Parameter-Efficient Fine-Tuning of Gemma-2-Instruct-9B for Stance Detection

Example application for fine-tuning the instruction-tuned 9B variant of Google's Gemma 2 model (google/gemma-2-9b-it) on a new task (Stance Detection).

To make the fine-tuning feasible on a consumer GPU, a parameter-efficient fine-tuning (PEFT) approach based on QLoRA (Quantized Low-Rank Adaptation) is applied. While the original model has 9.295.724.032 parameters, this approach works with only 54.018.048 trainable parameters (~0.581%), leaving the original model weights entirely frozen during the fine-tuning process. This way, the model was fine-tuned for two epochs on the task using a single RTX 4080 GPU.

Corpus

Each model was fine-tuned on a 5,000 sentences Stance Detection corpus that I manually annotated during my Master's Thesis. Stance Detection aims to classify the stance a sentence takes towards a claim (topic) as either Pro, Contra or Neutral. The sentences originate from Reddit's r/ChangeMyView subreddit in the time span between January 2013 and October 2018, as provided in the ConvoKit subreddit corpus. They cover five topics: abortion, climate change, gun control, minimum wage and veganism. The table below shows some examples.

topic	sentence	stance label
There should be more gun control.	It's the only country with a "2nd Amendment", yet 132 countries have a lower murder rate.	Pro
Humanity needs to combat climate change.	The overhwelming evidence could be lies and you would never know because you're content to live your life as a giant appeal to authority.	Contra
Vegans are right.	It's all about finding a system that works for you.	Neutral

For the instruction-based fine-tuning and inference, the sentence pairs are wrapped in the following prompt:

prompt

expected output

The first text is a hypothesis/claim, the second text is a sentence. Determine whether the sentence is a pro argument ("pro"), a contra argument ("con") or doesnt take position at all/is neutral ("neu") towards the hypothesis. For your answer, just write exactly one of pro, con or neu, not a full text. Sentence to predict: hypothesis: ```There should be more gun control.``` sentence: ```It's the only country with a "2nd Amendment", yet 132 countries have a lower murder rate.``` stance:

pro

Results

Model	Accuracy	Micro-F1	Macro-F1
base model (google/gemma-2-9b-it)	0.72	0.71	0.71
fine-tuned model (gemma2-9b-it-stance-finetuned)	0.90	0.89	0.90

Requirements

- Python >= 3.10

- Conda

• pytorch==2.4.0
• cudatoolkit=12.1

- pip

• transformers
• datasets
• trl
• sentencepiece
• protobuf
• peft
• bitsandbytes
• openpyxl
• scikit-learn

Notes

The dataset files in this repository are cut off after the first 50 rows. The trained model files adapter_model.safetensors, optimizer.pt and tokenizer.json are omitted in this repository.