arXiv:2412.10893cs.CLcs.AI2024-12被引 8

用1000亿字数据微调Gemma-2,让AI精通保加利亚语且不丢英语能力

BgGPT 1.0: Extending English-centric LLMs to other languages

  • 基于分支融合策略持续训练,用超大规模保加利亚语数据增强模型
  • 在保加利亚语任务中表现超越现有模型,英语能力保持不变
  • 开源模型权重+商业友好许可,适合语言研究与本地化应用

我们提出BgGPT-Gemma-2-27B-Instruct和BgGPT-Gemma-2-9B-Instruct:基于Google Gemma-2模型持续预训练与微调的版本,专为保加利亚语理解与生成优化。利用Gemma-2的多语言能力及超过1000亿词的保加利亚语与英语文本数据,模型在保加利亚语任务中表现优异,树立了语言专属AI的新标准。方法上采用近期分支融合技术的持续学习策略,并严格筛选训练数据以保留原始模型性能。模型提供商用友好许可,支持研究者、企业及爱好者使用。我们还基于非公开教育数据构建了全面评估基准,涵盖保加利亚语任务、安全性和对话能力。结果表明,对Gemma 2等先进模型进行细调,可有效提升语言特定应用性能,同时保持跨语言能力。

原文摘要 · Abstract (English)

We present BgGPT-Gemma-2-27B-Instruct and BgGPT-Gemma-2-9B-Instruct: continually pretrained and fine-tuned versions of Google's Gemma-2 models, specifically optimized for Bulgarian language understanding and generation. Leveraging Gemma-2's multilingual capabilities and over 100 billion tokens of Bulgarian and English text data, our models demonstrate strong performance in Bulgarian language tasks, setting a new standard for language-specific AI models. Our approach maintains the robust capabilities of the original Gemma-2 models, ensuring that the English language performance remains intact. To preserve the base model capabilities, we incorporate continual learning strategies based on recent Branch-and-Merge techniques as well as thorough curation and selection of training data. We provide detailed insights into our methodology, including the release of model weights with a commercial-friendly license, enabling broader adoption by researchers, companies, and hobbyists. Further, we establish a comprehensive set of benchmarks based on non-public educational data sources to evaluate models on Bulgarian language tasks as well as safety and chat capabilities. Our findings demonstrate the effectiveness of fine-tuning state-of-the-art models like Gemma 2 to enhance language-specific AI applications while maintaining cross-lingual capabilities.

多语言模型保加利亚语模型微调开源

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