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daggar/
flan-t5-dialogsum-qlora

Name: flan-t5-dialogsum-qlora
Rating: 2.5 (6 reviews)

peft

safetensors

summarization

dialogue-summarization

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Flan-T5-Large + QLoRA on DialogSum

google/flan-t5-large adapted for dialogue summarization on DialogSum, trained as part of an IIT-D Gen-AI course project comparing four fine-tuning methods under identical conditions.

Method: QLoRA — LoRA (r=16, alpha=32) over a 4-bit NF4 quantized base

Code, evaluation harness and the other three models: https://github.com/dipika-s/iitd-genai

Usage

python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large")
model = PeftModel.from_pretrained(base, "daggar/flan-t5-dialogsum-qlora")
tokenizer = AutoTokenizer.from_pretrained("daggar/flan-t5-dialogsum-qlora")

dialogue = "#Person1#: Hi, how was your weekend?\n#Person2#: Great, I went hiking."
inputs = tokenizer("Summarize the following dialogue:\n" + dialogue,
                   return_tensors="pt", max_length=512, truncation=True)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=128)[0],
                       skip_special_tokens=True))

Inputs must use the training prompt — "Summarize the following dialogue:\n" followed by the dialogue — truncated to 512 tokens. Summaries were trained at up to 128 tokens.

Training


Base model	`google/flan-t5-large`
Dataset	`knkarthick/dialogsum` — 12,460 train / 500 validation / 1,500 test
Epochs	2
Optimizer steps	3,116
Trainable parameters	4,718,592 of 498,461,696 (0.6%)
Peak GPU memory	2.52 GB
Training time	73.5 min on an AWS `g5.2xlarge` (A10G 24GB)
Final training loss	1.0581
Final eval loss	0.9104

Evaluation

Measured on the full 1,500-example DialogSum test split, against the untuned base model.

Metric	Base	This model
BLEU	5.7129	9.808
ROUGE-1	37.3183	45.0564
ROUGE-2	15.1221	19.5308
ROUGE-L	30.8519	37.013
METEOR	22.2824	33.8143
GLEU	9.7314	15.6236
BERTScore	89.3701	91.5786
CoSIM	34.366	40.9668
Repetition rate	2.2415	1.7126
Flesch reading ease	75.0617	67.0417
Toxicity	0.7889	0.5947
Novelty	71.759	60.8588
Diversity	22.809	22.207

All four methods

Method	Trainable params	Peak GPU	ROUGE-L	BERTScore
Full FT	783M (100%)	16.11 GB	37.42	91.64
LoRA	4.7M (0.60%)	4.51 GB	37.40	91.66
QLoRA	4.7M (0.60%)	2.52 GB	37.01	91.58
Prefix	4.9M (0.62%)	8.85 GB	30.85*	89.37*

* see the known issue on the prefix model card.

Limitations

Trained only on DialogSum, which is two-speaker English conversation transcripts using #Person1# / #Person2# speaker tags. Summaries of longer, multi-party, domain-specific or non-English dialogue will be unreliable. Dialogues over 512 tokens are truncated, so content late in a long conversation may be dropped. The model inherits any biases present in google/flan-t5-large and in DialogSum, and summaries can contain details not supported by the source dialogue.