Model Card for Model ID

wandb: Run history:
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wandb:            eval/runtime ▇▆▁█▂▆▅▃▅▃
wandb: eval/samples_per_second ▂▃█▁▇▃▄▆▄▆
wandb:   eval/steps_per_second ▁▃█▁▇▃▄▆▅▆
wandb:             train/epoch ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇██
wandb:       train/global_step ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇██
wandb:         train/grad_norm █▆▅▄▄▂▁▁▁▁▁▁▁▁▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:     train/learning_rate ▁▂▃▄▄▅▆▇▇██▇▇▇▆▆▆▅▅▅▅▄▄▄▃▃▃▂▂▂▁▁
wandb:              train/loss ██▆▅▄▂▂▂▂▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: 
wandb: Run summary:
wandb:                eval/loss 0.96903
wandb:             eval/runtime 29.7799
wandb:  eval/samples_per_second 11.283
wandb:    eval/steps_per_second 5.641
wandb:               total_flos 9.391463750477414e+16
wandb:              train/epoch 7.97568
wandb:        train/global_step 328
wandb:          train/grad_norm 0.27018
wandb:      train/learning_rate 0.0
wandb:               train/loss 0.63
wandb:               train_loss 0.98594
wandb:            train_runtime 3396.186
wandb: train_samples_per_second 6.2
wandb:   train_steps_per_second 0.097

training_arguments = SFTConfig(
    output_dir=new_model,
    run_name="fine_tune_ocr_correction",
    per_device_train_batch_size=4, # max 4 batches
    per_device_eval_batch_size=2,
    gradient_accumulation_steps=16, # the bigger the better for GPUs
    optim="paged_adamw_32bit",
    num_train_epochs=8, 
    eval_strategy="steps",
    eval_steps=30,  
    save_steps=30,
    logging_steps=10,  
    warmup_steps=100,
    logging_strategy="steps",
    learning_rate= 5e-5, # 5e-5 = 0.00005 ; 2e-4 = 0.0002, 
    fp16=use_fp16, 
    bf16=use_bf16,  
    group_by_length=True,
    report_to="wandb",
    max_seq_length=1220,
    save_strategy="steps",
    dataset_text_field="text",
    max_grad_norm=1.0,
    warmup_ratio=0.05,
    load_best_model_at_end = True
)

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