addressing comments and renaming pet to peft

examples/sequence_classification/LoRA.ipynb

@@ -13,7 +13,7 @@
     "import torch\n",
     "from torch.optim import AdamW\n",
     "from torch.utils.data import DataLoader\n",
-    "from pet import get_pet_config,get_pet_model, get_pet_model_state_dict, set_pet_model_state_dict, LoRAConfig, PETType, \\\n",
+    "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, LoraConfig, PeftType, \\\n",
     "PrefixTuningConfig, PromptEncoderConfig\n",
     "\n",
     "import evaluate\n",
@@ -32,7 +32,7 @@
     "batch_size = 32\n",
     "model_name_or_path = \"roberta-large\"\n",
     "task = \"mrpc\"\n",
-    "pet_type = PETType.LORA\n",
+    "peft_type = PeftType.LORA\n",
     "device = \"cuda\"\n",
     "num_epochs = 20"
    ]
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pet_config = LoRAConfig(\n",
+    "peft_config = LoraConfig(\n",
     "    task_type=\"SEQ_CLS\",\n",
     "    inference_mode=False,\n",
     "    r=8,\n",
@@ -1007,7 +1007,7 @@
    ],
    "source": [
     "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
-    "model = get_pet_model(model, pet_config)\n",
+    "model = get_peft_model(model, peft_config)\n",
     "model.print_trainable_parameters()\n",
     "model"
    ]

examples/sequence_classification/P_Tuning.ipynb

@@ -13,7 +13,7 @@
     "import torch\n",
     "from torch.optim import AdamW\n",
     "from torch.utils.data import DataLoader\n",
-    "from pet import get_pet_config,get_pet_model, get_pet_model_state_dict, set_pet_model_state_dict, LoRAConfig, PETType, \\\n",
+    "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, PeftType, \\\n",
     "PrefixTuningConfig, PromptEncoderConfig\n",
     "\n",
     "import evaluate\n",
@@ -32,7 +32,7 @@
     "batch_size = 32\n",
     "model_name_or_path = \"roberta-large\"\n",
     "task = \"mrpc\"\n",
-    "pet_type = PETType.P_TUNING\n",
+    "peft_type = PeftType.P_TUNING\n",
     "device = \"cuda\"\n",
     "num_epochs = 30"
    ]
@@ -45,7 +45,7 @@
    "outputs": [],
    "source": [
     "\n",
-    "pet_config = PromptEncoderConfig(\n",
+    "peft_config = PromptEncoderConfig(\n",
     "    task_type=\"SEQ_CLS\",\n",
     "    num_virtual_tokens=20,\n",
     "    encoder_hidden_size=128\n",
@@ -775,7 +775,7 @@
    ],
    "source": [
     "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
-    "model = get_pet_model(model, pet_config)\n",
+    "model = get_peft_model(model, peft_config)\n",
     "model.print_trainable_parameters()\n",
     "model"
    ]

examples/sequence_classification/Prompt_Tuning.ipynb

@@ -13,7 +13,7 @@
     "import torch\n",
     "from torch.optim import AdamW\n",
     "from torch.utils.data import DataLoader\n",
-    "from pet import get_pet_config,get_pet_model, get_pet_model_state_dict, set_pet_model_state_dict, LoRAConfig, PETType, \\\n",
+    "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, LoRAConfig, PeftType, \\\n",
     "PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig\n",
     "\n",
     "import evaluate\n",
@@ -32,7 +32,7 @@
     "batch_size = 32\n",
     "model_name_or_path = \"roberta-large\"\n",
     "task = \"mrpc\"\n",
-    "pet_type = PETType.PROMPT_TUNING\n",
+    "peft_type = PeftType.PROMPT_TUNING\n",
     "device = \"cuda\"\n",
     "num_epochs = 20"
    ]
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pet_config = PromptTuningConfig(\n",
+    "peft_config = PromptTuningConfig(\n",
     "    task_type=\"SEQ_CLS\",\n",
     "    num_virtual_tokens=10\n",
     ")\n",
@@ -766,7 +766,7 @@
    ],
    "source": [
     "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
-    "model = get_pet_model(model, pet_config)\n",
+    "model = get_peft_model(model, peft_config)\n",
     "model.print_trainable_parameters()\n",
     "model"
    ]

examples/sequence_classification/prefix_tuning.ipynb

@@ -13,7 +13,7 @@
     "import torch\n",
     "from torch.optim import AdamW\n",
     "from torch.utils.data import DataLoader\n",
-    "from pet import get_pet_config,get_pet_model, get_pet_model_state_dict, set_pet_model_state_dict, LoRAConfig, PETType, \\\n",
+    "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, PeftType, \\\n",
     "PrefixTuningConfig, PromptEncoderConfig\n",
     "\n",
     "import evaluate\n",
@@ -32,7 +32,7 @@
     "batch_size = 32\n",
     "model_name_or_path = \"roberta-large\"\n",
     "task = \"mrpc\"\n",
-    "pet_type = PETType.PREFIX_TUNING\n",
+    "peft_type = PeftType.PREFIX_TUNING\n",
     "device = \"cuda\"\n",
     "num_epochs = 20"
    ]
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pet_config = PrefixTuningConfig(\n",
+    "peft_config = PrefixTuningConfig(\n",
     "    task_type=\"SEQ_CLS\",\n",
     "    num_virtual_tokens=20\n",
     ")\n",
@@ -766,7 +766,7 @@
    ],
    "source": [
     "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
-    "model = get_pet_model(model, pet_config)\n",
+    "model = get_peft_model(model, peft_config)\n",
     "model.print_trainable_parameters()\n",
     "model"
    ]