How to resume training from lora checkpoint
model / tokenizer= “Mistral model”checkpoint_path = “model/checkpoint-1000”lora_r = 16lora_alpha = 64lora_dropout = 0.1lora_target_modules = [“q_proj”,“up_proj”,“o_proj”,“k_proj”,“down_proj”,“gate_proj”,“v_proj”,]peft_config = LoraConfig(
    r=lora_r,
    lora_alpha=lora_alpha,
    lora_dropout=lora_dropout,
    target_modules=lora_target_modules,
    bias="none",
    task_type="CAUSAL_LM",
)
training_arguments = TrainingArguments(
    per_device_train_batch_size=1,
    gradient_accumulation_steps=32,
    optim="paged_adamw_32bit",
    logging_steps=1,
    learning_rate=1e-4,
    fp16=True,
    max_grad_norm=0.3,
    num_train_epochs=10,
    save_steps=100,
    evaluation_strategy="steps",
    eval_steps=0.2,
    warmup_ratio=0.05,
    save_strategy="steps",
    group_by_length="steps",
    output_dir=OUTPUT_DIR,
    report_to="tensorboard",
    save_safetensors=True,
    lr_scheduler_type="cosine",
    seed=42,
)trainer = SFTTrainer(model=model,train_dataset=train_dataset[‘train’],eval_dataset=val_dataset[‘train’],peft_config=peft_config,dataset_text_field=“text”,max_seq_length=1024,tokenizer=tokenizer,args=training_arguments,)