I am training my LoRA model for fine tunning llms task, while training i got the following error, i had already add the adapters and LoRAConfig by following the suggest page:Load adapters with 🤗 PEFT , but still got the problem, always facing the same issue, can anyone
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training
import transformers
LORA_R = 256 # 512
LORA_ALPHA = 512 # 1024
LORA_DROPOUT = 0.05
# Define LoRA Config
peft_config = LoraConfig(
r = LORA_R, # the dimension of the low-rank matrices
lora_alpha = LORA_ALPHA, # scaling factor for the weight matrices
lora_dropout = LORA_DROPOUT, # dropout probability of the LoRA layers
bias="none",
task_type="CAUSAL_LM",
target_modules=["query_key_value"]
)
# Prepare int-8 model for training - utility function that prepares a PyTorch model for int8 quantization training. <https://huggingface.co/docs/peft/task_guides/int8-asr>
model2 = prepare_model_for_int8_training(model)
# initialize the model with the LoRA framework
# model3 = get_peft_model(model2, lora_config)
# model.print_trainable_parameters()
model2.add_adapter(peft_config = peft_config, adapter_name = "adapter_1")
# training the model
trainer = transformers.Trainer(
model=model2,
tokenizer=tokenizer,
args=training_args,
train_dataset=split_dataset['train'],
eval_dataset=split_dataset["test"],
data_collator=data_collator,
)
model.config.use_cache = False # silence the warnings. Please re-enable for inference!
trainer.train()
The error is:
I just don’t understand, i had already add my adapters, why does it always showing that i am performing on purely quantized models that need to attach adapters??
