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LoRA.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a9935ae2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "===================================BUG REPORT===================================\n",
+      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
+      "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
+      "================================================================================\n",
+      "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
+      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
+      "CUDA SETUP: Detected CUDA version 117\n",
+      "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
+    "import os\n",
+    "\n",
+    "import torch\n",
+    "from torch.optim import AdamW\n",
+    "from torch.utils.data import DataLoader\n",
+    "from peft import (\n",
+    "    get_peft_config,\n",
+    "    get_peft_model,\n",
+    "    get_peft_model_state_dict,\n",
+    "    set_peft_model_state_dict,\n",
+    "    LoraConfig,\n",
+    "    PeftType,\n",
+    "    PrefixTuningConfig,\n",
+    "    PromptEncoderConfig,\n",
+    ")\n",
+    "\n",
+    "import evaluate\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e3b13308",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 32\n",
+    "model_name_or_path = \"roberta-large\"\n",
+    "task = \"mrpc\"\n",
+    "peft_type = PeftType.LORA\n",
+    "device = \"cuda\"\n",
+    "num_epochs = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0526f571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peft_config = LoraConfig(task_type=\"SEQ_CLS\", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1)\n",
+    "lr = 3e-4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c2697d07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f74797387a941cbb0709487b8808eba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/27.9k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset glue (/home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a9ecc2f624343c3af8d1824afb66ac5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33b071c0e5794cb48b38bbf68f22b49b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/4 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a977694036394d5c99adfb13c023e258",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "facc8d9092dc4abe9e553fc8e5b795b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
+    "    padding_side = \"left\"\n",
+    "else:\n",
+    "    padding_side = \"right\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
+    "if getattr(tokenizer, \"pad_token_id\") is None:\n",
+    "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
+    "\n",
+    "datasets = load_dataset(\"glue\", task)\n",
+    "metric = evaluate.load(\"glue\", task)\n",
+    "\n",
+    "\n",
+    "def tokenize_function(examples):\n",
+    "    # max_length=None => use the model max length (it's actually the default)\n",
+    "    outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
+    "    return outputs\n",
+    "\n",
+    "\n",
+    "tokenized_datasets = datasets.map(\n",
+    "    tokenize_function,\n",
+    "    batched=True,\n",
+    "    remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
+    ")\n",
+    "\n",
+    "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
+    "# transformers library\n",
+    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
+    "\n",
+    "\n",
+    "def collate_fn(examples):\n",
+    "    return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
+    "\n",
+    "\n",
+    "# Instantiate dataloaders.\n",
+    "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
+    "eval_dataloader = DataLoader(\n",
+    "    tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ed5ac74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
+    "model = get_peft_model(model, peft_config)\n",
+    "model.print_trainable_parameters()\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0d2d0381",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
+    "\n",
+    "# Instantiate scheduler\n",
+    "lr_scheduler = get_linear_schedule_with_warmup(\n",
+    "    optimizer=optimizer,\n",
+    "    num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
+    "    num_training_steps=(len(train_dataloader) * num_epochs),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fa0e73be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                  | 0/115 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:28<00:00,  4.08it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 0: {'accuracy': 0.7009803921568627, 'f1': 0.8189910979228486}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:27<00:00,  4.18it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 1: {'accuracy': 0.7622549019607843, 'f1': 0.8482003129890453}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:27<00:00,  4.20it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 2: {'accuracy': 0.8651960784313726, 'f1': 0.9005424954792043}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 3: {'accuracy': 0.8921568627450981, 'f1': 0.9228070175438596}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 4: {'accuracy': 0.8970588235294118, 'f1': 0.9257950530035336}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 5: {'accuracy': 0.8823529411764706, 'f1': 0.9169550173010381}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:30<00:00,  3.81it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 6: {'accuracy': 0.8799019607843137, 'f1': 0.9170896785109983}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 7: {'accuracy': 0.8799019607843137, 'f1': 0.9150779896013865}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 8: {'accuracy': 0.8921568627450981, 'f1': 0.9233449477351917}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 9: {'accuracy': 0.8872549019607843, 'f1': 0.9217687074829931}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:27<00:00,  4.16it/s]\n",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 10: {'accuracy': 0.8774509803921569, 'f1': 0.9137931034482758}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 11: {'accuracy': 0.9068627450980392, 'f1': 0.9321428571428573}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 12: {'accuracy': 0.8946078431372549, 'f1': 0.925476603119584}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 13: {'accuracy': 0.8897058823529411, 'f1': 0.922279792746114}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 14: {'accuracy': 0.8970588235294118, 'f1': 0.9265734265734265}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 15: {'accuracy': 0.8970588235294118, 'f1': 0.9263157894736843}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
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+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 16: {'accuracy': 0.8921568627450981, 'f1': 0.9233449477351917}\n"
+     ]
+    },
+    {
+     "name": "stderr",
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+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:27<00:00,  4.18it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.58it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 17: {'accuracy': 0.8897058823529411, 'f1': 0.9220103986135182}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:30<00:00,  3.78it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.58it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 18: {'accuracy': 0.8921568627450981, 'f1': 0.9233449477351917}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:27<00:00,  4.16it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████���█████████| 13/13 [00:01<00:00,  8.60it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 19: {'accuracy': 0.8946078431372549, 'f1': 0.924693520140105}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.to(device)\n",
+    "for epoch in range(num_epochs):\n",
+    "    model.train()\n",
+    "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
+    "        batch.to(device)\n",
+    "        outputs = model(**batch)\n",
+    "        loss = outputs.loss\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        lr_scheduler.step()\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "    model.eval()\n",
+    "    for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+    "        batch.to(device)\n",
+    "        with torch.no_grad():\n",
+    "            outputs = model(**batch)\n",
+    "        predictions = outputs.logits.argmax(dim=-1)\n",
+    "        predictions, references = predictions, batch[\"labels\"]\n",
+    "        metric.add_batch(\n",
+    "            predictions=predictions,\n",
+    "            references=references,\n",
+    "        )\n",
+    "\n",
+    "    eval_metric = metric.compute()\n",
+    "    print(f\"epoch {epoch}:\", eval_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2b2caca",
+   "metadata": {},
+   "source": [
+    "## Share adapters on the 🤗 Hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "990b3c93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/smangrul/roberta-large-peft-lora/commit/c2c661898b8b6a0c68ecd068931e598d0a79686b', commit_message='Upload model', commit_description='', oid='c2c661898b8b6a0c68ecd068931e598d0a79686b', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.push_to_hub(\"smangrul/roberta-large-peft-lora\", use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d140b26",
+   "metadata": {},
+   "source": [
+    "## Load adapters from the Hub\n",
+    "\n",
+    "You can also directly load adapters from the Hub using the commands below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4d55c87d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
+      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.out_proj.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+      "  0%|                                                                                                   | 0/13 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.45it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.8946078431372549, 'f1': 0.924693520140105}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from peft import PeftModel, PeftConfig\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "\n",
+    "peft_model_id = \"smangrul/roberta-large-peft-lora\"\n",
+    "config = PeftConfig.from_pretrained(peft_model_id)\n",
+    "inference_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+    "\n",
+    "# Load the Lora model\n",
+    "inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
+    "\n",
+    "inference_model.to(device)\n",
+    "inference_model.eval()\n",
+    "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+    "    batch.to(device)\n",
+    "    with torch.no_grad():\n",
+    "        outputs = inference_model(**batch)\n",
+    "    predictions = outputs.logits.argmax(dim=-1)\n",
+    "    predictions, references = predictions, batch[\"labels\"]\n",
+    "    metric.add_batch(\n",
+    "        predictions=predictions,\n",
+    "        references=references,\n",
+    "    )\n",
+    "\n",
+    "eval_metric = metric.compute()\n",
+    "print(eval_metric)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27c43da1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5 (v3.10.5:f377153967, Jun  6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

P_Tuning.ipynb

@@ -0,0 +1,685 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a825ba6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "===================================BUG REPORT===================================\n",
+      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
+      "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
+      "================================================================================\n",
+      "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
+      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
+      "CUDA SETUP: Detected CUDA version 117\n",
+      "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
+    "import os\n",
+    "\n",
+    "import torch\n",
+    "from torch.optim import AdamW\n",
+    "from torch.utils.data import DataLoader\n",
+    "from peft import (\n",
+    "    get_peft_config,\n",
+    "    get_peft_model,\n",
+    "    get_peft_model_state_dict,\n",
+    "    set_peft_model_state_dict,\n",
+    "    PeftType,\n",
+    "    PrefixTuningConfig,\n",
+    "    PromptEncoderConfig,\n",
+    ")\n",
+    "\n",
+    "import evaluate\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2bd7cbb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 32\n",
+    "model_name_or_path = \"roberta-large\"\n",
+    "task = \"mrpc\"\n",
+    "peft_type = PeftType.P_TUNING\n",
+    "device = \"cuda\"\n",
+    "num_epochs = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "33d9b62e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peft_config = PromptEncoderConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=20, encoder_hidden_size=128)\n",
+    "lr = 1e-3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "152b6177",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset glue (/home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a451b90675e0451489cc6426465afa32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-9fa7887f9eaa03ae.arrow\n",
+      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-dc593149bbeafe80.arrow\n",
+      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-140ebe5b70e09817.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
+    "    padding_side = \"left\"\n",
+    "else:\n",
+    "    padding_side = \"right\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
+    "if getattr(tokenizer, \"pad_token_id\") is None:\n",
+    "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
+    "\n",
+    "datasets = load_dataset(\"glue\", task)\n",
+    "metric = evaluate.load(\"glue\", task)\n",
+    "\n",
+    "\n",
+    "def tokenize_function(examples):\n",
+    "    # max_length=None => use the model max length (it's actually the default)\n",
+    "    outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
+    "    return outputs\n",
+    "\n",
+    "\n",
+    "tokenized_datasets = datasets.map(\n",
+    "    tokenize_function,\n",
+    "    batched=True,\n",
+    "    remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
+    ")\n",
+    "\n",
+    "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
+    "# transformers library\n",
+    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
+    "\n",
+    "\n",
+    "def collate_fn(examples):\n",
+    "    return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
+    "\n",
+    "\n",
+    "# Instantiate dataloaders.\n",
+    "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
+    "eval_dataloader = DataLoader(\n",
+    "    tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6bc8144",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
+    "model = get_peft_model(model, peft_config)\n",
+    "model.print_trainable_parameters()\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "af41c571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
+    "\n",
+    "# Instantiate scheduler\n",
+    "lr_scheduler = get_linear_schedule_with_warmup(\n",
+    "    optimizer=optimizer,\n",
+    "    num_warmup_steps=0,  # 0.06*(len(train_dataloader) * num_epochs),\n",
+    "    num_training_steps=(len(train_dataloader) * num_epochs),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "90993c93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                  | 0/115 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:32<00:00,  3.54it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 0: {'accuracy': 0.6985294117647058, 'f1': 0.8172362555720655}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.61it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 1: {'accuracy': 0.6936274509803921, 'f1': 0.806201550387597}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.61it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 2: {'accuracy': 0.7132352941176471, 'f1': 0.8224582701062216}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.61it/s]\n",
+      "100%|████████████████████████████��█████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 3: {'accuracy': 0.7083333333333334, 'f1': 0.8199697428139183}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.61it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 4: {'accuracy': 0.7205882352941176, 'f1': 0.8246153846153846}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.62it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 5: {'accuracy': 0.7009803921568627, 'f1': 0.8200589970501474}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:32<00:00,  3.59it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 6: {'accuracy': 0.7254901960784313, 'f1': 0.8292682926829268}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.60it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 7: {'accuracy': 0.7230392156862745, 'f1': 0.8269525267993874}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:34<00:00,  3.34it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 8: {'accuracy': 0.7254901960784313, 'f1': 0.8297872340425533}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████��███████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.60it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 9: {'accuracy': 0.7230392156862745, 'f1': 0.828006088280061}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:32<00:00,  3.58it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 10: {'accuracy': 0.7181372549019608, 'f1': 0.8183254344391785}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.60it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 11: {'accuracy': 0.7132352941176471, 'f1': 0.803361344537815}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.59it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.85it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 12: {'accuracy': 0.7107843137254902, 'f1': 0.8206686930091186}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:32<00:00,  3.59it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.85it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 13: {'accuracy': 0.7181372549019608, 'f1': 0.8254931714719272}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:32<00:00,  3.59it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 14: {'accuracy': 0.7156862745098039, 'f1': 0.8253012048192772}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:32<00:00,  3.59it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 15: {'accuracy': 0.7230392156862745, 'f1': 0.8242612752721618}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:32<00:00,  3.49it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:02<00:00,  5.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 16: {'accuracy': 0.7181372549019608, 'f1': 0.8200312989045383}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:32<00:00,  3.49it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 17: {'accuracy': 0.7107843137254902, 'f1': 0.8217522658610272}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.60it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 18: {'accuracy': 0.7254901960784313, 'f1': 0.8292682926829268}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.61it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  6.89it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 19: {'accuracy': 0.7107843137254902, 'f1': 0.8206686930091186}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.to(device)\n",
+    "for epoch in range(num_epochs):\n",
+    "    model.train()\n",
+    "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
+    "        batch.to(device)\n",
+    "        outputs = model(**batch)\n",
+    "        loss = outputs.loss\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        lr_scheduler.step()\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "    model.eval()\n",
+    "    for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+    "        batch.to(device)\n",
+    "        with torch.no_grad():\n",
+    "            outputs = model(**batch)\n",
+    "        predictions = outputs.logits.argmax(dim=-1)\n",
+    "        predictions, references = predictions, batch[\"labels\"]\n",
+    "        metric.add_batch(\n",
+    "            predictions=predictions,\n",
+    "            references=references,\n",
+    "        )\n",
+    "\n",
+    "    eval_metric = metric.compute()\n",
+    "    print(f\"epoch {epoch}:\", eval_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a43bd9fb",
+   "metadata": {},
+   "source": [
+    "## Share adapters on the 🤗 Hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "871b75aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/smangrul/roberta-large-peft-p-tuning/commit/fa7abe613f498c76df5e16c85d9c19c3019587a7', commit_message='Upload model', commit_description='', oid='fa7abe613f498c76df5e16c85d9c19c3019587a7', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.push_to_hub(\"smangrul/roberta-large-peft-p-tuning\", use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c6a9036",
+   "metadata": {},
+   "source": [
+    "## Load adapters from the Hub\n",
+    "\n",
+    "You can also directly load adapters from the Hub using the commands below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "91b0b8f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.layer_norm.weight', 'roberta.pooler.dense.bias', 'lm_head.dense.weight', 'lm_head.bias']\n",
+      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e650799d58ec4bd1b21b6bc28ddf2069",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/4.29M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                   | 0/13 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  7.18it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.7107843137254902, 'f1': 0.8206686930091186}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from peft import PeftModel, PeftConfig\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "\n",
+    "peft_model_id = \"smangrul/roberta-large-peft-p-tuning\"\n",
+    "config = PeftConfig.from_pretrained(peft_model_id)\n",
+    "inference_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+    "\n",
+    "# Load the Lora model\n",
+    "inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
+    "\n",
+    "inference_model.to(device)\n",
+    "inference_model.eval()\n",
+    "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+    "    batch.to(device)\n",
+    "    with torch.no_grad():\n",
+    "        outputs = inference_model(**batch)\n",
+    "    predictions = outputs.logits.argmax(dim=-1)\n",
+    "    predictions, references = predictions, batch[\"labels\"]\n",
+    "    metric.add_batch(\n",
+    "        predictions=predictions,\n",
+    "        references=references,\n",
+    "    )\n",
+    "\n",
+    "eval_metric = metric.compute()\n",
+    "print(eval_metric)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a8d69d1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5 (v3.10.5:f377153967, Jun  6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Prompt_Tuning.ipynb

@@ -0,0 +1,692 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9ff5004e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "===================================BUG REPORT===================================\n",
+      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
+      "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
+      "================================================================================\n",
+      "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
+      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
+      "CUDA SETUP: Detected CUDA version 117\n",
+      "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
+    "import os\n",
+    "\n",
+    "import torch\n",
+    "from torch.optim import AdamW\n",
+    "from torch.utils.data import DataLoader\n",
+    "from peft import (\n",
+    "    get_peft_config,\n",
+    "    get_peft_model,\n",
+    "    get_peft_model_state_dict,\n",
+    "    set_peft_model_state_dict,\n",
+    "    PeftType,\n",
+    "    PrefixTuningConfig,\n",
+    "    PromptEncoderConfig,\n",
+    "    PromptTuningConfig,\n",
+    ")\n",
+    "\n",
+    "import evaluate\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e32c4a9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 32\n",
+    "model_name_or_path = \"roberta-large\"\n",
+    "task = \"mrpc\"\n",
+    "peft_type = PeftType.PROMPT_TUNING\n",
+    "device = \"cuda\"\n",
+    "num_epochs = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "622fe9c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peft_config = PromptTuningConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=10)\n",
+    "lr = 1e-3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "74e9efe0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset glue (/home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "76198cec552441818ff107910275e5be",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-9fa7887f9eaa03ae.arrow\n",
+      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-dc593149bbeafe80.arrow\n",
+      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-140ebe5b70e09817.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
+    "    padding_side = \"left\"\n",
+    "else:\n",
+    "    padding_side = \"right\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
+    "if getattr(tokenizer, \"pad_token_id\") is None:\n",
+    "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
+    "\n",
+    "datasets = load_dataset(\"glue\", task)\n",
+    "metric = evaluate.load(\"glue\", task)\n",
+    "\n",
+    "\n",
+    "def tokenize_function(examples):\n",
+    "    # max_length=None => use the model max length (it's actually the default)\n",
+    "    outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
+    "    return outputs\n",
+    "\n",
+    "\n",
+    "tokenized_datasets = datasets.map(\n",
+    "    tokenize_function,\n",
+    "    batched=True,\n",
+    "    remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
+    ")\n",
+    "\n",
+    "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
+    "# transformers library\n",
+    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
+    "\n",
+    "\n",
+    "def collate_fn(examples):\n",
+    "    return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
+    "\n",
+    "\n",
+    "# Instantiate dataloaders.\n",
+    "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
+    "eval_dataloader = DataLoader(\n",
+    "    tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3c15af0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
+    "model = get_peft_model(model, peft_config)\n",
+    "model.print_trainable_parameters()\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6d3c5edb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
+    "\n",
+    "# Instantiate scheduler\n",
+    "lr_scheduler = get_linear_schedule_with_warmup(\n",
+    "    optimizer=optimizer,\n",
+    "    num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
+    "    num_training_steps=(len(train_dataloader) * num_epochs),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4d279225",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                  | 0/115 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [02:09<00:00,  1.13s/it]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:08<00:00,  1.62it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 0: {'accuracy': 0.678921568627451, 'f1': 0.7956318252730109}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:50<00:00,  1.04it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 1: {'accuracy': 0.696078431372549, 'f1': 0.8171091445427728}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:36<00:00,  1.19it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:06<00:00,  2.00it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 2: {'accuracy': 0.6985294117647058, 'f1': 0.8161434977578476}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:37<00:00,  1.18it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:06<00:00,  2.09it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 3: {'accuracy': 0.7058823529411765, 'f1': 0.7979797979797979}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [02:03<00:00,  1.07s/it]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:07<00:00,  1.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 4: {'accuracy': 0.696078431372549, 'f1': 0.8132530120481929}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:53<00:00,  1.01it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.19it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 5: {'accuracy': 0.7107843137254902, 'f1': 0.8121019108280254}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:35<00:00,  1.20it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.20it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 6: {'accuracy': 0.6911764705882353, 'f1': 0.7692307692307693}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:36<00:00,  1.20it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 7: {'accuracy': 0.7156862745098039, 'f1': 0.8209876543209876}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:35<00:00,  1.20it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 8: {'accuracy': 0.7205882352941176, 'f1': 0.8240740740740742}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████���█████████████████████████████████████████████████████| 115/115 [01:36<00:00,  1.19it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 9: {'accuracy': 0.7205882352941176, 'f1': 0.8229813664596273}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:36<00:00,  1.20it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 10: {'accuracy': 0.7156862745098039, 'f1': 0.8164556962025317}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:35<00:00,  1.20it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 11: {'accuracy': 0.7058823529411765, 'f1': 0.8113207547169811}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:32<00:00,  1.24it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 12: {'accuracy': 0.7009803921568627, 'f1': 0.7946127946127945}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:32<00:00,  1.24it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.38it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 13: {'accuracy': 0.7230392156862745, 'f1': 0.8186195826645265}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:29<00:00,  1.29it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.31it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 14: {'accuracy': 0.7058823529411765, 'f1': 0.8130841121495327}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:30<00:00,  1.27it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 15: {'accuracy': 0.7181372549019608, 'f1': 0.8194662480376768}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:28<00:00,  1.29it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 16: {'accuracy': 0.7254901960784313, 'f1': 0.8181818181818181}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:30<00:00,  1.27it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.30it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 17: {'accuracy': 0.7205882352941176, 'f1': 0.820754716981132}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:30<00:00,  1.27it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 18: {'accuracy': 0.7254901960784313, 'f1': 0.821656050955414}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:28<00:00,  1.29it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.43it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 19: {'accuracy': 0.7303921568627451, 'f1': 0.8242811501597445}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.to(device)\n",
+    "for epoch in range(num_epochs):\n",
+    "    model.train()\n",
+    "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
+    "        batch.to(device)\n",
+    "        outputs = model(**batch)\n",
+    "        loss = outputs.loss\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        lr_scheduler.step()\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "    model.eval()\n",
+    "    for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+    "        batch.to(device)\n",
+    "        with torch.no_grad():\n",
+    "            outputs = model(**batch)\n",
+    "        predictions = outputs.logits.argmax(dim=-1)\n",
+    "        predictions, references = predictions, batch[\"labels\"]\n",
+    "        metric.add_batch(\n",
+    "            predictions=predictions,\n",
+    "            references=references,\n",
+    "        )\n",
+    "\n",
+    "    eval_metric = metric.compute()\n",
+    "    print(f\"epoch {epoch}:\", eval_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1ff3f44",
+   "metadata": {},
+   "source": [
+    "## Share adapters on the 🤗 Hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0bf79cb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/smangrul/roberta-large-peft-prompt-tuning/commit/893a909d8499aa8778d58c781d43c3a8d9360de8', commit_message='Upload model', commit_description='', oid='893a909d8499aa8778d58c781d43c3a8d9360de8', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.push_to_hub(\"smangrul/roberta-large-peft-prompt-tuning\", use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73870ad7",
+   "metadata": {},
+   "source": [
+    "## Load adapters from the Hub\n",
+    "\n",
+    "You can also directly load adapters from the Hub using the commands below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0654a552",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24581bb98582444ca6114b9fa267847f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/368 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.dense.bias']\n",
+      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1584da4d1c54cc3873a515182674980",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/4.25M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                   | 0/13 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.58it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.7303921568627451, 'f1': 0.8242811501597445}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from peft import PeftModel, PeftConfig\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "\n",
+    "peft_model_id = \"smangrul/roberta-large-peft-prompt-tuning\"\n",
+    "config = PeftConfig.from_pretrained(peft_model_id)\n",
+    "inference_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+    "\n",
+    "# Load the Lora model\n",
+    "inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
+    "\n",
+    "inference_model.to(device)\n",
+    "inference_model.eval()\n",
+    "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+    "    batch.to(device)\n",
+    "    with torch.no_grad():\n",
+    "        outputs = inference_model(**batch)\n",
+    "    predictions = outputs.logits.argmax(dim=-1)\n",
+    "    predictions, references = predictions, batch[\"labels\"]\n",
+    "    metric.add_batch(\n",
+    "        predictions=predictions,\n",
+    "        references=references,\n",
+    "    )\n",
+    "\n",
+    "eval_metric = metric.compute()\n",
+    "print(eval_metric)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

prefix_tuning.ipynb

@@ -0,0 +1,710 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a825ba6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "===================================BUG REPORT===================================\n",
+      "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
+      "For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link\n",
+      "================================================================================\n",
+      "CUDA SETUP: CUDA runtime path found: /home/sourab/miniconda3/envs/ml/lib/libcudart.so\n",
+      "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
+      "CUDA SETUP: Detected CUDA version 117\n",
+      "CUDA SETUP: Loading binary /home/sourab/miniconda3/envs/ml/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda117.so...\n"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
+    "import os\n",
+    "\n",
+    "import torch\n",
+    "from torch.optim import AdamW\n",
+    "from torch.utils.data import DataLoader\n",
+    "from peft import (\n",
+    "    get_peft_config,\n",
+    "    get_peft_model,\n",
+    "    get_peft_model_state_dict,\n",
+    "    set_peft_model_state_dict,\n",
+    "    PeftType,\n",
+    "    PrefixTuningConfig,\n",
+    "    PromptEncoderConfig,\n",
+    ")\n",
+    "\n",
+    "import evaluate\n",
+    "from datasets import load_dataset\n",
+    "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2bd7cbb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 32\n",
+    "model_name_or_path = \"roberta-large\"\n",
+    "task = \"mrpc\"\n",
+    "peft_type = PeftType.PREFIX_TUNING\n",
+    "device = \"cuda\"\n",
+    "num_epochs = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "33d9b62e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peft_config = PrefixTuningConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=20)\n",
+    "lr = 1e-2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "152b6177",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset glue (/home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be1eddbb9a7d4e6dae32fd026e167f96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-9fa7887f9eaa03ae.arrow\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b61574844b6c499b8960fd4d78c5e549",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-7e7eacaa5160936d.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
+    "    padding_side = \"left\"\n",
+    "else:\n",
+    "    padding_side = \"right\"\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
+    "if getattr(tokenizer, \"pad_token_id\") is None:\n",
+    "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
+    "\n",
+    "datasets = load_dataset(\"glue\", task)\n",
+    "metric = evaluate.load(\"glue\", task)\n",
+    "\n",
+    "\n",
+    "def tokenize_function(examples):\n",
+    "    # max_length=None => use the model max length (it's actually the default)\n",
+    "    outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
+    "    return outputs\n",
+    "\n",
+    "\n",
+    "tokenized_datasets = datasets.map(\n",
+    "    tokenize_function,\n",
+    "    batched=True,\n",
+    "    remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
+    ")\n",
+    "\n",
+    "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
+    "# transformers library\n",
+    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
+    "\n",
+    "\n",
+    "def collate_fn(examples):\n",
+    "    return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
+    "\n",
+    "\n",
+    "# Instantiate dataloaders.\n",
+    "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
+    "eval_dataloader = DataLoader(\n",
+    "    tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6bc8144",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
+    "model = get_peft_model(model, peft_config)\n",
+    "model.print_trainable_parameters()\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "af41c571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
+    "\n",
+    "# Instantiate scheduler\n",
+    "lr_scheduler = get_linear_schedule_with_warmup(\n",
+    "    optimizer=optimizer,\n",
+    "    num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
+    "    num_training_steps=(len(train_dataloader) * num_epochs),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "90993c93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                  | 0/115 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:29<00:00,  3.87it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 0: {'accuracy': 0.7132352941176471, 'f1': 0.7876588021778584}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:26<00:00,  4.42it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 1: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:26<00:00,  4.41it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 2: {'accuracy': 0.8088235294117647, 'f1': 0.8717105263157895}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:26<00:00,  4.39it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.34it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 3: {'accuracy': 0.7549019607843137, 'f1': 0.8475609756097561}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:26<00:00,  4.37it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:01<00:00,  8.34it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 4: {'accuracy': 0.8480392156862745, 'f1': 0.8938356164383561}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:40<00:00,  2.87it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:06<00:00,  1.93it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 5: {'accuracy': 0.8651960784313726, 'f1': 0.9053356282271946}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:53<00:00,  1.01it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:07<00:00,  1.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 6: {'accuracy': 0.8700980392156863, 'f1': 0.9065255731922399}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:42<00:00,  1.12it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 7: {'accuracy': 0.8676470588235294, 'f1': 0.9042553191489361}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:27<00:00,  1.31it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.45it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 8: {'accuracy': 0.875, 'f1': 0.9103690685413005}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:29<00:00,  1.29it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 9: {'accuracy': 0.8799019607843137, 'f1': 0.913884007029877}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:43<00:00,  1.11it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:06<00:00,  1.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 10: {'accuracy': 0.8725490196078431, 'f1': 0.902621722846442}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:53<00:00,  1.02it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:06<00:00,  2.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 11: {'accuracy': 0.875, 'f1': 0.9090909090909091}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:29<00:00,  1.28it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:04<00:00,  2.65it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 12: {'accuracy': 0.8823529411764706, 'f1': 0.9139784946236559}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:27<00:00,  1.31it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.46it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 13: {'accuracy': 0.8602941176470589, 'f1': 0.9018932874354562}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:27<00:00,  1.31it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████��█████████████████████| 13/13 [00:05<00:00,  2.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 14: {'accuracy': 0.8700980392156863, 'f1': 0.9075043630017452}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:27<00:00,  1.31it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 15: {'accuracy': 0.875, 'f1': 0.9087656529516995}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:27<00:00,  1.32it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 16: {'accuracy': 0.8578431372549019, 'f1': 0.9003436426116839}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:27<00:00,  1.31it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 17: {'accuracy': 0.8627450980392157, 'f1': 0.903448275862069}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:28<00:00,  1.31it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:04<00:00,  2.65it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 18: {'accuracy': 0.8700980392156863, 'f1': 0.9078260869565218}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████████████████████████████████████████████████████████| 115/115 [01:27<00:00,  1.32it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:05<00:00,  2.45it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "epoch 19: {'accuracy': 0.8774509803921569, 'f1': 0.9125874125874125}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.to(device)\n",
+    "for epoch in range(num_epochs):\n",
+    "    model.train()\n",
+    "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
+    "        batch.to(device)\n",
+    "        outputs = model(**batch)\n",
+    "        loss = outputs.loss\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        lr_scheduler.step()\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "    model.eval()\n",
+    "    for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+    "        batch.to(device)\n",
+    "        with torch.no_grad():\n",
+    "            outputs = model(**batch)\n",
+    "        predictions = outputs.logits.argmax(dim=-1)\n",
+    "        predictions, references = predictions, batch[\"labels\"]\n",
+    "        metric.add_batch(\n",
+    "            predictions=predictions,\n",
+    "            references=references,\n",
+    "        )\n",
+    "\n",
+    "    eval_metric = metric.compute()\n",
+    "    print(f\"epoch {epoch}:\", eval_metric)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7734299c",
+   "metadata": {},
+   "source": [
+    "## Share adapters on the 🤗 Hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "afaf42dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/smangrul/roberta-large-peft-prefix-tuning/commit/a00e05a4c9a68e700221784f8e073c2e194637c3', commit_message='Upload model', commit_description='', oid='a00e05a4c9a68e700221784f8e073c2e194637c3', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.push_to_hub(\"smangrul/roberta-large-peft-prefix-tuning\", use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42b20e77",
+   "metadata": {},
+   "source": [
+    "## Load adapters from the Hub\n",
+    "\n",
+    "You can also directly load adapters from the Hub using the commands below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "868e7580",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ce57b4de8ae4f868115733abc2fb883",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/373 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'roberta.pooler.dense.weight', 'lm_head.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias']\n",
+      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ace158c926a44b31a9b0ea80411bd7a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/8.14M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                                                                   | 0/13 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 13/13 [00:06<00:00,  2.04it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'accuracy': 0.8774509803921569, 'f1': 0.9125874125874125}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from peft import PeftModel, PeftConfig\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "\n",
+    "peft_model_id = \"smangrul/roberta-large-peft-prefix-tuning\"\n",
+    "config = PeftConfig.from_pretrained(peft_model_id)\n",
+    "inference_model = AutoModelForSequenceClassification.from_pretrained(config.base_model_name_or_path)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+    "\n",
+    "# Load the Lora model\n",
+    "inference_model = PeftModel.from_pretrained(inference_model, peft_model_id)\n",
+    "\n",
+    "inference_model.to(device)\n",
+    "inference_model.eval()\n",
+    "for step, batch in enumerate(tqdm(eval_dataloader)):\n",
+    "    batch.to(device)\n",
+    "    with torch.no_grad():\n",
+    "        outputs = inference_model(**batch)\n",
+    "    predictions = outputs.logits.argmax(dim=-1)\n",
+    "    predictions, references = predictions, batch[\"labels\"]\n",
+    "    metric.add_batch(\n",
+    "        predictions=predictions,\n",
+    "        references=references,\n",
+    "    )\n",
+    "\n",
+    "eval_metric = metric.compute()\n",
+    "print(eval_metric)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5 (v3.10.5:f377153967, Jun  6 2022, 12:36:10) [Clang 13.0.0 (clang-1300.0.29.30)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}