e-01,  1.4277e-03, -2.0337e-01, -9.4629e-01,  7.0605e-01,\n",
      "        -1.8201e-01, -1.0986e-01, -5.0928e-01, -2.6758e-01, -4.1107e-02,\n",
      "         2.7023e-02, -3.0493e-01,  9.1992e-01,  1.6833e-01,  4.4287e-01,\n",
      "         6.8359e-01,  1.0687e-01, -5.2734e-01, -4.3701e-01,  1.6101e-01,\n",
      "         6.2988e-02, -2.6685e-01,  8.2520e-02,  1.2863e-02, -2.7905e-01,\n",
      "        -6.6956e-02, -2.6050e-01, -5.3406e-02,  2.5635e-01, -2.5146e-01,\n",
      "        -5.8252e-01,  6.5283e-01,  3.0273e-01,  2.4988e-01, -4.3140e-01,\n",
      "         1.0614e-01, -3.8428e-01, -5.3027e-01, -3.0005e-01,  3.1860e-01,\n",
      "        -5.1221e-01, -4.4312e-01, -8.1494e-01,  1.9934e-01, -9.3018e-01,\n",
      "        -5.7080e-01, -3.9673e-01,  1.1553e+00,  1.4209e+00, -2.0776e-01,\n",
      "        -2.4780e-01,  2.2217e-01,  1.7053e-01,  1.8250e-01, -2.7783e-01,\n",
      "         4.3799e-01,  7.3975e-01,  5.2002e-01,  2.7295e-01, -3.2349e-01,\n",
      "        -5.5725e-02, -3.6182e-01,  4.6118e-01,  7.7051e-01,  1.0146e+00,\n",
      "        -7.0068e-01, -2.1399e-01,  1.5381e-01, -4.1719e+00,  9.0866e-03,\n",
      "        -4.9927e-02,  1.3538e-01, -5.9473e-01, -2.9541e-01,  3.5474e-01,\n",
      "        -3.3911e-01,  2.3340e-01, -6.3525e-01,  2.8882e-01,  3.3789e-01,\n",
      "        -5.1514e-02,  6.6589e-02, -2.2522e-01,  5.6494e-01,  2.0984e-01,\n",
      "         4.2798e-01, -2.6538e-01,  6.2744e-01,  3.8574e-01, -5.4736e-01,\n",
      "         4.2847e-01, -4.9341e-01, -1.9006e-01, -6.0498e-01, -1.3281e-01,\n",
      "        -3.0005e-01, -2.2168e-01, -2.8882e-01, -5.1758e-01, -5.4443e-01,\n",
      "         1.8677e-01, -5.1123e-01,  1.4233e-01,  1.8237e-01,  1.7590e-01,\n",
      "        -4.5801e-01, -3.2056e-01, -4.7290e-01,  5.2441e-01, -2.8662e-01,\n",
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      "        -3.1250e-01, -1.5002e-01, -9.8633e-01, -4.1528e-01])\n",
      "model.decoder.layers.2.fc1.weight Parameter containing:\n",
      "tensor([[-0.0232, -0.0186, -0.0078,  ..., -0.0018,  0.0277, -0.0107],\n",
      "        [-0.0571,  0.0375,  0.0089,  ...,  0.0361,  0.0094,  0.0284],\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.2.fc1.bias Parameter containing:\n",
      "tensor([-0.5015, -2.4297, -2.6855,  ..., -2.2383, -1.5996, -1.2207],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.2.fc2.weight Parameter containing:\n",
      "tensor([[ 2.8809e-02, -3.6713e-02,  4.7699e-02,  ..., -3.9558e-03,\n",
      "         -7.0751e-05,  6.5155e-03],\n",
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      "          1.6876e-02, -1.7500e-03]], requires_grad=True)\n",
      "model.decoder.layers.2.fc2.bias Parameter containing:\n",
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      "         3.7280e-01,  1.9775e-02,  7.4036e-02,  3.1348e-01,  1.1467e-02,\n",
      "         1.6675e-01, -1.1487e-01,  1.3965e-01,  3.2425e-03],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.2.final_layer_norm.weight Parameter containing:\n",
      "tensor([1.6699, 1.1484, 1.8398, 1.8037, 1.3506, 1.5527, 1.7793, 1.3301, 1.5898,\n",
      "        1.6523, 1.6387, 1.6416, 1.5078, 1.5635, 1.2100, 1.7861, 1.5986, 1.9062,\n",
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      "        1.5322, 1.7666, 1.7939, 1.7617, 1.7227, 1.7930, 1.1406, 1.7041, 1.6641,\n",
      "        1.6250, 1.5186, 1.7676, 1.4141, 1.8691, 1.6396, 1.7783, 1.1680, 1.6562,\n",
      "        1.5479, 1.7637, 1.4600, 2.0664, 1.4951, 1.6348, 0.8394, 1.6348, 1.6592,\n",
      "        1.7383, 1.6191, 1.5459, 1.5898, 1.5068, 1.7412, 1.1055, 1.5947, 1.4932,\n",
      "        1.7158, 1.6641, 1.5293, 1.5146, 1.4561, 1.7051, 1.5840, 1.7344, 1.6592,\n",
      "        1.3018, 1.4873, 1.4121, 1.5273, 1.6816, 1.7061, 1.5527, 1.5430, 1.5420,\n",
      "        1.7490, 1.5498, 1.9453, 1.3008, 1.4482, 1.5166, 1.3203, 1.5781, 1.7705,\n",
      "        1.6895, 1.4902, 1.5322, 1.4385, 1.7617, 1.6748, 1.5908, 1.6787, 1.6387,\n",
      "        1.5928, 1.6465, 1.5303, 1.7705, 1.5586, 1.1855, 1.7080, 1.5674, 1.4082,\n",
      "        1.6611, 1.7041, 1.8936, 1.5273, 1.4434, 1.5693, 1.7090, 1.7676, 1.6816,\n",
      "        1.3594, 1.6006, 1.4912, 1.5449, 1.5469, 1.6592, 1.6602, 1.5771, 1.6074,\n",
      "        1.3574, 1.5566, 1.9434, 1.3760, 2.7051, 1.9072], requires_grad=True)\n",
      "model.decoder.layers.2.final_layer_norm.bias Parameter containing:\n",
      "tensor([ 1.6675e-01,  7.5867e-02,  1.5088e-01, -9.7778e-02,  7.6514e-01,\n",
      "         8.3923e-02, -1.0469e+00, -6.0742e-01,  2.3279e-01, -3.6182e-01,\n",
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      "         1.0439e+00, -6.3525e-01,  6.9531e-01, -1.3342e-01,  2.8491e-01,\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.3.self_attn.q_proj.weight Parameter containing:\n",
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      "          3.9124e-02,  7.0190e-02]], requires_grad=True)\n",
      "model.decoder.layers.3.self_attn.q_proj.bias Parameter containing:\n",
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      "       requires_grad=True)\n",
      "model.decoder.layers.3.self_attn.out_proj.weight Parameter containing:\n",
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      "          8.8120e-03, -1.4122e-02],\n",
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      "        ...,\n",
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      "          5.5389e-03, -2.4429e-02]], requires_grad=True)\n",
      "model.decoder.layers.3.self_attn.out_proj.bias Parameter containing:\n",
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      "        -6.9434e-01,  3.2617e-01, -2.3901e-01,  3.7427e-01, -3.1079e-01,\n",
      "         9.8511e-02, -1.0754e-01, -2.1912e-01, -3.3569e-01, -8.9062e-01,\n",
      "         6.4307e-01,  3.6963e-01, -9.3994e-01, -1.2390e-01, -4.7089e-02,\n",
      "         8.0469e-01, -3.1738e-02,  7.2461e-01,  1.9458e-01,  5.1941e-02,\n",
      "         3.2227e-01,  3.7280e-01, -8.7812e+00,  3.3081e-01, -1.9409e-01,\n",
      "        -9.6008e-02, -1.5417e-01,  8.0078e-01, -4.5581e-01,  1.6492e-01,\n",
      "        -2.0276e-01,  5.6250e-01, -4.1412e-02,  1.7212e-02],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.3.self_attn_layer_norm.weight Parameter containing:\n",
      "tensor([1.7119, 2.2305, 1.7646, 1.7969, 1.8926, 1.7812, 2.2402, 2.1406, 1.7725,\n",
      "        1.9932, 2.0254, 1.8369, 1.9980, 1.8271, 2.1348, 1.8535, 1.8975, 1.6992,\n",
      "        1.6699, 1.7520, 1.8721, 2.0566, 1.8838, 2.1484, 1.8379, 1.8535, 1.7461,\n",
      "        1.8691, 1.7422, 1.8086, 1.7539, 1.5674, 2.1641, 2.5664, 1.7822, 1.7881,\n",
      "        1.8682, 2.1055, 1.7412, 2.1680, 1.8066, 1.8203, 1.8262, 1.9795, 1.8691,\n",
      "        1.8242, 2.1699, 1.8867, 1.7354, 2.1523, 2.1426, 1.7402, 1.9316, 2.0820,\n",
      "        2.1406, 1.8721, 1.9092, 1.8730, 1.8389, 1.8955, 1.7021, 2.0625, 1.4102,\n",
      "        1.8564, 1.7910, 1.8809, 1.8076, 1.9873, 1.8818, 1.8418, 1.8330, 1.8916,\n",
      "        2.0586, 1.1084, 1.8955, 2.0352, 1.6543, 1.9053, 1.7607, 1.8037, 1.9951,\n",
      "        1.8369, 1.9336, 1.8037, 2.0293, 1.8721, 1.9141, 1.9355, 1.7666, 1.8809,\n",
      "        1.8281, 1.7686, 1.6064, 2.2422, 1.9053, 1.9805, 1.7441, 1.8604, 2.1660,\n",
      "        1.8779, 1.6045, 1.8486, 1.4336, 1.8896, 1.5527, 1.5020, 1.8906, 1.9238,\n",
      "        1.6494, 1.9238, 2.0449, 1.9473, 1.9082, 2.0820, 1.8691, 1.6250, 1.8789,\n",
      "        1.9873, 1.7695, 1.8877, 1.8594, 2.0098, 1.9551, 2.0312, 1.8779, 1.8740,\n",
      "        1.7686, 1.8525, 1.7158, 1.7305, 1.7627, 1.7490, 1.7695, 1.6143, 1.8916,\n",
      "        2.0352, 1.7900, 1.8467, 1.8115, 1.6074, 1.6406, 1.9482, 1.8760, 1.9629,\n",
      "        1.7031, 1.8438, 1.7588, 1.6074, 1.7461, 1.8516, 1.8896, 1.8721, 1.8750,\n",
      "        1.4629, 1.7207, 1.9297, 1.7236, 2.0312, 1.7969, 2.1816, 1.7744, 1.7178,\n",
      "        1.6631, 1.6338, 1.8262, 1.7422, 1.8779, 1.7568, 1.9521, 1.6758, 1.8984,\n",
      "        2.8594, 1.8320, 1.8467, 2.0430, 1.8818, 1.8350, 2.0352, 2.1094, 1.8105,\n",
      "        2.0195, 1.8789, 1.8145, 1.8418, 1.4131, 1.6826, 1.7188, 1.8115, 1.8818,\n",
      "        1.7578, 0.9941, 1.9316, 1.9795, 1.7891, 1.6240, 1.7686, 1.7344, 1.8145,\n",
      "        1.9326, 1.9854, 1.9268, 2.2695, 2.0098, 1.7285, 1.9189, 1.6924, 2.1562,\n",
      "        2.1250, 1.8477, 2.0332, 2.0117, 2.1914, 2.0234, 1.8740, 1.8975, 1.5029,\n",
      "        1.7568, 1.8613, 2.1738, 1.9473, 1.8242, 1.8125, 1.7520, 1.8613, 2.0117,\n",
      "        1.3730, 1.8281, 1.7959, 1.9785, 2.2129, 1.8320, 1.9424, 1.7725, 2.0508,\n",
      "        2.0684, 1.9092, 2.3672, 1.7900, 1.6025, 1.8525, 1.1455, 2.0117, 1.9434,\n",
      "        1.7949, 1.8516, 1.7295, 1.8652, 1.6084, 1.7686, 1.7070, 1.9434, 1.8105,\n",
      "        1.8145, 1.8711, 1.8721, 1.5732, 1.9238, 1.9697, 1.8994, 1.9404, 1.7871,\n",
      "        1.7871, 1.8281, 1.7852, 1.6133, 1.4180, 1.7715, 1.4980, 1.8945, 2.0840,\n",
      "        1.9072, 1.9756, 1.8359, 2.1094, 1.8193, 1.7549, 1.9336, 1.5186, 1.8271,\n",
      "        1.6250, 1.9229, 1.7832, 1.7051, 1.5586, 1.8809, 2.1152, 1.8477, 1.8164,\n",
      "        1.6660, 2.0391, 1.9873, 1.8447, 1.8096, 1.8770, 1.8730, 2.2500, 1.9209,\n",
      "        2.0352, 1.7910, 2.0469, 1.3096, 1.9180, 1.8877, 4.8203, 1.8750, 1.9170,\n",
      "        1.9717, 1.6992, 1.7109, 1.8887, 1.6953, 1.9893, 1.9951, 1.9111, 1.9404,\n",
      "        1.8242, 1.8477, 1.7021, 1.8857, 1.6104, 1.9043, 1.8398, 1.9795, 1.6533,\n",
      "        1.8311, 1.8477, 1.9854, 1.8018, 1.9551, 1.9609, 1.8682, 1.8145, 1.8311,\n",
      "        1.7607, 1.8750, 1.5859, 1.6543, 1.9883, 1.7119, 2.0664, 1.8154, 1.8389,\n",
      "        1.8145, 1.6016, 1.6719, 2.0312, 1.6387, 1.7842, 1.7051, 1.7051, 1.8916,\n",
      "        1.7393, 1.8105, 1.9717, 1.7842, 2.0332, 2.0605, 1.7803, 1.9355, 1.7510,\n",
      "        1.7861, 1.9209, 1.5234, 1.7305, 1.8916, 1.7236, 1.8789, 1.8896, 1.9053,\n",
      "        1.8916, 1.4854, 1.8154, 0.7373, 1.8008, 2.0020, 1.6064, 1.8154, 1.7646,\n",
      "        1.8252, 1.7441, 1.8408, 2.0742, 2.0664, 1.6963], requires_grad=True)\n",
      "model.decoder.layers.3.self_attn_layer_norm.bias Parameter containing:\n",
      "tensor([-5.0879e-01,  3.3020e-02, -1.7563e-02, -1.2512e-01, -6.6406e-02,\n",
      "        -3.4644e-01, -1.0244e+00, -6.3599e-02, -2.7075e-01, -2.3511e-01,\n",
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      "         3.1445e-01, -3.6157e-01,  5.0125e-03, -2.2751e-02, -1.5564e-01,\n",
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      "         3.1342e-02,  1.7136e-02,  2.1204e-01,  9.5642e-02, -2.9199e-01,\n",
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      "        -1.2915e-01, -9.5520e-02, -3.4399e-01, -8.5815e-02, -2.4915e-01,\n",
      "        -3.5229e-01,  2.1729e-01,  4.2456e-01, -1.9434e-01, -1.9861e-01,\n",
      "         3.2544e-01, -1.3672e-01, -2.7710e-01, -2.1228e-01,  1.3379e-01,\n",
      "        -1.7578e-02, -1.8750e-01,  5.5312e+00, -8.1177e-02, -8.2825e-02,\n",
      "        -1.4856e-01, -3.0054e-01, -4.0088e-01, -1.9385e-01,  1.5942e-01,\n",
      "        -6.3843e-02,  3.6304e-01,  1.7432e-01,  8.7204e-03],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.3.encoder_attn.k_proj.weight Parameter containing:\n",
      "tensor([[-0.0531, -0.0146, -0.0245,  ...,  0.0444, -0.0345,  0.0193],\n",
      "        [ 0.0124, -0.0141,  0.0292,  ..., -0.0208,  0.0204, -0.0055],\n",
      "        [-0.0660, -0.0278,  0.0972,  ...,  0.0348, -0.0348,  0.0021],\n",
      "        ...,\n",
      "        [ 0.0016, -0.0020, -0.0046,  ...,  0.0112, -0.0034,  0.0121],\n",
      "        [-0.0025,  0.0093, -0.0007,  ...,  0.0276, -0.0039,  0.0067],\n",
      "        [-0.0286,  0.0023, -0.0110,  ...,  0.0282, -0.0130, -0.0096]])\n",
      "model.decoder.layers.3.encoder_attn.v_proj.weight Parameter containing:\n",
      "tensor([[-0.0724, -0.0138, -0.0092,  ...,  0.0206, -0.0126,  0.0191],\n",
      "        [-0.0274, -0.0059, -0.0305,  ...,  0.0478,  0.0032, -0.0082],\n",
      "        [-0.0005,  0.0074,  0.0088,  ...,  0.0388,  0.0277,  0.0440],\n",
      "        ...,\n",
      "        [ 0.0769,  0.0182,  0.0120,  ..., -0.0663, -0.0296, -0.0169],\n",
      "        [ 0.0218,  0.0308, -0.0637,  ..., -0.1016,  0.0086, -0.0002],\n",
      "        [ 0.0064, -0.0432,  0.0123,  ..., -0.0216, -0.0081, -0.0060]])\n",
      "model.decoder.layers.3.encoder_attn.v_proj.bias Parameter containing:\n",
      "tensor([-0.0112, -0.0332,  0.0645,  0.1871, -0.1746, -0.0045, -0.0666,  0.0345,\n",
      "        -0.0782,  0.2756, -0.0157,  0.0230, -0.0136,  0.1007,  0.0353, -0.0886,\n",
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      "         2.4475e-01, -1.8298e-01, -5.1123e-01,  4.2383e-01, -5.1807e-01,\n",
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      "        -7.5000e-01, -7.2168e-01, -6.0254e-01, -1.0272e-01,  1.5527e-01,\n",
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      "         5.8887e-01, -3.6572e-01,  2.9785e-01,  2.1741e-01, -4.4604e-01,\n",
      "        -2.8247e-01,  2.4048e-01, -1.4734e-01,  1.6035e+00,  2.5781e-01,\n",
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      "        -5.4688e-02,  5.9131e-01, -8.8721e-01, -2.8394e-01, -3.1421e-01,\n",
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      "         3.0347e-01,  1.2915e-01,  7.3608e-02, -2.3279e-01,  3.1714e-01,\n",
      "        -9.0149e-02, -1.2659e-01, -8.7646e-01,  8.6731e-02, -2.8149e-01,\n",
      "         3.1641e-01,  1.2463e-01, -5.5237e-02,  3.0322e-01, -1.1951e-01,\n",
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      "        -2.1606e-01, -2.5464e-01,  2.1082e-01,  4.9878e-01, -1.4124e-01,\n",
      "         5.1562e-01,  3.9526e-01,  3.6304e-01, -5.8655e-02, -7.4561e-01,\n",
      "        -3.2227e-01, -3.1204e-02, -7.7588e-01, -3.6670e-01,  2.1802e-01,\n",
      "        -2.9932e-01,  9.5752e-01,  2.1936e-01,  3.9917e-02, -1.0516e-01,\n",
      "         6.7334e-01,  1.5833e-01, -9.0078e+00,  6.1279e-01, -5.0244e-01,\n",
      "        -3.3643e-01,  5.0873e-02,  4.7681e-01, -1.2866e-01, -3.2471e-01,\n",
      "        -3.4326e-01,  2.4475e-01, -4.7217e-01, -1.4917e-01])\n",
      "model.decoder.layers.3.encoder_attn_layer_norm.weight Parameter containing:\n",
      "tensor([1.8643, 5.9141, 1.3252, 1.3945, 2.0195, 1.3994, 1.1543, 3.9629, 1.6104,\n",
      "        1.4854, 3.0469, 1.9883, 1.7979, 2.3066, 3.8457, 1.6123, 2.2930, 2.2422,\n",
      "        2.0625, 1.4980, 1.7891, 3.5547, 1.4873, 4.1445, 2.1992, 1.6191, 1.6250,\n",
      "        2.2148, 1.6914, 3.3691, 1.3564, 2.0508, 1.9150, 8.0859, 1.5840, 1.4033,\n",
      "        2.2168, 3.9160, 2.5059, 5.6641, 1.8701, 1.3770, 1.4453, 3.9570, 1.7354,\n",
      "        1.5801, 4.5898, 1.6338, 1.8809, 3.5898, 1.9014, 1.0371, 2.1953, 2.9785,\n",
      "        2.7109, 2.1270, 1.9121, 2.4141, 2.2598, 2.9375, 2.3848, 5.4492, 1.4883,\n",
      "        1.7754, 1.2168, 2.1348, 2.1855, 1.6309, 1.4688, 2.3535, 1.6318, 1.9014,\n",
      "        3.1602, 1.3398, 1.9697, 2.1055, 1.6455, 1.5908, 1.2881, 1.7705, 1.4727,\n",
      "        2.1191, 3.2266, 2.0801, 2.0645, 2.8516, 1.8799, 2.8633, 1.5576, 2.8945,\n",
      "        1.2988, 2.5586, 2.3340, 5.3359, 1.6182, 1.5010, 1.6289, 1.4443, 4.0352,\n",
      "        1.4346, 1.9424, 1.7412, 1.6006, 1.8916, 1.7021, 2.2090, 1.6035, 1.8301,\n",
      "        1.8691, 2.4395, 4.4297, 1.4248, 1.7891, 3.5801, 1.9590, 1.3555, 1.6523,\n",
      "        3.0605, 1.1699, 2.2812, 1.2002, 2.7773, 1.6787, 3.2598, 1.7129, 1.8457,\n",
      "        2.5820, 1.5127, 1.4551, 1.6367, 2.1289, 1.5654, 2.4551, 1.4707, 1.8281,\n",
      "        2.0352, 1.3857, 2.9141, 1.4902, 1.3379, 1.2207, 1.7363, 1.9004, 1.3486,\n",
      "        1.9004, 3.2051, 1.6064, 1.4043, 1.4697, 1.6299, 2.8457, 1.5127, 2.2070,\n",
      "        3.0957, 1.6270, 1.8643, 1.6738, 3.6953, 1.3779, 3.3438, 1.6904, 1.6475,\n",
      "        2.3633, 2.1016, 1.6123, 1.5039, 4.2031, 2.7695, 2.3555, 1.7148, 2.4199,\n",
      "        2.8184, 2.1699, 1.9990, 6.3945, 2.6777, 1.5332, 1.7822, 4.0938, 1.8086,\n",
      "        1.3604, 1.8320, 1.2715, 1.3369, 1.7783, 1.2637, 1.2783, 1.5166, 2.3574,\n",
      "        1.7422, 1.6670, 1.3965, 2.6895, 1.7109, 1.7510, 1.4424, 1.7490, 1.8037,\n",
      "        2.1934, 1.8330, 2.7734, 2.7988, 2.0332, 2.1113, 2.8535, 1.5283, 4.9688,\n",
      "        3.1230, 2.9336, 1.8301, 2.9531, 2.3281, 3.7754, 1.4111, 1.5762, 2.1816,\n",
      "        1.5547, 2.0742, 3.7031, 1.1455, 1.4561, 1.4111, 1.4307, 1.9922, 1.6709,\n",
      "        1.5098, 2.0566, 1.8750, 1.7451, 6.5469, 1.8945, 0.9512, 2.4297, 2.2969,\n",
      "        2.7637, 1.3330, 7.6094, 2.0859, 3.3027, 1.4199, 1.9775, 1.5645, 2.3027,\n",
      "        1.5605, 1.4443, 2.1113, 1.8213, 2.3477, 1.5430, 1.1406, 2.7734, 1.6650,\n",
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      "        1.4199, 1.4395, 1.3740, 1.5059, 2.5176, 1.8662, 2.1309, 1.7920, 2.6250,\n",
      "        1.8740, 2.3418, 1.9336, 2.7988, 1.7783, 2.5352, 3.6289, 2.0059, 1.5176,\n",
      "        1.7031, 2.3691, 1.4912, 1.6865, 1.9365, 1.4238, 2.6211, 1.5088, 1.8184,\n",
      "        1.4844, 2.3789, 1.4043, 1.2451, 1.7578, 1.7109, 1.6211, 5.5156, 1.5596,\n",
      "        1.5342, 1.5205, 3.5137, 1.6289, 1.1445, 1.3359, 2.4785, 2.2461, 1.4873,\n",
      "        1.5137, 2.2754, 1.7285, 2.5352, 1.3252, 2.1699, 5.5195, 1.5664, 1.6094,\n",
      "        1.6562, 1.8145, 1.5947, 2.7227, 1.3838, 1.5654, 1.3525, 2.3535, 1.6143,\n",
      "        3.2090, 1.9844, 2.5469, 2.2578, 1.6904, 1.3262, 2.1504, 1.9541, 2.0449,\n",
      "        1.5547, 2.9297, 1.9746, 2.6348, 1.4746, 1.3057, 3.2031, 1.4111, 1.4922,\n",
      "        1.6201, 1.3242, 2.4688, 2.4375, 2.3965, 2.4707, 1.8945, 1.5986, 1.4385,\n",
      "        1.8359, 1.3262, 2.5664, 1.5322, 2.4434, 5.5117, 2.1406, 2.4023, 2.6562,\n",
      "        1.2812, 2.2324, 2.3398, 1.4219, 4.5273, 1.4238, 1.3760, 1.4385, 1.7129,\n",
      "        2.1074, 1.4629, 2.1191, 0.7500, 1.9395, 1.8125, 1.9414, 2.2539, 1.3926,\n",
      "        2.5137, 2.1914, 2.0859, 5.1680, 3.2832, 1.9053])\n",
      "model.decoder.layers.3.encoder_attn_layer_norm.bias Parameter containing:\n",
      "tensor([-0.1893, -0.2395, -0.4268, -0.4473, -0.2019, -0.1272, -1.5879, -0.8853,\n",
      "        -0.2617,  0.2783, -0.4751,  0.1984, -0.8374, -0.3289,  0.6021,  0.2805,\n",
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      "        -0.5811, -1.4346,  0.3796,  0.0661,  0.2656,  0.5444,  0.8594,  0.3752,\n",
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      "        -0.5283,  0.5669,  0.9111,  1.6465, -0.5435, -0.2441,  0.6562,  0.2942,\n",
      "         0.2269,  0.9829,  0.4734,  1.0635,  0.1305, -0.7622, -0.1183, -0.5889,\n",
      "        -0.0721, -0.1683,  0.8389,  0.8462, -0.0611,  0.5850, -0.0275, -4.9609,\n",
      "         0.1349,  1.2891,  0.3103, -0.0981,  0.6050,  1.1895, -0.3184,  0.7163,\n",
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      "        -0.1595, -1.0986,  0.7686,  0.3401, -0.3115,  0.6367, -0.3093,  0.5205,\n",
      "         0.0181,  0.5386,  0.5884, -0.3132, -0.5669, -0.4167, -1.3066,  0.2094,\n",
      "        -1.4482,  1.6201, -0.8179, -0.6519, -0.0551, -0.1511, -0.2072,  0.1011,\n",
      "         0.2920, -0.4434, -1.3984, -0.2040, -0.9146, -0.2634,  0.4807, -0.0310,\n",
      "         0.4253, -1.0732,  0.3855, -0.2416, -0.1156,  0.3594,  0.8550,  1.9102,\n",
      "        -0.9048, -0.3862,  2.6680,  0.0906, -0.1936, -0.9639,  0.0487, -1.2969,\n",
      "        -0.2290,  0.1086, -0.1119, -0.9141,  7.8672, -0.6509,  0.2986,  0.2051,\n",
      "         0.0201, -0.9272,  0.9893, -0.3069, -0.1455, -1.4766, -1.1113,  0.1801])\n",
      "model.decoder.layers.3.fc1.weight Parameter containing:\n",
      "tensor([[-0.0272, -0.0249, -0.0008,  ...,  0.0240, -0.0270,  0.0037],\n",
      "        [-0.0169, -0.0443, -0.0077,  ..., -0.0256, -0.0243,  0.0004],\n",
      "        [-0.0148, -0.0334,  0.0060,  ...,  0.0130, -0.0184,  0.0315],\n",
      "        ...,\n",
      "        [-0.0166,  0.0202,  0.0299,  ..., -0.0102, -0.0219, -0.0153],\n",
      "        [-0.0297,  0.0070, -0.0225,  ..., -0.0076, -0.0147, -0.0285],\n",
      "        [ 0.0140, -0.0027,  0.0131,  ...,  0.0040, -0.0187, -0.0145]],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.3.fc1.bias Parameter containing:\n",
      "tensor([-0.9663, -1.8389, -2.8945,  ..., -0.1147, -0.6699, -0.9419],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.3.fc2.weight Parameter containing:\n",
      "tensor([[ 0.0314,  0.0132, -0.0023,  ...,  0.0065,  0.0029,  0.0289],\n",
      "        [ 0.0285, -0.0211, -0.0044,  ..., -0.0188,  0.0149, -0.0262],\n",
      "        [-0.0033, -0.0500, -0.0075,  ..., -0.0396, -0.0207, -0.0004],\n",
      "        ...,\n",
      "        [ 0.0254, -0.0038,  0.0358,  ...,  0.0278,  0.0158, -0.0030],\n",
      "        [-0.0188, -0.0626,  0.1387,  ..., -0.0345,  0.0597, -0.0497],\n",
      "        [ 0.0195,  0.0179, -0.0258,  ...,  0.0097, -0.0083,  0.0624]],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.3.fc2.bias Parameter containing:\n",
      "tensor([ 6.7432e-01, -1.2988e-01, -4.7290e-01, -2.0850e-01,  9.9243e-02,\n",
      "        -5.6982e-01, -9.2871e-01, -1.1572e+00,  2.5391e-01, -6.3379e-01,\n",
      "         2.9736e-01,  4.5728e-01, -8.6328e-01, -7.0898e-01,  1.1969e-01,\n",
      "         4.2017e-01, -6.4331e-02, -7.2693e-02,  1.4758e-01,  1.3954e-02,\n",
      "        -1.1963e-01,  2.6440e-01, -2.6611e-01,  3.8379e-01,  2.4414e-02,\n",
      "         4.5459e-01,  4.6875e-01,  3.2520e-01, -1.2891e+00,  2.1704e-01,\n",
      "        -9.9121e-02, -4.6802e-01,  4.6118e-01,  2.2571e-01, -1.5149e-01,\n",
      "         2.2607e-01,  7.4609e-01,  5.6445e-01,  4.5166e-01,  5.0684e-01,\n",
      "        -8.5297e-03, -1.3105e+00,  3.6450e-01, -6.6772e-02,  3.7915e-01,\n",
      "         1.0293e+00,  1.1377e+00, -1.0291e-01, -3.1543e-01, -4.6802e-01,\n",
      "        -3.1982e-01,  2.3743e-01, -1.5671e-02,  3.3154e-01,  1.0576e+00,\n",
      "         4.4556e-01, -1.0244e+00,  4.1504e-01,  9.8438e-01, -3.6060e-01,\n",
      "         3.3855e-03, -1.7249e-01,  3.5791e-01, -6.4893e-01,  7.1924e-01,\n",
      "         3.5571e-01,  7.3828e-01,  1.0742e+00, -9.5361e-01,  3.4131e-01,\n",
      "        -3.7378e-01, -8.3398e-01, -5.7526e-02,  1.7549e+00, -4.7705e-01,\n",
      "        -7.5684e-01,  2.3242e-01,  2.5073e-01,  7.1094e-01, -4.2676e-01,\n",
      "        -1.1484e+00,  1.1169e-01, -5.2063e-02,  8.1445e-01, -4.5947e-01,\n",
      "         5.8289e-02,  6.0400e-01,  8.7061e-01, -8.2324e-01,  6.2500e-01,\n",
      "        -6.7871e-01,  4.2871e-01,  1.0420e+00,  2.8882e-01,  6.5869e-01,\n",
      "         5.4443e-01,  2.6978e-01,  2.4673e-02,  3.8818e-01, -9.4873e-01,\n",
      "        -6.7676e-01, -8.3594e-01, -9.7290e-02, -5.1123e-01,  1.0474e-01,\n",
      "        -2.3352e-01, -2.7100e-01, -5.9033e-01,  6.1066e-02, -1.5400e+00,\n",
      "         6.1377e-01,  4.7852e-02,  2.2510e-01,  1.1707e-01,  8.5596e-01,\n",
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      "        -7.6221e-01, -1.4990e-01, -1.3623e+00,  1.8359e-01,  2.2961e-01,\n",
      "        -5.3516e-01,  4.0381e-01,  7.8491e-02,  2.8101e-01,  1.1511e-01,\n",
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      "        -8.4961e-02, -7.1899e-02,  3.8330e-01, -7.9639e-01, -5.3223e-01,\n",
      "         2.4036e-01,  4.6777e-01,  1.3657e-02, -1.9863e+00,  3.9185e-01,\n",
      "         3.5596e-01,  2.4500e-01,  1.6785e-02, -1.4612e-01, -4.1577e-01,\n",
      "         1.6436e+00, -4.2920e-01,  1.7944e-01, -8.3496e-01, -2.0874e-01,\n",
      "         3.0786e-01, -1.1426e+00, -3.2837e-01, -4.5044e-01, -6.5479e-01,\n",
      "         6.5430e-01,  3.9575e-01,  1.9055e-01,  8.2764e-01, -1.0957e+00,\n",
      "        -1.0186e+00,  5.1611e-01, -2.2644e-01, -9.7949e-01, -2.2314e-01,\n",
      "        -6.1377e-01,  2.6758e+00,  1.7517e-01, -7.0410e-01,  9.6240e-01,\n",
      "         3.6499e-01, -8.3496e-01,  5.2539e-01, -1.1871e-01,  3.7939e-01,\n",
      "        -5.0928e-01,  2.0288e-01, -1.0803e-01, -9.0527e-01,  1.2607e+00,\n",
      "        -1.5723e-01, -1.0332e+00,  4.5801e-01, -2.9007e-02, -7.1924e-01,\n",
      "         1.0205e+00, -6.3721e-02, -3.3032e-01, -8.3923e-02,  1.6650e-01,\n",
      "        -6.1371e-02,  1.9299e-01,  2.8381e-02,  2.9224e-01,  5.8014e-02,\n",
      "        -5.8887e-01, -1.5271e-01, -2.7686e-01, -1.3438e+00,  5.9814e-01,\n",
      "         2.8198e-01,  3.1494e-01,  1.0693e+00, -5.8252e-01, -8.4424e-01,\n",
      "        -7.6965e-02,  9.0625e-01, -2.4634e-01, -5.8203e-01, -1.0684e+00,\n",
      "         1.0615e+00,  8.8623e-01,  4.1992e-01,  3.4595e-01, -1.0449e+00,\n",
      "        -5.3662e-01,  5.8350e-01,  2.1899e-01, -1.8335e-01, -3.7524e-01,\n",
      "        -1.7065e-01, -3.9233e-01,  9.3460e-03,  3.7305e-01, -3.1299e-01,\n",
      "         1.1738e+00, -1.7188e-01,  2.9956e-01, -8.3069e-02, -4.3237e-01,\n",
      "         8.7451e-01, -7.8796e-02, -1.3652e+00,  2.4622e-01,  3.3228e-01,\n",
      "        -1.6426e+00, -8.0078e-01, -1.4514e-01, -8.8916e-01, -4.4116e-01,\n",
      "         6.3232e-01,  1.6270e+00, -2.5681e-02, -1.3843e-01,  8.0566e-01,\n",
      "        -8.9697e-01,  4.6692e-02, -8.3789e-01,  3.6597e-01,  6.8321e-03,\n",
      "        -5.6836e-01, -4.2212e-01,  5.7959e-01,  9.5557e-01,  5.1758e-01,\n",
      "        -6.4307e-01, -4.5312e-01, -7.3389e-01,  1.0327e-01, -4.2310e-01,\n",
      "        -1.5244e+00,  9.7839e-02,  6.3818e-01,  8.6914e-02, -2.0605e-01,\n",
      "         7.0654e-01, -5.0415e-02, -6.5039e-01, -7.5745e-02, -1.6296e-01,\n",
      "         2.5171e-01,  5.8643e-01, -7.8369e-02,  8.1982e-01, -5.8136e-02,\n",
      "        -7.6599e-02,  4.8889e-02,  9.4141e-01,  2.9126e-01, -1.0391e+00,\n",
      "         2.1326e-01, -4.1534e-02,  1.5723e-01,  3.9746e-01,  4.9707e-01,\n",
      "         1.8176e-01,  8.4033e-01,  3.5400e-02, -4.7314e-01,  3.1787e-01,\n",
      "        -8.6475e-01,  5.2100e-01, -2.6978e-01,  2.6733e-01,  7.8125e-01,\n",
      "         4.7559e-01,  1.6516e-01,  2.1484e-01, -3.4668e+00, -1.4856e-01,\n",
      "         1.3047e+00, -5.8252e-01,  4.3970e-01,  1.7041e-01,  7.3975e-01,\n",
      "         2.0859e-02,  1.2021e+00,  5.7959e-01,  1.3818e+00,  4.7144e-01,\n",
      "         2.0447e-01,  2.3743e-01, -7.1777e-01, -5.6348e-01,  1.4990e-01,\n",
      "        -7.0435e-02, -1.2100e+00,  1.2168e+00,  2.4841e-01,  3.3740e-01,\n",
      "        -7.2449e-02, -4.0015e-01,  3.7207e-01,  5.2002e-01, -3.5767e-01,\n",
      "        -1.0857e-02, -2.2412e-01, -3.4448e-01, -1.0553e-01, -9.0479e-01,\n",
      "         2.6099e-01, -4.1016e-01,  1.8154e+00, -3.5400e-01,  3.2861e-01,\n",
      "        -3.4741e-01, -3.6041e-02, -6.6490e-03, -3.0493e-01,  2.5781e-01,\n",
      "        -2.2385e-02, -4.9536e-01, -8.7354e-01, -1.1777e+00,  2.8027e-01,\n",
      "         5.5127e-01,  2.2595e-01,  8.9783e-02, -6.6602e-01,  5.3174e-01,\n",
      "        -5.9180e-01, -6.3330e-01,  8.8928e-02,  3.0469e-01,  1.4951e+00,\n",
      "        -4.2920e-01,  1.6882e-01,  1.7256e+00,  6.7041e-01, -5.0049e-01,\n",
      "        -1.5027e-01, -8.0127e-01, -9.8877e-01, -5.6396e-01,  2.0569e-01,\n",
      "         1.8640e-01, -5.8545e-01,  1.7178e+00, -8.5059e-01,  6.4746e-01,\n",
      "         4.3774e-01, -2.0691e-01, -1.1416e+00,  6.3037e-01,  6.5430e-02,\n",
      "         3.5840e-01, -5.4395e-01, -8.6230e-01,  3.2642e-01],\n",
      "       requires_grad=True)\n",
      "model.decoder.layers.3.final_layer_norm.weight Parameter containing:\n",
      "tensor([2.8867, 2.4844, 2.1230, 2.5469, 2.9199, 2.5781, 2.1309, 1.8496, 2.6699,\n",
      "        2.2793, 2.4180, 2.3047, 2.2031, 2.4961, 2.2305, 2.2383, 2.5449, 2.8301,\n",
      "        6.0938, 2.5273, 2.5898, 2.6465, 2.5898, 2.0312, 2.4258, 2.5957, 2.2676,\n",
      "        2.5684, 2.3066, 2.3555, 2.1797, 4.7031, 3.4434, 3.0039, 2.8418, 2.3398,\n",
      "        2.6250, 1.9883, 2.6797, 2.3672, 2.4746, 2.1973, 2.3516, 2.2539, 2.3281,\n",
      "        2.1758, 1.5732, 2.5566, 2.5820, 2.7969, 2.3320, 4.8945, 2.4199, 2.2285,\n",
      "        2.3555, 2.4648, 2.3691, 2.3086, 2.0996, 2.0840, 4.2969, 2.2754, 3.0645,\n",
      "        2.3555, 1.8760, 2.1562, 2.2598, 2.0918, 2.1582, 2.0918, 2.8066, 2.4824,\n",
      "        2.6289, 2.7070, 2.4375, 2.2773, 2.6230, 2.3906, 2.5586, 2.3965, 2.0625,\n",
      "        2.4570, 2.4961, 2.1738, 2.5684, 2.3984, 2.8535, 2.5625, 2.2773, 2.5020,\n",
      "        2.2676, 2.2461, 4.2852, 2.3887, 2.6582, 2.2598, 2.3516, 2.4590, 1.9209,\n",
      "        2.1387, 3.7695, 2.5879, 4.6602, 2.3672, 3.0918, 5.5117, 2.1406, 2.1543,\n",
      "        2.8848, 2.1016, 2.2578, 2.3574, 2.4863, 2.5469, 2.1172, 2.6172, 2.2246,\n",
      "        2.2324, 2.6777, 2.4785, 2.2344, 2.1953, 2.0215, 2.0332, 2.2754, 2.4199,\n",
      "        5.1328, 2.6270, 2.1543, 2.5645, 2.3242, 2.6777, 2.5605, 3.4297, 2.2363,\n",
      "        2.3809, 2.4531, 2.4473, 2.4355, 2.9590, 2.4531, 2.3086, 2.5410, 2.0449,\n",
      "        2.4570, 2.4551, 2.4707, 2.3145, 2.4590, 2.4199, 1.9209, 2.2539, 2.6484,\n",
      "        4.9258, 2.5859, 2.5117, 2.2207, 2.3027, 2.4883, 2.2363, 2.4336, 3.3848,\n",
      "        2.7109, 5.0312, 2.1094, 2.1660, 2.2109, 2.3613, 2.1035, 2.5605, 2.9434,\n",
      "        1.8428, 2.5410, 2.3457, 1.6719, 2.6387, 2.3672, 2.2832, 2.0527, 2.4141,\n",
      "        2.2617, 2.4961, 2.5801, 2.5254, 3.7812, 2.4941, 2.4707, 2.3809, 2.2852,\n",
      "        2.3770, 1.9111, 2.4160, 2.0410, 2.5664, 2.6504, 2.9297, 2.6289, 2.3965,\n",
      "        2.2832, 2.5195, 2.3594, 2.5938, 2.4355, 2.1777, 2.4258, 3.0586, 2.1133,\n",
      "        2.1543, 3.1074, 2.3477, 2.6055, 2.6211, 2.4961, 2.3945, 2.1953, 2.2070,\n",
      "        2.4121, 2.2695, 2.6738, 2.0566, 2.0547, 2.4395, 2.6562, 2.3496, 2.2480,\n",
      "        6.4492, 3.7578, 2.4688, 2.3203, 2.8145, 1.9521, 2.3633, 2.3809, 2.0879,\n",
      "        2.2969, 2.1289, 1.8135, 2.1719, 2.4277, 2.5566, 5.2734, 2.1582, 2.3320,\n",
      "        2.2188, 2.3418, 2.2422, 1.9824, 3.9414, 2.3340, 2.7910, 2.2617, 2.4551,\n",
      "        3.0996, 2.4492, 2.5352, 3.9961, 2.2109, 2.7578, 2.2148, 2.2188, 2.3477,\n",
      "        2.2891, 2.7578, 2.5957, 2.8438, 4.7305, 2.6172, 2.8105, 2.2715, 2.4961,\n",
      "        2.2695, 2.2637, 2.7266, 2.4434, 2.5977, 4.9336, 2.2363, 5.2578, 2.4375,\n",
      "        2.7969, 2.3027, 2.4648, 2.7754, 3.6152, 2.0508, 3.0098, 2.3594, 2.4238,\n",
      "        2.4219, 2.2129, 2.3809, 2.2852, 3.4531, 2.4004, 2.3535, 2.2383, 2.5918,\n",
      "        2.2637, 2.6035, 2.5586, 5.0859, 2.5332, 2.4570, 1.9219, 2.3945, 2.3613,\n",
      "        2.2539, 3.3301, 2.7168, 2.4531, 2.7617, 1.9697, 1.9102, 2.4707, 2.1035,\n",
      "        2.6699, 2.7324, 2.8086, 2.4473, 2.6602, 2.5371, 2.1680, 1.9092, 3.1602,\n",
      "        2.0273, 2.5684, 2.4219, 2.4199, 2.4609, 2.1719, 2.4004, 2.5859, 2.5781,\n",
      "        2.5039, 1.9834, 4.4648, 3.5449, 1.9785, 2.6816, 2.2910, 2.5918, 2.3535,\n",
      "        2.5156, 2.7617, 2.3301, 2.4160, 2.7480, 2.0254, 2.6113, 2.9023, 2.3262,\n",
      "        2.6738, 2.6465, 2.0859, 2.4062, 2.1699, 2.1406, 2.3477, 2.3203, 1.9307,\n",
      "        2.6387, 2.4297, 4.0039, 2.2695, 2.3125, 2.4961, 1.9414, 2.0098, 2.5117,\n",
      "        2.7656, 2.6973, 2.1445, 0.9624, 2.2578, 2.3438, 2.7168, 2.6484, 2.5488,\n",
      "        2.5020, 2.5117, 4.6523, 2.2891, 6.3125, 3.6309], requires_grad=True)\n",
      "model.decoder.layers.3.final_layer_norm.bias Parameter containing:\n",
      "tensor([ 8.6719e-01, -5.3906e-01, -1.0166e+00, -7.1729e-01, -1.8628e-01,\n",
      "        -8.0908e-01, -2.6270e+00, -1.4219e+00,  4.0112e-01, -9.5020e-01,\n",
      "         1.4427e-02,  6.1816e-01, -1.4854e+00, -1.4297e+00,  3.5376e-01,\n",
      "         5.1855e-01,  3.1250e-02, -2.9077e-01,  9.7733e-03,  1.2274e-01,\n",
      "        -4.0894e-01,  3.5205e-01, -6.6211e-01,  7.4561e-01, -8.1253e-03,\n",
      "         6.3037e-01,  4.4727e-01,  3.8379e-01, -2.3164e+00,  1.0736e-01,\n",
      "        -3.6987e-01,  4.6051e-02,  1.5342e+00,  3.5303e-01, -4.2432e-01,\n",
      "         4.3335e-01,  1.3232e+00,  8.2812e-01,  5.8252e-01,  6.3965e-01,\n",
      "        -2.1765e-01, -2.2871e+00,  2.2803e-01, -1.6992e-01,  4.4263e-01,\n",
      "         1.9482e+00,  1.1963e+00,  3.5858e-02, -8.0762e-01, -6.6357e-01,\n",
      "        -3.5303e-01, -1.3074e-01, -4.5532e-01,  7.2571e-02,  1.7520e+00,\n",
      "         3.1714e-01, -1.3906e+00,  5.0977e-01,  1.1875e+00, -1.1191e+00,\n",
      "         1.4121e+00, -1.9019e-01, -3.0957e-01, -1.0088e+00,  1.1191e+00,\n",
      "         5.2539e-01,  9.4287e-01,  1.2891e+00, -1.2344e+00,  4.7266e-01,\n",
      "        -1.1016e+00, -1.4951e+00,  1.0779e-01,  2.7500e+00, -1.0088e+00,\n",
      "        -1.3691e+00,  1.5161e-01,  6.1676e-02,  1.1494e+00, -1.2402e+00,\n",
      "        -1.9033e+00, -2.4683e-01, -1.3416e-01,  1.2383e+00, -6.6260e-01,\n",
      "         5.9601e-02,  1.0967e+00,  1.0498e+00, -1.4580e+00,  9.4336e-01,\n",
      "        -1.7334e+00,  1.0498e+00,  1.9736e+00,  5.2539e-01,  1.4482e+00,\n",
      "         6.9873e-01,  7.2461e-01, -4.5410e-01, -8.6365e-02, -1.6143e+00,\n",
      "        -8.1348e-01, -1.3457e+00, -1.2783e+00, -8.6230e-01,  1.2421e-01,\n",
      "         1.2610e-01, -1.1699e+00, -1.1270e+00,  9.0039e-01, -2.0801e+00,\n",
      "         8.9746e-01,  3.7378e-01,  7.9712e-02,  2.0959e-01,  1.1943e+00,\n",
      "        -9.2041e-01, -5.9174e-02, -4.6729e-01,  4.2480e-01, -2.7515e-01,\n",
      "        -1.1738e+00, -3.8574e-01, -1.7363e+00,  1.0913e-01,  2.9468e-01,\n",
      "        -9.9707e-01,  4.7836e-03, -1.0492e-01,  8.9648e-01,  3.7695e-01,\n",
      "         8.2275e-01, -5.1025e-01,  1.1836e+00, -2.3613e+00, -8.3984e-01,\n",
      "        -9.0454e-02, -4.0210e-01,  4.4897e-01, -1.5547e+00, -1.5352e+00,\n",
      "         1.2520e+00,  5.4395e-01,  2.7893e-02, -2.5117e+00,  4.0796e-01,\n",
      "         7.7197e-01,  7.0508e-01,  1.0779e-01, -7.6721e-02, -6.0889e-01,\n",
      "         1.8643e+00, -6.7334e-01,  6.1572e-01, -2.4277e+00, -5.1172e-01,\n",
      "         8.2080e-01, -2.0078e+00, -8.7744e-01, -9.2041e-01, -1.4346e+00,\n",
      "         6.1328e-01,  7.3779e-01,  6.1816e-01,  1.0537e+00, -1.8604e+00,\n",
      "        -1.5088e+00,  1.6418e-01, -9.1699e-01, -1.2021e+00, -1.1357e+00,\n",
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      "        -4.7363e-01,  5.3711e-01, -3.7549e-01, -1.2725e+00,  1.5928e+00,\n",
      "        -1.3660e-01, -2.3945e+00,  7.5391e-01, -3.8354e-01, -1.0420e+00,\n",
      "         2.4355e+00,  2.1484e-01, -7.4854e-01, -3.7903e-02, -1.5541e-02,\n",
      "         3.1079e-01, -3.0420e-01,  3.3150e-03,  2.0630e-01,  1.6077e-01,\n",
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      "         8.8379e-01,  4.6362e-01,  1.3643e+00, -6.8408e-01, -1.0225e+00,\n",
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      "         1.4482e+00,  2.1836e+00,  9.5166e-01, -9.6729e-01,  1.6543e+00,\n",
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      "        -1.4580e+00, -8.8379e-01,  6.9922e-01,  1.8486e+00,  8.8574e-01,\n",
      "        -1.0947e+00, -7.2656e-01, -1.1553e+00, -7.1350e-02, -9.4043e-01,\n",
      "        -3.9434e+00,  2.3010e-01,  1.0723e+00, -1.4679e-02, -1.3867e-01,\n",
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      "        -3.6084e-01,  1.1108e-01,  1.0654e+00,  3.6875e+00, -1.4307e+00,\n",
      "         7.6965e-02,  1.7517e-01,  4.6570e-02,  8.7891e-01,  6.5723e-01,\n",
      "         1.8408e-01,  1.3076e+00,  2.3730e-01, -9.2188e-01,  1.7419e-01,\n",
      "        -1.2051e+00,  4.4775e-01, -4.3677e-01,  4.8120e-01,  8.6621e-01,\n",
      "         9.1943e-01,  4.9976e-01,  1.6943e-01, -4.8750e+00, -4.3750e-01,\n",
      "         1.8115e+00, -8.7012e-01, -1.2344e-02,  6.0303e-01,  1.0488e+00,\n",
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      "        -2.5909e-02, -9.7961e-02, -1.8203e+00, -6.2158e-01,  7.0996e-01,\n",
      "        -7.5989e-02, -2.3457e+00,  1.9365e+00,  7.8760e-01,  2.6172e-01,\n",
      "        -8.1482e-02, -6.5088e-01,  4.8242e-01,  8.5498e-01, -8.7500e-01,\n",
      "        -1.5515e-01, -1.5051e-01, -6.4307e-01,  3.6102e-02, -1.1836e+00,\n",
      "         8.5010e-01, -1.2725e+00,  2.6973e+00, -1.2354e+00, -9.9426e-02,\n",
      "        -8.5547e-01, -7.0215e-01, -5.6641e-02, -4.5361e-01,  3.1567e-01,\n",
      "         1.1462e-01, -1.1172e+00, -1.2451e+00, -2.0098e+00, -5.7678e-02,\n",
      "         8.5986e-01,  6.5723e-01,  2.3975e-01, -1.0645e+00,  5.9717e-01,\n",
      "        -9.0430e-01, -8.7451e-01, -4.4629e-01,  8.0762e-01,  2.0508e+00,\n",
      "        -4.0894e-01, -2.3682e-01,  6.2734e+00,  6.6211e-01, -5.2734e-01,\n",
      "        -6.7871e-01, -1.3330e+00, -1.4639e+00, -3.8184e-01,  4.8071e-01,\n",
      "        -1.0996e+00, -9.3018e-01,  1.0500e+01, -1.5127e+00,  1.0596e+00,\n",
      "         6.9531e-01, -1.6357e-02, -2.0449e+00,  1.2070e+00,  2.9858e-01,\n",
      "         1.9360e-01, -8.7549e-01, -1.5225e+00,  5.1221e-01],\n",
      "       requires_grad=True)\n",
      "model.decoder.layer_norm.weight Parameter containing:\n",
      "tensor([ 11.7109,  10.3359,   7.9414,   9.2734,  10.3516,  10.0703,   4.8594,\n",
      "          9.8203,  10.1562,  11.3672,  16.4844,  10.9453,  10.7500,  10.1562,\n",
      "         10.4141,   9.6719,  12.0391,  12.3984,  22.8438,  10.3594,  10.9141,\n",
      "         10.6172,  11.3984,  10.5312,  10.8125,  10.4922,  10.1797,  11.7500,\n",
      "         11.5781,  11.2422,   7.9805,  14.8672,   9.9922,  10.5938,  12.0234,\n",
      "         10.2656,  12.7734,  10.0234,  11.8203,  10.8203,  10.9688,   9.6953,\n",
      "          9.9297,  10.7344,  10.5547,  11.2578,  10.1016,  10.3672,  10.5547,\n",
      "         10.5938,  14.8203,  16.2031,  10.2500,  15.8594,  11.9531,  10.1484,\n",
      "         10.9375,  11.1719,  10.5078,   9.6250,  16.2812,  10.8906,   6.5625,\n",
      "         10.0703,   7.7930,  11.0781,  10.8438,   9.3906,  10.5234,  11.2344,\n",
      "         10.0000,  11.0938,  11.2500,  10.1328,  10.6719,  12.7344,  10.8125,\n",
      "         10.0547,  10.6172,   9.9844,  10.7656,  10.4219,  10.1016,  10.1250,\n",
      "         11.9531,  11.3750,  10.9062,  14.4375,   9.0156,  11.0000,   7.8633,\n",
      "         12.7578,  20.2188,  10.9609,  10.9141,  11.4531,   9.9688,  10.5156,\n",
      "         11.5703,  11.3984,  17.3750,  10.2188,  15.4062,  11.5781,  11.6641,\n",
      "         19.6562,   9.7188,   9.7188,  10.6250,  10.3125,  10.6641,   8.8047,\n",
      "          9.9688,  10.9297,  12.7578,  10.2344,   9.3984,  10.8516,   9.9766,\n",
      "         10.3438,   9.6719,  14.9531,  10.5156,  12.1250,   9.7734,  10.8594,\n",
      "         18.3281,  10.8047,   7.3633,  10.5703,  10.7422,  10.4062,  11.0469,\n",
      "         14.1328,  11.0703,  11.1719,   9.3594,  12.3047,  10.5703,  11.8047,\n",
      "          9.8047,  11.0703,   9.1719,  10.7500,   9.5234,  10.6953,  10.6484,\n",
      "          8.8594,   9.0000,   9.3203,  11.9688,   8.5078,  10.9766,  10.2109,\n",
      "          8.6719,  11.2812,  10.2344,  10.3516,  10.6250,  10.4922,   9.4922,\n",
      "         12.9141,  11.5547,  23.0625,   9.1250,   9.7031,  10.3750,  10.6250,\n",
      "         10.6797,   9.9375,  11.1094,  10.4297,   9.8203,  10.8984,   9.9766,\n",
      "         10.1797,  10.3281,  11.0156,  10.8672,   9.2891,  10.5000,   9.8516,\n",
      "          9.7266,  10.6953,  14.3594,  10.4062,  12.4062,   9.9219,  11.5781,\n",
      "         10.5625,  12.0703,  10.2188,  13.8281,   9.5703,  11.3594,  10.6406,\n",
      "         10.0156,  10.1953,  11.7422,  11.0000,  11.0391,  10.9609,  11.8047,\n",
      "         10.5781,  11.0000,  10.5000,  10.9688,  10.9922,  10.7188,  11.5156,\n",
      "          9.9297,  11.3594,  11.7109,  10.5547,   8.9141,  10.6641,   9.7422,\n",
      "         10.3359,  10.6953,   9.5938,   9.1016,   9.8281,  10.7344,  12.1797,\n",
      "         12.1953,  13.3672,  12.8984,  11.2188,  11.3750,  10.5781,  10.5156,\n",
      "          9.4766,  10.3516,  11.4219,  11.5234,   9.1953,   9.7578,  12.6797,\n",
      "         11.1719,  12.0391,  12.1641,   9.5000,  12.8047,   9.8906,  10.8828,\n",
      "         11.9062,  11.0078,  16.8906,   9.6953,  11.1016,  10.0781,  10.4609,\n",
      "         12.9297,  10.8594,  10.5859,  17.1250,  11.9375,  11.5312,  11.4453,\n",
      "         10.9062,   8.6953,   8.7266,  11.2031,  10.3594,  10.8438,  10.8984,\n",
      "         10.4141,  12.5312,  10.1797,  11.1875,  11.4453,  11.6641,  10.7578,\n",
      "         13.6406,  11.0234,  13.9375,  11.8750,  11.4375,  10.8203,  11.5391,\n",
      "         10.9375,   8.8438,   9.8672,  11.2266,   8.8750,  11.1016,   9.3906,\n",
      "         10.8359,   8.9141,  11.5000,  10.7578,  10.3438,  17.9219,  10.4531,\n",
      "          9.2422,  10.0859,  10.5547,  11.2422,   9.6484,  14.5938,  25.8594,\n",
      "         10.5938,  10.1641, -10.2656,  10.9609,  10.8359,   9.3438,  11.2500,\n",
      "         11.0156,  11.3984,  10.2344,  12.8125,   9.5859,  10.1797,   8.9219,\n",
      "         10.9688,  11.2578,  10.1562,  10.4062,  10.6172,  10.5547,  10.6250,\n",
      "         11.2422,  10.0234,  12.5938,  11.0547,   9.3906,  11.8672,  12.8516,\n",
      "         10.2734,  12.6875,   9.9062,  11.6094,  10.6250,  11.6797,  13.1875,\n",
      "         12.1094,  11.9453,  10.0938,  11.8750,   9.7656,   9.9609,   9.3516,\n",
      "         11.6484,  10.9531,  10.0469,  10.5781,  10.1797,  10.4766,  11.4219,\n",
      "         11.1094,  10.7969,  10.1328,  10.5625,   8.2578,  14.0391,  10.0078,\n",
      "         11.6641,  11.2891,  10.4766,  10.6875,  10.9609,  11.9922,  10.8359,\n",
      "         11.5391,  10.6484,   9.9844,  11.2578,  11.2656,  10.7500,   6.5898,\n",
      "         10.6172,  -0.3364,  10.6562,  11.1406,  11.4219,  10.4453,  11.2188,\n",
      "         10.4375,  11.5938,  21.5781,  11.0078,  47.0312,  14.2188],\n",
      "       requires_grad=True)\n",
      "model.decoder.layer_norm.bias Parameter containing:\n",
      "tensor([ 2.4695e-01,  1.7639e-01, -1.2610e-01, -7.0557e-01,  5.8929e-02,\n",
      "        -3.2373e-01,  6.1768e-01, -8.9502e-01,  3.5278e-01, -3.5449e-01,\n",
      "        -2.4980e+00,  4.2920e-01, -7.1924e-01, -5.9521e-01,  5.2686e-01,\n",
      "        -8.2642e-02,  1.4026e-01, -3.6816e-01, -1.7285e+00,  2.4329e-01,\n",
      "        -1.2488e-01, -2.9492e-01, -2.2583e-01, -7.1594e-02, -3.2178e-01,\n",
      "         3.4399e-01,  3.3521e-01, -1.1237e-01, -4.7241e-01,  5.1562e-01,\n",
      "        -2.0447e-01, -1.3770e+00,  6.6553e-01,  2.2488e-03, -1.3232e-01,\n",
      "         3.8354e-01,  1.0704e-02,  4.2871e-01,  3.8281e-01,  3.2196e-03,\n",
      "        -3.6157e-01, -8.8525e-01,  5.0928e-01,  1.4832e-01,  5.1221e-01,\n",
      "         1.2051e+00,  1.5808e-01, -2.8516e-01, -4.4141e-01,  1.4624e-01,\n",
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      "        -2.5171e-01, -6.5381e-01, -4.9652e-02, -1.0010e-01, -1.0254e+00,\n",
      "         3.8965e-01,  1.8689e-01,  8.7012e-01, -3.0078e-01,  2.6978e-01,\n",
      "         1.7822e-01, -7.0129e-02,  1.5662e-01, -2.6636e-01, -4.4116e-01,\n",
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      "        -8.4668e-01,  3.5522e-01,  3.8379e-01,  1.1938e-01, -1.9067e-01,\n",
      "        -2.6428e-02, -1.1504e+00,  9.9182e-02,  6.8652e-01, -2.2803e-01,\n",
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      "        -4.6289e-01,  3.9337e-02, -2.7783e-01, -3.7622e-01, -1.3037e-01,\n",
      "         6.2891e-01,  9.4666e-02, -4.0015e-01, -1.3916e+00, -1.6687e-01,\n",
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      "        -6.8213e-01, -2.3352e-01, -2.2314e-01, -9.7168e-01, -2.9126e-01,\n",
      "        -5.5908e-01,  2.6807e-01, -1.9360e-01,  2.6660e-01, -6.0986e-01,\n",
      "         4.0985e-02, -3.1641e-01, -6.9727e-01,  2.8976e-02, -3.6182e-01,\n",
      "        -7.7026e-02, -1.5996e+00,  8.2336e-02,  3.8672e-01, -1.4600e-01,\n",
      "        -5.1465e-01,  6.5186e-01, -1.6943e+00, -4.6826e-01,  3.1470e-01,\n",
      "         1.9928e-02, -2.8418e-01,  1.4160e-01, -9.4727e-01,  9.4849e-02,\n",
      "        -1.0127e+00,  3.2251e-01, -3.0200e-01, -1.1504e+00, -5.2295e-01,\n",
      "         3.9233e-01,  2.5781e-01,  2.6074e-01, -2.6953e-01, -1.2695e-01,\n",
      "         1.2451e-01,  2.0422e-01,  2.9492e-01, -4.7266e-01, -5.5615e-01,\n",
      "         6.8799e-01,  1.2201e-01,  3.8257e-01,  5.7190e-02, -1.4783e-01,\n",
      "        -2.4634e-01,  1.9434e-01, -2.1008e-01, -2.3877e-01,  5.5725e-02,\n",
      "        -1.2559e+00, -5.3467e-01,  2.2568e-02,  1.6602e-01,  2.0544e-01,\n",
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      "         2.0508e-01,  4.1895e-01, -9.6436e-01, -2.4841e-01,  5.1361e-02,\n",
      "         6.7711e-03,  3.2812e-01, -3.7378e-01, -3.4644e-01,  9.9976e-02,\n",
      "         4.6704e-01,  5.3809e-01,  4.8730e-01,  4.0918e-01,  9.5947e-02,\n",
      "        -6.2317e-02,  1.4136e-01, -1.9609e+00,  3.7451e-01, -2.7319e-01,\n",
      "        -1.4756e+00, -7.7734e-01, -5.8447e-01,  7.1387e-01,  3.3057e-01,\n",
      "        -3.3350e-01, -8.2275e-02,  1.3000e-01,  6.2061e-01, -5.2539e-01,\n",
      "        -3.5278e-02,  1.1432e-01,  7.0459e-01,  6.4270e-02, -9.1736e-02,\n",
      "         1.5723e-01,  6.7749e-02, -1.8103e-01, -1.9258e+00, -6.6113e-01,\n",
      "        -9.7119e-01, -1.2051e+00, -9.3066e-01,  3.3838e-01, -1.9983e-01,\n",
      "        -4.0649e-01, -3.5950e-02, -1.2372e-01,  5.4531e+00, -8.6572e-01,\n",
      "        -3.9551e-01, -1.8372e-02, -2.4182e-01,  9.5154e-02,  2.4658e-01,\n",
      "        -2.9834e-01,  2.0020e-01,  2.2595e-01,  1.6431e-01, -2.6221e-01,\n",
      "        -6.5332e-01, -3.8623e-01,  3.0029e-02,  1.0504e-01, -1.6621e+00,\n",
      "        -4.9854e-01, -2.3059e-01,  1.0602e-01,  1.7119e+00, -5.5908e-01,\n",
      "         1.1894e-02, -2.6880e-01, -5.0439e-01, -6.5967e-01, -1.0345e-01,\n",
      "         4.0747e-01, -1.5247e-01, -7.0752e-01,  3.9673e-01,  4.5825e-01,\n",
      "        -8.1885e-01,  2.8613e-01, -6.0840e-01,  7.7820e-02, -2.3975e-01,\n",
      "         1.8103e-01, -5.5469e-01,  2.0203e-01,  3.4473e-01, -1.1816e+00,\n",
      "        -1.7303e-02, -2.4805e-01, -2.8882e-01,  6.0840e-01, -2.0349e-01,\n",
      "         5.7129e-01, -4.0137e-01, -1.0506e-02, -2.6001e-01, -6.6309e-01,\n",
      "         4.0710e-02, -5.7275e-01,  8.5547e-01, -1.0065e-01, -2.2534e-01,\n",
      "        -7.5244e-01, -1.7151e-01,  2.7808e-01,  1.1261e-01, -1.9336e-01,\n",
      "         5.3009e-02, -2.9858e-01, -3.5254e-01, -7.4268e-01, -5.4413e-02,\n",
      "         4.9957e-02, -3.8086e-02,  2.7051e-01, -7.0850e-01,  5.8496e-01,\n",
      "        -3.5156e-01, -2.4377e-01, -1.7407e-01,  3.2837e-01,  2.3315e-01,\n",
      "        -5.3516e-01, -1.3901e-02,  1.1807e+00, -3.1763e-01, -7.7881e-02,\n",
      "        -4.1473e-02, -5.6006e-01, -3.3496e-01,  6.6345e-02, -2.7026e-01,\n",
      "         4.1260e-02,  7.9956e-02,  2.0957e+00,  8.6914e-02, -1.2268e-01,\n",
      "         5.4871e-02,  4.0771e-01, -1.3867e+00, -1.2634e-02, -6.9336e-02,\n",
      "         7.9590e-01,  2.4353e-02,  2.0156e+00, -6.6992e-01],\n",
      "       requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "\n",
    "    print(name, param.requires_grad)  # This should print False for all encoder parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c49a1f47-3844-4181-8121-5d41cd25abfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def divide(a, b):\n",
    "    return a / b\n",
    "\n",
    "def main():\n",
    "    x = 10\n",
    "    y = 0\n",
    "    result = divide(x, y)\n",
    "    print(f\"The result is: {result}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95ff14b5-a2e2-4f48-9904-2a5e02f87d4b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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