peft_training.ipynb

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   "source": [
    "!pip install optimum\n",
    "!pip install auto-gptq"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f595948",
   "metadata": {},
   "source": [
    "# Finetuning Quantized Llama-2-7b-Chat-GPTQ using PEFT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ecccb11",
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    "tags": []
   },
   "source": [
    "## Working with LLAMA 7B quantized in 4bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "793eeb09",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_checkpoint = \"TheBloke/Llama-2-7b-Chat-GPTQ\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe980c41",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5125e9cf",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from peft import prepare_model_for_kbit_training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34152157",
   "metadata": {
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   "source": [
    "quantization_config = GPTQConfig(bits=4,use_exllama=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "581354b5",
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   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(model_checkpoint,quantization_config=quantization_config)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 12,
   "id": "f550dffd",
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    {
     "data": {
      "text/plain": [
       "{'quant_method': <QuantizationMethod.GPTQ: 'gptq'>,\n",
       " 'bits': 4,\n",
       " 'tokenizer': None,\n",
       " 'dataset': None,\n",
       " 'group_size': 128,\n",
       " 'damp_percent': 0.01,\n",
       " 'desc_act': False,\n",
       " 'sym': True,\n",
       " 'true_sequential': True,\n",
       " 'use_cuda_fp16': False,\n",
       " 'model_seqlen': None,\n",
       " 'block_name_to_quantize': None,\n",
       " 'module_name_preceding_first_block': None,\n",
       " 'batch_size': 1,\n",
       " 'pad_token_id': None,\n",
       " 'use_exllama': False,\n",
       " 'max_input_length': None,\n",
       " 'exllama_config': {'version': <ExllamaVersion.ONE: 1>},\n",
       " 'cache_block_outputs': True,\n",
       " 'modules_in_block_to_quantize': None}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.config.quantization_config.to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da338d97",
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    "tags": []
   },
   "source": [
    "### Preprocessing model for quantized training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cfa2d3e",
   "metadata": {
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     "duration": 0.016048,
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     "start_time": "2024-04-27T15:23:59.905809",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Gradient checkpointing strikes a compromise between saving activations from the forward pass and  forget all activations during the forward pass and recompute them on demand during the backward pass. It strategically saves selected activations throughout the computational graph so only a fraction of the activations need to be re-computed for the gradients. This lowers memory load.For details refer to [@HuggingFace](https://huggingface.co/docs/transformers/v4.18.0/en/performance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bccb2a32",
   "metadata": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "model.gradient_checkpointing_enable()\n",
    "model = prepare_model_for_kbit_training(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b901699",
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     "duration": 0.01659,
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   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "61f8a990",
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    "tags": []
   },
   "source": [
    "### Convert model to peft mdoel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6e4a8356",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from peft import LoraConfig, get_peft_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96992825",
   "metadata": {
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     "start_time": "2024-04-27T15:24:00.140903",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "LoRA is a parameter-efficient fine-tuning (PEFT) method. It decomposes large attention matrix into two low rank matrices and drastically reduces number of parameters to be trained. The attention dimension is the rank of these low rank matrices. It detemines the degree of information reatined. Smaller rank means more compression and high degree of information loss. Research Papaer - [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
   "id": "bf760d4d",
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   "source": [
    "lora_config = LoraConfig(r=8,\n",
    "                        lora_alpha=32,\n",
    "                        target_modules = [\"k_proj\",\"o_proj\",\"q_proj\",\"v_proj\"],\n",
    "                        lora_dropout = 0.05,\n",
    "                        bias = 'none',\n",
    "                        task_type = 'CAUSAL_LM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23d94434",
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   "outputs": [],
   "source": [
    "model = get_peft_model(model,lora_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8bc5ccee",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 8,388,608 || all params: 270,798,848 || trainable%: 3.097726619575575\n"
     ]
    }
   ],
   "source": [
    "model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34275a8e",
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   },
   "source": [
    "### Dataset\n",
    "\n",
    "- flytech/python-codes-25k form HiggingFace\n",
    "- instruction: The instructional task to be performed / User input.\n",
    "- input: Very short, introductive part of AI response or empty.\n",
    "- output: Python code that accomplishes the task.\n",
    "- text: All fields combined together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "69c0547a",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset,load_from_disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5bde0ac",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "raw_dataset = load_dataset('flytech/python-codes-25k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9d1143c7",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "raw_dataset = raw_dataset['train'].train_test_split(test_size=0.1,seed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "94e0a3fd",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "raw_dataset['train'] = raw_dataset['train'].select([*range(20000)])\n",
    "raw_dataset['test'] = raw_dataset['train'].select([*range(100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e7e48c0",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'input', 'output', 'instruction'],\n",
       "        num_rows: 20000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'input', 'output', 'instruction'],\n",
       "        num_rows: 100\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset"
   ]
  },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['text', 'input', 'output', 'instruction'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset['train'][0].keys()"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "\"Code an optimization problem using Python that finds the maximum of a function with two optimization variables and a given constraint f(x, y) = 3x + 2y\\n Constraint: x + y ? 10 Setting things in motion! Here we go! ```python\\nfrom scipy.optimize import minimize\\n\\ndef f(x):\\n return 3*x[0] + 2*x[1]\\n\\ndef con(x):\\n return x[0] + x[1] - 10\\n\\ncons = ({'type': 'ineq', 'fun': con})\\n\\nresult = minimize(f, [0, 0], method='SLSQP', constraints=cons)\\nx = result.x\\n\\nprint('Solution:', x)\\nprint('Maximum value:', result.value)\\n```\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_dataset['train'][0]['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d34c4e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-27T15:24:08.066390Z",
     "iopub.status.busy": "2024-04-27T15:24:08.066048Z",
     "iopub.status.idle": "2024-04-27T15:24:13.537742Z",
     "shell.execute_reply": "2024-04-27T15:24:13.536776Z"
    },
    "papermill": {
     "duration": 5.496113,
     "end_time": "2024-04-27T15:24:13.540419",
     "exception": false,
     "start_time": "2024-04-27T15:24:08.044306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tokenized_dataset = raw_dataset.map(lambda samples: tokenizer(samples['text']), batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67beb0aa",
   "metadata": {
    "papermill": {
     "duration": 0.018682,
     "end_time": "2024-04-27T15:24:13.578112",
     "exception": false,
     "start_time": "2024-04-27T15:24:13.559430",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Data Collator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "690661ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-27T15:24:13.618074Z",
     "iopub.status.busy": "2024-04-27T15:24:13.617702Z",
     "iopub.status.idle": "2024-04-27T15:24:14.438663Z",
     "shell.execute_reply": "2024-04-27T15:24:14.437537Z"
    },
    "papermill": {
     "duration": 0.844753,
     "end_time": "2024-04-27T15:24:14.442140",
     "exception": false,
     "start_time": "2024-04-27T15:24:13.597387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForLanguageModeling\n",
    "\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b2124fc",
   "metadata": {
    "papermill": {
     "duration": 0.022328,
     "end_time": "2024-04-27T15:24:14.489990",
     "exception": false,
     "start_time": "2024-04-27T15:24:14.467662",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8cb7786d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-27T15:24:14.536844Z",
     "iopub.status.busy": "2024-04-27T15:24:14.536070Z",
     "iopub.status.idle": "2024-04-27T15:24:14.549321Z",
     "shell.execute_reply": "2024-04-27T15:24:14.548384Z"
    },
    "papermill": {
     "duration": 0.038343,
     "end_time": "2024-04-27T15:24:14.551174",
     "exception": false,
     "start_time": "2024-04-27T15:24:14.512831",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch.optim import AdamW\n",
    "\n",
    "optimizer = AdamW(model.parameters(), lr=2e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "999c700e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-27T15:24:14.596879Z",
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     "exception": false,
     "start_time": "2024-04-27T15:24:14.572094",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LlamaConfig {\n",
       "  \"_name_or_path\": \"TheBloke/Llama-2-7b-Chat-GPTQ\",\n",
       "  \"architectures\": [\n",
       "    \"LlamaForCausalLM\"\n",
       "  ],\n",
       "  \"attention_bias\": false,\n",
       "  \"attention_dropout\": 0.0,\n",
       "  \"bos_token_id\": 1,\n",
       "  \"eos_token_id\": 2,\n",
       "  \"hidden_act\": \"silu\",\n",
       "  \"hidden_size\": 4096,\n",
       "  \"initializer_range\": 0.02,\n",
       "  \"intermediate_size\": 11008,\n",
       "  \"max_length\": 4096,\n",
       "  \"max_position_embeddings\": 4096,\n",
       "  \"model_type\": \"llama\",\n",
       "  \"num_attention_heads\": 32,\n",
       "  \"num_hidden_layers\": 32,\n",
       "  \"num_key_value_heads\": 32,\n",
       "  \"pad_token_id\": 0,\n",
       "  \"pretraining_tp\": 1,\n",
       "  \"quantization_config\": {\n",
       "    \"batch_size\": 1,\n",
       "    \"bits\": 4,\n",
       "    \"block_name_to_quantize\": null,\n",
       "    \"cache_block_outputs\": true,\n",
       "    \"damp_percent\": 0.01,\n",
       "    \"dataset\": null,\n",
       "    \"desc_act\": false,\n",
       "    \"exllama_config\": {\n",
       "      \"version\": 1\n",
       "    },\n",
       "    \"group_size\": 128,\n",
       "    \"max_input_length\": null,\n",
       "    \"model_seqlen\": null,\n",
       "    \"module_name_preceding_first_block\": null,\n",
       "    \"modules_in_block_to_quantize\": null,\n",
       "    \"pad_token_id\": null,\n",
       "    \"quant_method\": \"gptq\",\n",
       "    \"sym\": true,\n",
       "    \"tokenizer\": null,\n",
       "    \"true_sequential\": true,\n",
       "    \"use_cuda_fp16\": false,\n",
       "    \"use_exllama\": false\n",
       "  },\n",
       "  \"rms_norm_eps\": 1e-06,\n",
       "  \"rope_scaling\": null,\n",
       "  \"rope_theta\": 10000.0,\n",
       "  \"tie_word_embeddings\": false,\n",
       "  \"torch_dtype\": \"float16\",\n",
       "  \"transformers_version\": \"4.39.3\",\n",
       "  \"use_cache\": true,\n",
       "  \"vocab_size\": 32000\n",
       "}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3d82ed24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-27T15:24:14.653287Z",
     "iopub.status.busy": "2024-04-27T15:24:14.652935Z",
     "iopub.status.idle": "2024-04-27T15:24:14.658858Z",
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     "end_time": "2024-04-27T15:24:14.660864",
     "exception": false,
     "start_time": "2024-04-27T15:24:14.632890",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GenerationConfig {\n",
       "  \"bos_token_id\": 1,\n",
       "  \"eos_token_id\": 2,\n",
       "  \"pad_token_id\": 0\n",
       "}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.generation_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "746fe689",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-27T15:24:14.699714Z",
     "iopub.status.busy": "2024-04-27T15:24:14.699153Z",
     "iopub.status.idle": "2024-04-27T15:24:16.246666Z",
     "shell.execute_reply": "2024-04-27T15:24:16.245625Z"
    },
    "papermill": {
     "duration": 1.569744,
     "end_time": "2024-04-27T15:24:16.249361",
     "exception": false,
     "start_time": "2024-04-27T15:24:14.679617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import Trainer, TrainingArguments\n",
    "\n",
    "args = TrainingArguments(\n",
    "    output_dir=\"codeparrot-ds\",\n",
    "    per_device_train_batch_size=4,\n",
    "    per_device_eval_batch_size=4,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    logging_steps=10,\n",
    "    gradient_accumulation_steps=8,\n",
    "    num_train_epochs=1,\n",
    "    warmup_steps=8,\n",
    "    lr_scheduler_type=\"linear\",\n",
    "    learning_rate=2e-4,\n",
    "    save_strategy='epoch',\n",
    "    fp16=True,\n",
    "    report_to='none'\n",
    "\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    args=args,\n",
    "    data_collator=data_collator,\n",
    "    train_dataset=tokenized_dataset[\"train\"],\n",
    "    eval_dataset=tokenized_dataset[\"test\"],\n",
    ")\n",
    "\n",
    "model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9573a9ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-27T15:24:16.289599Z",
     "iopub.status.busy": "2024-04-27T15:24:16.288831Z",
     "iopub.status.idle": "2024-04-28T01:51:17.179124Z",
     "shell.execute_reply": "2024-04-28T01:51:17.178163Z"
    },
    "papermill": {
     "duration": 37620.912304,
     "end_time": "2024-04-28T01:51:17.181211",
     "exception": false,
     "start_time": "2024-04-27T15:24:16.268907",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [625/625 10:25:58, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.708600</td>\n",
       "      <td>0.705514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "TrainOutput(global_step=625, training_loss=0.8139624240875244, metrics={'train_runtime': 37620.5067, 'train_samples_per_second': 0.532, 'train_steps_per_second': 0.017, 'total_flos': 4350923021058048.0, 'train_loss': 0.8139624240875244, 'epoch': 1.0})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "trainer.train()"
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