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\u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["pip install transformers datasets evaluate accelerate peft bitsandbytes trl -q"]},{"cell_type":"code","source":["from google.colab import drive\n","import datetime\n","from datasets import (load_dataset,\n","                      Dataset,\n","                      DatasetDict)\n","import pandas as pd\n","import torch\n","from transformers import (BitsAndBytesConfig,\n","                          AutoModelForCausalLM,\n","                          TrainingArguments,\n","                          Trainer,\n","                          AutoTokenizer,\n","                          set_seed)\n","from tqdm.auto import tqdm\n","from peft import (prepare_model_for_kbit_training,\n","                  get_peft_model,\n","                  LoraConfig,\n","                  TaskType)\n","\n","import accelerate\n","from trl import (SFTTrainer,\n","                 DataCollatorForCompletionOnlyLM)"],"metadata":{"id":"BLIySINaV1jy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["drive.mount('/content/drive')"],"metadata":{"id":"BKp1JhUTV36J","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1715888639584,"user_tz":-120,"elapsed":20588,"user":{"displayName":"koen van der leij","userId":"09547956247146573124"}},"outputId":"03e303ca-14ba-4827-c036-8774f57edeeb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["base_model = \"microsoft/phi-2\"\n","\n","set_seed(42)\n","timestamp = str(int(datetime.datetime.now().replace(microsecond=0).timestamp()))\n","print(timestamp)\n","\n","filestamp = \"1711835043\" # Select the timestamp of the latest version\n","source = \"mix\"\n","\n","train_path = f'/content/drive/MyDrive/Thesis data/relevant_selection/{filestamp}_{source}_train.json'\n","val_path = f'/content/drive/MyDrive/Thesis data/relevant_selection/{filestamp}_{source}_val.json'\n","\n","log_path = f'/content/drive/MyDrive/Thesis_models/Logs/{timestamp}_{filestamp}_{source}'\n","\n","adapter_name = f\"{timestamp}_{filestamp}_{source}_trained\"\n","output_dir = f'/content/drive/MyDrive/Thesis_models/{adapter_name}'\n","\n","batch_size = 2\n","logging_steps = 1800\n","epochs = 10\n"],"metadata":{"id":"V0jIs-T6V5p5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1715888641158,"user_tz":-120,"elapsed":322,"user":{"displayName":"koen van der leij","userId":"09547956247146573124"}},"outputId":"490b28a0-66a4-471f-ffa8-0bdccd233de3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1715888640\n"]}]},{"cell_type":"code","source":["# Data loading\n","train = pd.read_json(train_path)\n","val = pd.read_json(val_path)\n","\n","train_ds = Dataset.from_pandas(train).rename_column(\"result_1\", \"labels\")\n","val_ds = Dataset.from_pandas(val).rename_column(\"result_1\", \"labels\")\n","\n","data = DatasetDict()\n","data['train'] = train_ds\n","data['val'] = val_ds\n","\n","del train, val, train_ds, val_ds"],"metadata":{"id":"tCaNDQ20XIAo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Load the tokenizer\n","tokenizer = AutoTokenizer.from_pretrained(base_model,trust_remote_code=True,)\n","tokenizer.pad_token = tokenizer.eos_token"],"metadata":{"id":"AnAT7TPjXLrI","colab":{"base_uri":"https://localhost:8080/","height":280,"referenced_widgets":["c6118d00202d4f9e8bd8ac251d08cee8","f7b1ea194e304481a7158fbad64afd88","5358f80bee114aa0865566652b94c92a","b6518f3dd32b43b094346b8cb1257ed8","82c0f025c06e4242ade1858f6d4c7076","a890290e2b034ff78e321b15cff759be","a87d1394fa3a440aa3dd6b98a5caf50c","a5398f9a2aa8423b8cf20d6a1bc297b3","9f850fd546bd4a8b86d0d1927fe9d9c4","4ab170f125f1448fa9b31da64f2016cc","482e7788e4eb4cb3bb218f331298f104","eb49bfdd9f11463a8ba452fa710633d5","f1476bb6e3b447d0b5f9dd2a9f0ecfde","4b1e8dc0eb944c6d8d80dde557c70791","0f6e7e5b19a74807b6f8e0f140efc8f0","74e1c916d97b490cbff73fa71389abbe","d0426f1ad3be44a39fcb1e2ddb120d2d","48a21de02eb4445e8764c85cb626d2c2","b26ef305d2614c92b719c9b24b6065c5","37860da176a54f009c3e85e0b8924cf6","8ac1eb78db8d47f79cb3c741d0d23330","e674983582244d5692c4d6d876549a84","a5bcd8ccce3c4426b6761aa74b80a371","2ba5d35662d04b549bb508f653c873a3","bcbf34a36048442eaf70ae314c155d12","b5e66a4e37c2481b8aea710b53fc9d10","f055266594a94b7a8998cf15ba0b12bb","96618e34748a421a8194a0c7cc066a57","177de2929ff64ea0bb6222e97af7b0e5","b331e4b9178d4d3292d9e0ece42d5995","ff5c86ad4cf44bf8a8cede4ec5f41679","a7bba121e5ec4cdbb8b1b5d4dc4328bb","ec5a64861096499c9635338909a6187a","4ed4d63b035249999cf1530411a8d938","e1024de54eb54de991c12374f64b9684","2e31efed32ad408e86cd3e1bcdbf2d96","15c7f829cdf24b298cb48789e7ca8a9a","24e8974a97d34a2f8b2416c63b6d9b78","47b0bfd1a17b4ab4bfc0a70766444b1a","23fadad944464246963a1b80e2a0c2de","2ba142e45e224dd09515965b293bf611","3d57cce360a541b69e2d947b144a12ec","e67fab8869824e9da8e817162078a37b","53c27300bb684c60b619fd0de303ea69","73d383bd0bc74717a04333ea2772ca24","460cfba59a7849cbae06f17b390188a9","6cd1f04a10bc4f39ba8b12bd592a0b4f","8786f9b090404176b3e88bb2f61ac98e","895c4b8004f941daadf29fbcbe6bc6e3","98c4d51364474029aa3ea40e9fd55c73","9610d5341aa3404dbccac08b887994da","a33eeeb194c849098582b02f715cdd87","1b200004354e4713bbad25d5ffbff559","97ef1fafad4a4cfa8014e875bc82ffea","27266d53def044a0b4321f518e066cf6","0522aefc9100446b8c9995fa2df624ca","542fd645b6404a879510365629ccb07f","cc29042c9f6341e0b18ece08d5ce982c","f8141d80cb6e4b93918a83c0a1788171","da1fd94244844e06b1f83d96ad4d51ce","f2b85bc997504ecd844c59233f01a4a6","7fa1171d4a314fb3ab2054fee6054dd4","d606f9d358e7491db28e99e45aa19cb9","4bbf1a118fee4d5ab16b7e6cc778d996","25c3dbdc194544d9a1004ab21eb1c249","376725d6d88a4c6cb6a10fcc2d90a09a"]},"executionInfo":{"status":"ok","timestamp":1715888656194,"user_tz":-120,"elapsed":6643,"user":{"displayName":"koen van der leij","userId":"09547956247146573124"}},"outputId":"712a84bd-5f37-4d2b-b7d4-0495887aa4b5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/7.34k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c6118d00202d4f9e8bd8ac251d08cee8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eb49bfdd9f11463a8ba452fa710633d5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a5bcd8ccce3c4426b6761aa74b80a371"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4ed4d63b035249999cf1530411a8d938"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["added_tokens.json:   0%|          | 0.00/1.08k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"73d383bd0bc74717a04333ea2772ca24"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0522aefc9100446b8c9995fa2df624ca"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]}]},{"cell_type":"code","source":["# Quantization config\n","bnb_config = BitsAndBytesConfig(\n","    load_in_4bit= True,\n","    bnb_4bit_quant_type= \"nf4\",\n","    bnb_4bit_compute_dtype= torch.bfloat16,\n","    bnb_4bit_use_double_quant= False,\n",")"],"metadata":{"id":"vayAW2rQXNju"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Load the base model\n","model = AutoModelForCausalLM.from_pretrained(\n","    base_model,\n","    quantization_config=bnb_config,\n","    device_map='auto',\n",")"],"metadata":{"id":"ktFqc522XPk-","colab":{"base_uri":"https://localhost:8080/","height":241,"referenced_widgets":["3d88846bd3024e8196e9ac1ee8dd0b31","2ff30c124df946f887b99c31be9bd7a4","cea19ded313045ffb18638011d0106ac","0a767a6891604df1828d52165a4bdbce","8c39e8a9437044a8b20b52639aee3825","002e4b55cd654ea8bb307931b3a70597","29ccf27df6434b2b9423745d76cadc88","4b70ff55dbaf4f518c5229f13fcdc8a6","8869dcbb1bd94e798e91d45037fd33f0","4a2ada8de3044eee89a2755b069dac79","24bfa5ffdb0e4f8aaf9a37a367a8d633","51b9b88139a9472e910119027f23cf1e","3e7236768c994facbead2d27d2276fab","73e7f9e193dc4627ae8ee3343c869e22","63d84fcc0ea344ee9bdd824d6cde579e","c3e0a6863206448ebca105e53c75597d","ec4efdcbefab418b95f73cbf4b4dbb91","afca9a41d87f491c823c5788f3d34cc1","1d75e7ae47844c9fb48a9c09bfdab7fe","0c9b589f372e4181935f84b8d1f90ee1","e8d1981d2e104b1c9d5cbd77390287a3","3e6f106266cd4b9a822cc239e556c90a","56c586fa544b48c58b601dcb187a6ad0","54a4b6fbdc024fea943f314fb384bdcf","272408dfea274ef2a1a889b24a9eb3d2","cd6b0f1c900a495fbd98d14ce7782ecf","7ca086ebbd68498e891e4476c3e92254","5c2f035bd54e4a1b99239bdb041fd73b","fa7135a6bc2b4f21b7def0546cea47e1","1c8002241e0d4f19a76b4c7dcc26e718","5cde78ea90bf435ebe89e919bcda56c9","1371d558fb4a475b9263b0a8f422acd2","72318ce01ef64dbaaf7153f3761b46ba","b6c06e3c17654df8b91f316afce96029","739f73a681784c439f7df4912ed377e3","100758c0c0924b83b710eb7eb8c23946","6e03f7854b8544eb8bd8e2e46ccbc2a1","7699e06fe6674b269c695c1c106f02cb","abaea76c33884cea9a24f78b09105a83","f0bb760ae93b40ac9d47ecabe010bb3a","5f8a225fcffb424db21db589b4c6f2db","0d121f59e64f49d5a3b66b8b0fb2ad11","7e91343c22dd423c896ffe6d55baa591","4fb65357e69c4f1ca20afe7c0b531178","e80d0356ff0743e18ef24b6e5430289e","52180727e6f3451bb938d6c270fb443b","c71bb2e2857e4262b8da7ff51d37ec8f","a752e780c32243b4bc070a7620752880","326b9f6d124d4e529866a1cda8cf0103","903967a7daf04f11822639d4897c9127","4dae1b5657b245f1ae513ddff83230bc","bfbcdb96a70b4026b96f57c2ae5a70b9","01c7426a9dbe4ea3a2f7c1243027a6a6","db4ec2a013fc4371b7e4640441607b91","43fe61cb3227413b8b7097d4ede51f14","e787872041ae491d8e0baaf28d787eac","de9c70d901044fa497e019ae9f1d3b7e","cee849efddcc40ba87b742b436af7d3a","d1b94a18cdfb4f28897ec06b5b4ed698","19436d02c3c2443cbdfb082f1beb5c23","9f50ea7c81504f11b14e0d49bbf7f2ef","d20fb72d88ce4716b50d1fbd46707d22","39eab79b82fe4cfb93145b48ee53a229","0e4e03ebce5742a08cdc672066a404e3","474e26719bb441cf981cbfaa8e6c52d9","cd40cb4f16dd486fa2e579fb293c7971","99a02538ad5f42fcb48d9e4820fc13d7","475d42d4e2054143968d85bd7215d709","b3549f98bb61481198873fbc97a180d3","152da9427c894764b545e9cfb633ce7c","70136366baf8477d9a82c7518a815755","d8dde7c938ef4d9585af9d153593085b","7cfe8a3cffd043c590756c0f33484ada","af7160a7eec64581a064195a6ea4b7d0","33386aede2624897819bdd4772d02340","97ceab942715421cb6abfc2450eeb1f1","a8da4d268e884057afbf237698f1e321"]},"executionInfo":{"status":"ok","timestamp":1715888734146,"user_tz":-120,"elapsed":73077,"user":{"displayName":"koen van der leij","userId":"09547956247146573124"}},"outputId":"2ddb5395-e486-46d4-b99e-7cdfcd3b21b8"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/735 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3d88846bd3024e8196e9ac1ee8dd0b31"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json:   0%|          | 0.00/35.7k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"51b9b88139a9472e910119027f23cf1e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"56c586fa544b48c58b601dcb187a6ad0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00002.safetensors: 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"99a02538ad5f42fcb48d9e4820fc13d7"}},"metadata":{}}]},{"cell_type":"code","source":["model = prepare_model_for_kbit_training(model)"],"metadata":{"id":"lCPEaDqsXSM9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Parameter efficient fine-tuning config\n","peft_config = LoraConfig(\n","                            r=32,\n","                            lora_alpha=32,\n","                            target_modules=[\n","                            'q_proj','k_proj','v_proj','dense',\n","                            ],\n","                            bias=\"none\",\n","                            lora_dropout=0.05,\n","                        )\n"],"metadata":{"id":"LXD8V_2lXWI7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Function to format the prompts\n","def formatting_prompts_func(example):\n","    output_texts = []\n","    for i in range(len(example['prompt'])):\n","        text = f\"{example['prompt'][i]} {example['labels'][i]}\"\n","        output_texts.append(text)\n","    return output_texts\n","\n","response_template = \" \\n\\nOutput:\"\n","collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)\n","\n","training_args = TrainingArguments(\n","    output_dir=output_dir,\n","    learning_rate=5e-6,\n","    per_device_train_batch_size=batch_size,\n","    per_device_eval_batch_size=batch_size,\n","    num_train_epochs=epochs,\n","    weight_decay=0.0,\n","    # max_grad_norm=0.5,\n","    evaluation_strategy=\"steps\",\n","    label_names = [\"labels\"],\n","    eval_steps=logging_steps,\n","    logging_steps=logging_steps,\n","    save_steps=logging_steps,\n","    load_best_model_at_end=True,\n",")\n","\n","trainer = SFTTrainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=data[\"train\"],\n","    peft_config = peft_config,\n","    eval_dataset=data[\"val\"],\n","    formatting_func=formatting_prompts_func,\n","    tokenizer=tokenizer,\n","    data_collator=collator,\n","    max_seq_length=2048,\n","\n",")\n","\n"],"metadata":{"id":"lZWi4cJRXZ9v","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["e33ca09d518345ada63f5ac2f7bf0961","e2c48a5d339e422598925104e614c577","c60c7a85fcb745f0906404ac6109802e","8c979f55990e4fef8052a241635d2ac0","6a9b3398491b449388d1c13610c9a8f8","ac3f39abf1df4d2c89adb2f4a9f813a2","d2b01f70ad2c4784afdcad3de2e0cb58","d19cf41d4f8c4e479ef6496b0bc85077","0708bdbc51ba460faf4ff1286af0a439","04a2981bdfd148f1abe474c112af781d","ae572d57e6064c9dbab53dbe26b1af64","aa12d421a2844426853d426b45939d27","d96a048e9c674693bb08a041f8bf14be","346dfd22f41d479aae8315ff947e9c84","968fb7db305c472284957656c89a6237","48e613029b614484b4ef5f1df0fdfbb8","6630635adec84a8198cab0852c847a31","eb6baee3ba1b49b59a477c1fba97e901","782b1dd68f56477683bacfca9a490dd7","8ba51f46a0d74cbab1512853339dbcaf","0f3c542ec59f470d994fb2e1e5458bcc","dbf5ac0028004537bd9e6eda822d8f9a"]},"executionInfo":{"status":"ok","timestamp":1715888738958,"user_tz":-120,"elapsed":4815,"user":{"displayName":"koen van der leij","userId":"09547956247146573124"}},"outputId":"6c6b314b-e61c-442b-a1a2-597e7e1f73a0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/3600 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e33ca09d518345ada63f5ac2f7bf0961"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/200 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aa12d421a2844426853d426b45939d27"}},"metadata":{}}]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"id":"fAYddb_WXiR8","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1715928966077,"user_tz":-120,"elapsed":40227122,"user":{"displayName":"koen van der leij","userId":"09547956247146573124"}},"outputId":"72430af4-ec60-433c-b190-00afc1f191f5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='18000' max='18000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [18000/18000 11:10:21, Epoch 10/10]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1800</td>\n","      <td>0.965200</td>\n","      <td>0.750982</td>\n","    </tr>\n","    <tr>\n","      <td>3600</td>\n","      <td>0.735100</td>\n","      <td>0.731519</td>\n","    </tr>\n","    <tr>\n","      <td>5400</td>\n","      <td>0.734600</td>\n","      <td>0.693762</td>\n","    </tr>\n","    <tr>\n","      <td>7200</td>\n","      <td>0.730200</td>\n","      <td>0.698244</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>0.725600</td>\n","      <td>0.694269</td>\n","    </tr>\n","    <tr>\n","      <td>10800</td>\n","      <td>0.719900</td>\n","      <td>0.700679</td>\n","    </tr>\n","    <tr>\n","      <td>12600</td>\n","      <td>0.716300</td>\n","      <td>0.703563</td>\n","    </tr>\n","    <tr>\n","      <td>14400</td>\n","      <td>0.721400</td>\n","      <td>0.698256</td>\n","    </tr>\n","    <tr>\n","      <td>16200</td>\n","      <td>0.711900</td>\n","      <td>0.695788</td>\n","    </tr>\n","    <tr>\n","      <td>18000</td>\n","      <td>0.706400</td>\n","      <td>0.695454</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:460: 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","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=18000, training_loss=0.7466578369140625, metrics={'train_runtime': 40226.8268, 'train_samples_per_second': 0.895, 'train_steps_per_second': 0.447, 'total_flos': 3.690779296267469e+17, 'train_loss': 0.7466578369140625, 'epoch': 10.0})"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["log_history = pd.DataFrame(trainer.state.log_history)\n","log_history.to_csv(log_path)"],"metadata":{"id":"GabJShohubY7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import runtime\n","runtime.unassign()"],"metadata":{"id":"CY6BjBhGud7P"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"T1h11Tfhuo_E"},"execution_count":null,"outputs":[]}]}