MuntasirHossain/flan-t5-large-samsum-qlora

Model description

flan-t5-large-samsum-qlora is an LLM model adapter and a fine-tuned version of google/flan-t5-large on the samsum dataset containing dialoges. Parameter-efficient fine-tuning with QLoRA was employed to fine-tune the base model. The model is intended for generative summarization tasks and achieved the following scores on the test dataset:

• Rougue1: 49.249596%
• Rouge2: 23.513032%
• RougeL: 39.960812%
• RougeLsum: 39.968438%

How to use

Load the model:

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

# Load the peft adapter model config
peft_model_id = 'MuntasirHossain/flan-t5-large-samsum-qlora' 
peft_config = PeftConfig.from_pretrained(peft_model_id)

# load the base model and tokenizer
base_model = AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path,  load_in_8bit=True,  device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)

# Load the peft model
model = PeftModel.from_pretrained(base_model, peft_model_id, device_map="auto")
model.eval()

Example Inference:

# random sample text from the samsum test dataset
text = """
Emma: Hi, we're going with Peter to Amiens tomorrow.
Daniel: oh! Cool.
Emma: Wanna join?
Daniel: Sure, I'm fed up with Paris.
Emma: We're too. The noise, traffic etc. Would be nice to see some countrysides.
Daniel: I don't think Amiens is exactly countrysides though :P
Emma: Nope. Hahahah. But not a megalopolis either!
Daniel: Right! Let's do it!
Emma: But we should leave early. The days are shorter now.
Daniel: Yes, the stupid winter time.
Emma: Exactly!
Daniel: Where should we meet then?
Emma: Come to my place by 9am.
Daniel: oohhh. It means I have to get up before 7!
Emma: Yup. The early bird gets the worm (in Amiens).
Daniel: You sound like my grandmother.
Emma: HAHAHA. I'll even add: no parties tonight, no drinking dear Daniel
Daniel: I really hope Amiens is worth it!
"""

input = tokenizer(text, return_tensors="pt")
outputs = model.generate(input_ids=input["input_ids"].cuda(), max_new_tokens=40) 
print("Summary: ", tokenizer.decode(outputs[0], skip_special_tokens=True))

Summary:  Emma and Peter are going to Amiens tomorrow. Daniel will join them. They will meet at Emma's place by 9 am. They will not have any parties tonight.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.001
• 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: 5

Training results

Framework versions

• PEFT 0.8.2
• Transformers 4.38.1
• Pytorch 2.1.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2