80亿参数只开13亿，腾讯开源模型想用更少算力追平大模型
腾讯开源了一个大模型，总参数80亿，但每次推理只激活13亿——相当于一个团队80人，但每次干活只叫醒13个最相关的。它靠这个省算力、降成本，同时用20万亿token的精选语料和强化学习把能力拉上去，数学、编程、智能体任务上逼近比它大得多的模型。它还内置了两种思考模式：简单问题走快通道，复杂问题走慢通道，像人一样看题下菜。对普通用户，它不是你明天能装进手机的东西，但它是“开源模型用更少资源逼近闭源巨头”这条路上又一块实打实的里程碑。
📄 原文摘要(英文)
We present Hunyuan-A13B, an open-source large language model based on a Mixture-of-Experts architecture. It contains 80 billion total parameters but activates only 13 billion during inference, balancing model capability, computational efficiency, and deployment cost. The model is pretrained on a rigorously filtered 20T-token corpus with enhanced STEM data curation, improving factual reliability and reasoning ability. High-quality supervised fine-tuning and large-scale reinforcement learning further enhance its overall performance. Hunyuan-A13B also introduces a dual-mode Chain-of-Thought framework that adapts reasoning depth to task complexity: fast thinking for routine queries and slow thinking for complex, multi-step problems. Evaluations show competitive performance across mathematics, science, programming, general language understanding, and agent tasks, often approaching that of much larger models. Its high inference throughput makes it suitable for latency-sensitive applications. We release Hunyuan-A13B to support open research and practical LLM deployment.