中文简介
Qwen3 14B 面向对输出质量要求更高、且具备服务器或高配置工作站条件的团队。它延续 Qwen3 的思考与非思考双模式，适合在 8B 以上进一步评估复杂指令、代码和知识任务。
上游模型卡 / 数据集卡
Qwen3 14B 是 Qwen3 系列的中大型稠密语言模型，支持思考与非思考模式，适合对复杂指令、代码和知识任务进行更高质量的评估。
其原生上下文长度为 32,768 tokens，上游提供 YaRN 扩展至最高 131,072 tokens 的配置说明。长上下文能力需要结合目标框架和真实材料验证。
14B 档会带来更高的存储、内存和计算要求。选择前宜与 8B 档做同任务对照，确认质量增益、时延和部署成本。
Qwen3-14B Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios. **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**. Model Overview Qwen3-14B** has the following features: Type: Causal Language Models Training Stage: Pretraining & Post-training Number of Parameters: 14.8B Number of Paramaters (Non-Embedding): 13.2B Number of Layers: 40 Number of Attention Heads (GQA): 40 for Q and 8 for KV Context Length: 32,768 natively and 131,072 tokens with YaRN. For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation. Quickstart The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you t
适用场景
适合复杂文档处理、代码辅助、知识工作流、企业内部评测和高质量内容生成。应以领域样本比较 8B、14B 与更大档位的增益是否值得额外成本。
模型参数
约 14.8B 参数的稠密模型，原生上下文长度为 32,768 tokens。可按上游 YaRN 说明扩展至最高 131,072 tokens，并支持思考与非思考模式。
文件说明
Hugging Face 官方 API 在已审核提交中记录 18 个文件条目，总存储量约 27.52 GB。交付前仍应按目标版本和格式重新确认具体文件。
上游文件元数据
.gitattributes1.53 KBconfig.json728 Bgeneration_config.json239 BLICENSE11.08 KBmerges.txt1.59 MBmodel-00001-of-00008.safetensors3.58 GBmodel-00002-of-00008.safetensors3.69 GBmodel-00003-of-00008.safetensors3.69 GBmodel-00004-of-00008.safetensors3.69 GBmodel-00005-of-00008.safetensors3.69 GBmodel-00006-of-00008.safetensors3.69 GBmodel-00007-of-00008.safetensors3.69 GB
硬件建议
通常需要比 8B 档更充足的 GPU 显存或系统内存。实际容量随精度、量化、上下文、批量和框架变化，建议用目标工作负载做显存峰值、吞吐和稳定性测试。
注意事项
更大参数量不代表对所有任务都更好，也不会消除幻觉、偏见或提示注入风险。业务数据进入模型前仍需落实分类、脱敏、权限和日志制度。
本页面为橙子AI科技基于固定版本上游卡片整理的中文信息与服务说明，不代表资源作者或平台官方页面。实际许可、访问和使用条件以上游原文为准。