Fine-Tuning Qwen 2.5 3B for Realistic Movie Dialogue Generation

--The Qwen 2.5 3B base model was fine-tuned to generate contextually rich and engaging movie dialogue, leveraging the Cornell Movie-Dialog Corpus, a curated dataset of movie conversations. Due to limitations in GPU computing and VRAM, the training process began with the 0.5B model, progressively scaling up to the 1.5B and 3B versions as efficiency improvements were implemented. The Qwen 2.5 series, developed by Alibaba Group, stands at the forefront of small open-source pre-trained models, particularly excelling in creative tasks compared to alternatives like Meta's Llama 3.2 and Google's Gemma. Results demonstrate the ability of small models to produce high-quality, realistic dialogue, offering a promising approach for real-time, context-sensitive conversation generation. This project aimed to fine-tune a small, pretrained large language model (LLM) to generate realistic and compelling movie dialogue when prompted with a preceding line. To achieve this, the Qwen 2.5 3B base model was fine-tuned using the Cornell Movie-Dialog Corpus, a curated dataset of movie dialogue [1].