Model Card for Model ID
wandb: Run history:
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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 0.50501
wandb: eval/runtime 49.8148
wandb: eval/samples_per_second 6.745
wandb: eval/steps_per_second 2.248
wandb: total_flos 1.7024284869758976e+16
wandb: train/epoch 1.45897
wandb: train/global_step 480
wandb: train/grad_norm 0.46409
wandb: train/learning_rate 0.00014
wandb: train/loss 0.2886
wandb: train_loss 0.55963
wandb: train_runtime 2282.1745
wandb: train_samples_per_second 5.766
wandb: train_steps_per_second 0.721
training_arguments = SFTConfig(
output_dir=new_model,
run_name="fine_tune_ocr_correction",
per_device_train_batch_size=2, #8
per_device_eval_batch_size=3,
gradient_accumulation_steps=4,
optim="paged_adamw_32bit",
num_train_epochs=5,
eval_strategy="steps",
eval_steps=30,
save_steps=30,
logging_steps=10,
warmup_steps=10,
logging_strategy="steps",
learning_rate= 2e-4, #5e-5,
fp16=use_fp16,
bf16=use_bf16,
group_by_length=True,
report_to="wandb",
max_seq_length=1220,
save_strategy="steps",
dataset_text_field="text",
load_best_model_at_end = True
)
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