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chore: import upstream snapshot with attribution
2026-07-13 13:24:42 +08:00

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{
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"id": "a9935ae2",
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},
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"source": [
"import argparse\n",
"import os\n",
"\n",
"import torch\n",
"from torch.optim import AdamW\n",
"from torch.utils.data import DataLoader\n",
"import peft\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": null,
"id": "e3b13308",
"metadata": {
"id": "e3b13308"
},
"outputs": [],
"source": [
"batch_size = 8\n",
"model_name_or_path = \"roberta-large\"\n",
"task = \"mrpc\"\n",
"peft_type = peft.PeftType.IA3\n",
"device = torch.accelerator.current_accelerator().type if hasattr(torch, \"accelerator\") else \"cuda\"\n",
"num_epochs = 12"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0526f571",
"metadata": {
"id": "0526f571"
},
"outputs": [],
"source": [
"# peft_config = LoraConfig(task_type=\"SEQ_CLS\", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1)\n",
"peft_config = peft.IA3Config(task_type=\"SEQ_CLS\", inference_mode=False)\n",
"lr = 1e-3"
]
},
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"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",
")\n",
"test_dataloader = DataLoader(tokenized_datasets[\"test\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size)"
]
},
{
"cell_type": "code",
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"id": "2ed5ac74",
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{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0cecb897c86c4892b94a1990ab08a926",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading model.safetensors: 0%| | 0.00/1.42G [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"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.bias', '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.weight', 'classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 2,275,332 || all params: 356,585,476 || trainable%: 0.6380888042675075\n"
]
},
{
"data": {
"text/plain": [
"PeftModelForSequenceClassification(\n",
" (base_model): IA3Model(\n",
" (model): RobertaForSequenceClassification(\n",
" (roberta): RobertaModel(\n",
" (embeddings): RobertaEmbeddings(\n",
" (word_embeddings): Embedding(50265, 1024, padding_idx=1)\n",
" (position_embeddings): Embedding(514, 1024, padding_idx=1)\n",
" (token_type_embeddings): Embedding(1, 1024)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): RobertaEncoder(\n",
" (layer): ModuleList(\n",
" (0-23): 24 x RobertaLayer(\n",
" (attention): RobertaAttention(\n",
" (self): RobertaSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(\n",
" in_features=1024, out_features=1024, bias=True\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (value): Linear(\n",
" in_features=1024, out_features=1024, bias=True\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1024x1])\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): RobertaSelfOutput(\n",
" (dense): Linear(\n",
" in_features=1024, out_features=1024, bias=True\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1x1024])\n",
" )\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): RobertaIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): RobertaOutput(\n",
" (dense): Linear(\n",
" in_features=4096, out_features=1024, bias=True\n",
" (ia3_l): ParameterDict( (default): Parameter containing: [torch.FloatTensor of size 1x4096])\n",
" )\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (classifier): ModulesToSaveWrapper(\n",
" (original_module): RobertaClassificationHead(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (out_proj): Linear(in_features=1024, out_features=2, bias=True)\n",
" )\n",
" (modules_to_save): ModuleDict(\n",
" (default): RobertaClassificationHead(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (out_proj): Linear(in_features=1024, out_features=2, bias=True)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
")"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
"model = peft.get_peft_model(model, peft_config)\n",
"model.print_trainable_parameters()\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0d2d0381",
"metadata": {
"id": "0d2d0381"
},
"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": 8,
"id": "fa0e73be",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fa0e73be",
"outputId": "bb17c146-8acc-477d-8f9f-65b8be794abb"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/459 [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%|██████████| 459/459 [01:41<00:00, 4.52it/s]\n",
"100%|██████████| 51/51 [00:05<00:00, 8.89it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 0: {'accuracy': 0.7034313725490197, 'f1': 0.8212703101920238}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:45<00:00, 4.35it/s]\n",
"100%|██████████| 51/51 [00:05<00:00, 8.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 1: {'accuracy': 0.7794117647058824, 'f1': 0.8432055749128919}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:47<00:00, 4.26it/s]\n",
"100%|██████████| 51/51 [00:05<00:00, 8.50it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 2: {'accuracy': 0.8406862745098039, 'f1': 0.8794063079777366}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:49<00:00, 4.20it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 8.45it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 3: {'accuracy': 0.8480392156862745, 'f1': 0.8923611111111109}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:49<00:00, 4.18it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 7.52it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 4: {'accuracy': 0.8529411764705882, 'f1': 0.894736842105263}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:49<00:00, 4.19it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 8.47it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 5: {'accuracy': 0.8700980392156863, 'f1': 0.9090909090909091}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:49<00:00, 4.20it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 8.41it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 6: {'accuracy': 0.875, 'f1': 0.9090909090909091}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:49<00:00, 4.21it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 8.41it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 7: {'accuracy': 0.8676470588235294, 'f1': 0.9042553191489361}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:50<00:00, 4.17it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 8.45it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 8: {'accuracy': 0.8700980392156863, 'f1': 0.9068541300527241}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:49<00:00, 4.20it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 8.39it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 9: {'accuracy': 0.8676470588235294, 'f1': 0.9028776978417268}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:49<00:00, 4.20it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 8.39it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 10: {'accuracy': 0.875, 'f1': 0.9100529100529101}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 459/459 [01:49<00:00, 4.18it/s]\n",
"100%|██████████| 51/51 [00:06<00:00, 8.41it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 11: {'accuracy': 0.8651960784313726, 'f1': 0.9012567324955117}\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": {
"id": "f2b2caca"
},
"source": [
"## Share adapters on the 🤗 Hub"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "990b3c93",
"metadata": {
"colab": {
"referenced_widgets": [
"ad8979af959541b2a5a67f389884f057",
"1c020005cfe04de6bdd404ef41e0dfef"
]
},
"id": "990b3c93",
"outputId": "0e2f13c2-0285-4f47-ab9a-d3aefea7ee05"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ad8979af959541b2a5a67f389884f057",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1c020005cfe04de6bdd404ef41e0dfef",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"adapter_model.bin: 0%| | 0.00/4.93M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/SumanthRH/roberta-large-peft-ia3/commit/9968de770e53ecd24e8e689a10144b7f55059a75', commit_message='Upload model', commit_description='', oid='9968de770e53ecd24e8e689a10144b7f55059a75', pr_url='https://huggingface.co/SumanthRH/roberta-large-peft-ia3/discussions/3', pr_revision='refs/pr/3', pr_num=3)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.push_to_hub(\"SumanthRH/roberta-large-peft-ia3\", use_auth_token=True)"
]
},
{
"cell_type": "markdown",
"id": "9d140b26",
"metadata": {
"id": "9d140b26"
},
"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": null,
"id": "4d55c87d",
"metadata": {
"colab": {
"referenced_widgets": [
"fd320d7b5d9a43df8abf044897e25cc7",
"0022338db24d43bba27d0e72773855e0"
]
},
"id": "4d55c87d",
"outputId": "e6e232d7-22a3-4c85-c72e-90d8275c9762"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd320d7b5d9a43df8abf044897e25cc7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading (…)/adapter_config.json: 0%| | 0.00/345 [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.decoder.weight', 'roberta.pooler.dense.bias', 'roberta.pooler.dense.weight', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', '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.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', '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"
]
},
{
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"application/vnd.jupyter.widget-view+json": {
"model_id": "0022338db24d43bba27d0e72773855e0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading adapter_model.bin: 0%| | 0.00/4.93M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/51 [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%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 51/51 [00:04<00:00, 10.57it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'accuracy': 0.8774509803921569, 'f1': 0.9116607773851589}\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 = \"SumanthRH/roberta-large-peft-ia3\"\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": {
"id": "27c43da1"
},
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}
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