metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - t5
  - flan
  - small
  - peft
  - QLoRA
  - cnn_dailymail
datasets:
  - cnn_dailymail
metrics:
  - rouge
base_model: google/flan-t5-small
model-index:
  - name: QLoRA-Flan-T5-Small
    results: []

QLoRA-Flan-T5-Small

This model is a fine-tuned version of google/flan-t5-small on the cnn_dailymail dataset. It achieves the following on the test set:

ROUGE-1: 0.3484265780526604
ROUGE-2: 0.14343059577230782
ROUGE-l: 0.32809541498574013

Model description

This model was fine-tuned with the purpose of performing the task of abstractive summarization.

Training and evaluation data

Fine-tuned on cnn_dailymail training set Evaluated on cnn_dailymail test set

How to use model

Loading the model

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load peft config for pre-trained checkpoint etc. 
peft_model_id = "emonty777/QLoRA-Flan-T5-Small"

config = PeftConfig.from_pretrained(peft_model_id)

# load base LLM model and tokenizer / runs on CPU
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# load base LLM model and tokenizer for GPU
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path,  load_in_8bit=True,  device_map={"":0})
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id, device_map={"":0})
model.eval()

Generating summaries

text = "Your text goes here..."

# If you want to use CPU
input_ids = tokenizer(text, return_tensors="pt", truncation=True).input_ids
# If you want to use GPU
input_ids = tokenizer(text, return_tensors="pt", truncation=True).input_ids.cuda()
# Adjust max_new_tokens based on size. This is set up for articles of text
outputs = model.generate(input_ids=input_ids, max_new_tokens=120, do_sample=False)

print(f"input sentence: {sample['article']}\n{'---'* 20}")
print(f"summary:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]}")

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4

Training results

Evaluated on full CNN Dailymail test set

ROUGE-1: 0.3484265780526604
ROUGE-2: 0.14343059577230782
ROUGE-l: 0.32809541498574013

Framework versions

Transformers 4.27.1
Pytorch 2.0.1+cu118
Datasets 2.9.0
Tokenizers 0.13.3