Genre-Controlled Story Generation using QLoRA
Project Type
Objective
Built a reproducible instruction-tuning pipeline for genre-controlled short story generation using Gemma 3-1B, QLoRA, and PEFT, then evaluated whether fine-tuning improved controllability and output quality.
Tools & Technologies
Project Details
Fine-tuned Gemma 3-1B with QLoRA and PEFT for controlled text generation using structured prompt formatting, dataset curation, and held-out evaluation.
Evaluated base vs. adapted models using validation loss, perplexity, genre fidelity, coherence, and LLM-as-Judge scoring.
Experimented with decoding parameters such as temperature and top-k to improve generation quality while tracking failure cases including repetition, weak endings, and genre drift.
Built an interactive Streamlit app so users could test genre, prompt, and generation settings in a simple interface.