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This is a flan-t5-base model finetuned using QLoRA (PEFT) on dialogSum dataset : https://huggingface.co/datasets/knkarthick/dialogsum
Model Details
Training Details:
This is just a basic fine tuned model using below training args and params
lora_config = LoraConfig( r=16, lora_alpha=32, target_modules=['q','k','v','o'], lora_dropout=.05, bias='none', task_type=TaskType.SEQ_2_SEQ_LM #flan-t5 )
output_dir = f'/kaggle/working/qlora-peft-flant5-base-dialogue-summary-training-{str(int(time.time()))}'
peft_training_args_4bit = TrainingArguments(
output_dir=output_dir,
auto_find_batch_size=True,
learning_rate=1e-3, # Higher learning rate than full fine-tuning.
num_train_epochs=200,
logging_steps=10,
max_steps=200
)
peft_trainer_4bit = Trainer( model=peft_model_4bit, args=peft_training_args_4bit, train_dataset=tokenized_dataset_cleaned["train"], eval_dataset=tokenized_dataset_cleaned['validation'] )
Recorded training loss as below:
Step Training Loss 10 29.131100 20 4.856900 30 3.241400 40 1.346500 50 0.560900 60 0.344000 70 0.258600 80 0.201600 90 0.202900 100 0.198700 110 0.185000 120 0.177200 130 0.161400 140 0.164200 150 0.164300 160 0.165800 170 0.168700 180 0.155100 190 0.161200 200 0.170300
Rouge1 score for 100 test dataset(out of 1500) is : ORIGINAL MODEL: {'rouge1': 0.2232663790087573, 'rouge2': 0.06084131871447254, 'rougeL': 0.1936115999187245, 'rougeLsum': 0.19319411133637282} PEFT MODEL: {'rouge1': 0.34502805897556865, 'rouge2': 0.11517693222074701, 'rougeL': 0.2800665095598698, 'rougeLsum': 0.27941257109947587}
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Training Details
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Framework versions
- PEFT 0.7.1
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