pip install -q peft transformers datasets

from datasets import load_dataset

ds = load_dataset("ought/raft", "twitter_complaints")

classes = [k.replace("_", " ") for k in ds["train"].features["Label"].names]
ds = ds.map(
    lambda x: {"text_label": [classes[label] for label in x["Label"]]},
    batched=True,
    num_proc=1,
)
ds["train"][0]
{"Tweet text": "@HMRCcustomers No this is my first job", "ID": 0, "Label": 2, "text_label": "no complaint"}

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")
if tokenizer.pad_token_id is None:
    tokenizer.pad_token_id = tokenizer.eos_token_id
target_max_length = max([len(tokenizer(class_label)["input_ids"]) for class_label in classes])
print(target_max_length)

import torch

max_length = 64

def preprocess_function(examples, text_column="Tweet text", label_column="text_label"):
    batch_size = len(examples[text_column])
    inputs = [f"{text_column} : {x} Label : " for x in examples[text_column]]
    targets = [str(x) for x in examples[label_column]]
    model_inputs = tokenizer(inputs)
    labels = tokenizer(targets)
    classes = [k.replace("_", " ") for k in ds["train"].features["Label"].names]
    for i in range(batch_size):
        sample_input_ids = model_inputs["input_ids"][i]
        label_input_ids = labels["input_ids"][i]
        model_inputs["input_ids"][i] = [tokenizer.pad_token_id] * (
            max_length - len(sample_input_ids)
        ) + sample_input_ids
        model_inputs["attention_mask"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[
            "attention_mask"
        ][i]
        labels["input_ids"][i] = [-100] * (max_length - len(label_input_ids)) + label_input_ids
        model_inputs["input_ids"][i] = torch.tensor(model_inputs["input_ids"][i][:max_length])
        model_inputs["attention_mask"][i] = torch.tensor(model_inputs["attention_mask"][i][:max_length])
        labels["input_ids"][i] = torch.tensor(labels["input_ids"][i][:max_length])
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

processed_ds = ds.map(
    preprocess_function,
    batched=True,
    num_proc=1,
    remove_columns=ds["train"].column_names,
    load_from_cache_file=False,
    desc="Running tokenizer on dataset",
)

from torch.utils.data import DataLoader
from transformers import default_data_collator

train_ds = processed_ds["train"]
eval_ds = processed_ds["test"]

batch_size = 16

train_dataloader = DataLoader(train_ds, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)
eval_dataloader = DataLoader(eval_ds, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz-560m")

from transformers import get_linear_schedule_with_warmup

lr = 3e-2
num_epochs = 50

optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
lr_scheduler = get_linear_schedule_with_warmup(
    optimizer=optimizer,
    num_warmup_steps=0,
    num_training_steps=(len(train_dataloader) * num_epochs),
)

from tqdm import tqdm

device = "cuda"
model = model.to(device)

for epoch in range(num_epochs):
    model.train()
    total_loss = 0
    for step, batch in enumerate(tqdm(train_dataloader)):
        batch = {k: v.to(device) for k, v in batch.items()}
        outputs = model(**batch)
        loss = outputs.loss
        total_loss += loss.detach().float()
        loss.backward()
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    eval_loss = 0
    eval_preds = []
    for step, batch in enumerate(tqdm(eval_dataloader)):
        batch = {k: v.to(device) for k, v in batch.items()}
        with torch.no_grad():
            outputs = model(**batch)
        loss = outputs.loss
        eval_loss += loss.detach().float()
        eval_preds.extend(
            tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)
        )

    eval_epoch_loss = eval_loss / len(eval_dataloader)
    eval_ppl = torch.exp(eval_epoch_loss)
    train_epoch_loss = total_loss / len(train_dataloader)
    train_ppl = torch.exp(train_epoch_loss)
    print(f"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}")

from huggingface_hub import notebook_login

account = <your-hf-account-name>
peft_model_id = f"{account}/bloomz-560-m-peft-method"
model.push_to_hub(peft_model_id)

from peft import AutoPeftModelForCausalLM

model = AutoPeftModelForCausalLM.from_pretrained("peft_model_id").to("cuda")
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz-560m")

i = 15
inputs = tokenizer(f'{text_column} : {ds["test"][i]["Tweet text"]} Label : ', return_tensors="pt")
print(ds["test"][i]["Tweet text"])
"@NYTsupport i have complained a dozen times & yet my papers are still thrown FAR from my door. Why is this so hard to resolve?"

with torch.no_grad():
    inputs = {k: v.to(device) for k, v in inputs.items()}
    outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10)
    print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))
"['Tweet text : @NYTsupport i have complained a dozen times & yet my papers are still thrown FAR from my door. Why is this so hard to resolve? Label : complaint']"