After fine tuning, saving and reloading the model, he is "forgetting" fine tuning
Hello everyone,I’m a beginner in AI. I’m trying to fine tune the “bofenghuang/vigogne-2-7b-instruct” model to accomplish one specific task. I have a dataset of 30k examples. I fine tune the model using thé LoRA technique and the Stanford Alpaca prompt pattern.I use the following code to fine tune it :from datasets import load_dataset
from google.colab import drive

drive.mount("./drive")
train_dataset = load_dataset("csv", data_files="./drive/MyDrive/DATA/train3.csv")

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer

model_name = "bofenghuang/vigogne-2-7b-instruct"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    trust_remote_code=True
)
model.config.use_cache = False

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

from peft import LoraConfig, get_peft_model

lora_alpha = 16
lora_dropout = 0.1
lora_r = 64

peft_config = LoraConfig(
    lora_alpha=lora_alpha,
    lora_dropout=lora_dropout,
    r=lora_r,
    bias="none",
    task_type="CAUSAL_LM"
)

from transformers import TrainingArguments

output_dir = "results"
per_device_train_batch_size = 4
gradient_accumulation_steps = 4
optim = "paged_adamw_32bit"
save_steps = 100
logging_steps = 10
learning_rate = 2e-4
max_grad_norm = 0.3
max_steps = 500
warmup_ratio = 0.03
lr_scheduler_type = "constant"

training_arguments = TrainingArguments(
    output_dir=output_dir,
    per_device_train_batch_size=per_device_train_batch_size,
    gradient_accumulation_steps=gradient_accumulation_steps,
    optim=optim,
    save_steps=save_steps,
    logging_steps=logging_steps,
    learning_rate=learning_rate,
    fp16=True,
    max_grad_norm=max_grad_norm,
    max_steps=max_steps,
    warmup_ratio=warmup_ratio,
    group_by_length=True,
    lr_scheduler_type=lr_scheduler_type,
)

from trl import SFTTrainer

max_seq_length = 512

trainer = SFTTrainer(
    model=model,
    train_dataset=train_dataset["train"],
    peft_config=peft_config,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    tokenizer=tokenizer,
    args=training_arguments,
)

for name, module in trainer.model.named_modules():
    if "norm" in name:
        module = module.to(torch.float32)

trainer.train()

model_to_save = trainer.model.module if hasattr(trainer.model, 'module') else trainer.model  # Take care of distributed/parallel training
model_to_save.save_pretrained("outputs")After that it accomplish the wanted task perfectly. But when I restart the colab runtime and load the model with thé produced adapter using the following code :lora_config = LoraConfig.from_pretrained('results/checkpoint-500')
model = get_peft_model(model, lora_config)The model is completly failling, it just remember a little bit, but in somme case it forget everything.Do you know why this is happening.Thanks you.