Lora fine-tuning taking too long

Any reason why this is giving me a month of expected processing time?

More importantly, how to speed this up?

My dataset is a collection of 20k short sentences (max 100 words each).

import transformers
import torch

model_id = "tiiuae/falcon-40b-instruct"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
    )

tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto", trust_remote_code=True)

from peft import prepare_model_for_kbit_training

model.gradient_checkpointing_enable()
model = prepare_model_for_kbit_training(model)

from peft import LoraConfig, get_peft_model

config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["query_key_value"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
    )

model = get_peft_model(model, config)

trainer = transformers.Trainer(
    model=model,
    train_dataset=tokenized_data,
    args=transformers.TrainingArguments(
        num_train_epochs=100,
        per_device_train_batch_size=4,
        gradient_accumulation_steps=4,
        warmup_ratio=0.05,
        learning_rate=2e-4,
        fp16=False,
        logging_steps=1,
        output_dir="output",
        optim="paged_adamw_8bit",
        lr_scheduler_type='cosine',
    ),
    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
)
model.config.use_cache = False  # silence the warnings. Please re-enable for inference!

trainer.train()```

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# 答案1
**得分**: 1

我认为原因是因为 GPU 没有被使用，如果你有一个 GPU，你可以启用它，并使用它进行微调。

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<summary>英文:</summary>

I think the reason is because the gpu is not being used, if you have one you could enable it and do fine tuning with that.

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