用小模型生成逼真电影对白，效果媲美大模型。
Fine-Tuning Qwen 2.5 3B for Realistic Movie Dialogue Generation
- 基于Qwen 2.5 3B模型，逐步微调生成对话。
- 在有限算力下实现高质量、上下文相关对白生成。
- 适合需要实时互动的影视创作与对话系统。
本文将阿里巴巴集团开发的Qwen 2.5 3B基础模型进行微调，以生成具有情境丰富性与吸引力的电影对白，所用数据集为精选的Cornell Movie-Dialog Corpus。由于GPU计算资源与显存限制，训练过程从0.5B模型起步，逐步扩展至1.5B和3B版本，并随着效率提升不断优化。相较于Meta的Llama 3.2与Google的Gemma等模型，Qwen 2.5系列在创意任务中表现更优，尤其在小型开源模型中处于领先地位。实验表明，小型模型亦可生成高质量、真实的对白内容，为实时、上下文敏感的对话生成提供了可行方案。
原文摘要 · Abstract (English)
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 the 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。