---
library_name: peft
base_model: google/flan-t5-base
---

# Model Card for Model ID

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}


## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Training Hyperparameters

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## Evaluation

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### Results

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Framework versions

- PEFT 0.7.1