We present F2LLM-v2, a new family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a newly curated composite of 60 million publicly available high-quality data samples, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages. By integrating a two-stage LLM-based embedding training pipeline with matryoshka learning, model pruning, and knowledge distillation techniques, we present models that are far more efficient than previous LLM-based embedding models while retaining competitive performances. Extensive evaluations confirm that F2LLM-v2-14B ranks first on 11 MTEB benchmarks, while the smaller models in the family also set a new state of the art for resource-constrained applications. To facilitate open-source embedding model research, we release all models, data, code, and intermediate checkpoints.

翻译：我们提出F2LLM-v2，这是一个面向多语言通用嵌入模型的新系列，包含从80M到14B的8种不同规模。该系列模型基于新精选的6000万条公开高质量数据样本进行训练，支持200余种语言，特别侧重以往服务不足的中低资源语言。通过将两阶段LLM嵌入训练流程与俄罗斯套娃学习、模型剪枝及知识蒸馏技术相结合，我们提出的模型在保持竞争力的同时，效率远超以往基于LLM的嵌入模型。广泛评估证实，F2LLM-v2-14B在11项MTEB基准测试中排名第一，而系列中较小规模的模型也为资源受限应用树立了新标杆。为促进开源嵌入模型研究，我们公开了所有模型、数据、代码及中间检查点。