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百度开源 280B 参数基座模型 ERNIE-5.5-Flash-Base

原标题:Baidu releases ERNIE 5.5 Flash Base with 280 billion parameters as an open-source foundation model

百度发布开源基座模型 ERNIE-5.5-Flash-Base,采用 MoE 架构,总参数 280B、每输入激活 14B,未做微调或后训练,后训练版本将随后推出。模型在 33.5 万亿 token 上预训练,训练上下文最高 128k token,微调和推理设计支持最高 100 万 token 上下文;百度称其在推理、代码和长上下文基准上可与 DeepSeek V4 Flash、MiMo V2.5 等同规模开源基座模型竞争,权重以 FP8 和 BF16 格式在 Hugging Face 提供。

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原文给出参数规模、训练数据量和上下文窗口等具体规格,读者可据此评估这款开源基座模型与同类模型的定位。

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原文

Baidu's AI development is still alive, but most of the work is on you. The Chinese company has released ERNIE-5.5-Flash-Base, an open foundation model that ships deliberately without fine-tuning or post-training. Developers are expected to decide for themselves where to take the model. A post-trained version is supposed to follow.

ERNIE 5.5 uses a mixture-of-experts architecture with 280 billion parameters, 14 billion of which are active per input. The model was pre-trained on 33.5 trillion tokens with a training context of up to 128,000 tokens, though it's designed to support a context of up to one million tokens during fine-tuning and inference.

Benchmark results for ERNIE 5.5 Flash Base compared to DeepSeek-V4-Flash-Base, Hy3-preview-Base, and MiMo-V2.5-Base, tested with the same evaluation pipeline across the categories Exam, STEM, Reasoning, Knowledge, Code, Multilingual, and Long Context. | Image: Baidu

In benchmarks for reasoning, code, and long-context tasks, the model competes with similarly sized open foundation models like DeepSeek V4 Flash or MiMo V2.5, according to Baidu. The weights are available on Hugging Face in FP8 and BF16.

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事件综述

百度开源280B参数MoE基座模型ERNIE-5.5-Flash-Base

2026年11月5日,The Decoder报道百度发布开源基座模型ERNIE-5.5-Flash-Base。该模型采用MoE架构,总参数280B、每输入激活14B,在33.5万亿token上预训练,训练上下文最高128k token,微调和推理设计支持最高100万token上下文。它是预训练结束时的checkpoint,未做微调或后训练,可作为领域适配、指令微调和强化学习研究的起点;后训练版本将随后推出。百度称其在推理、代码和长上下文基准上可与DeepSeek V4 Flash、MiMo V2.5等同规模开源基座模型竞争。11月7日,Hugging Face热门模型页面的后续报道补充了架构细节:模型采用混合MLA+HCA注意力(37层HCA、6层MLA+DSA),每层512个路由专家(top-10)加1个共享专家,并引入低秩门控注意力与VHA以降低KV cache和长上下文成本;评测与DeepSeek-V4-Flash-Base、Hy3-preview-Base、MiMo-V2.5-Base用同一流程对比。目前进展:权重已以FP8和BF16两种精度格式在Hugging Face提供(后续报道称也可在ModelScope获取)。两篇报道的参数量、激活参数、训练token数与上下文长度等关键数字一致,无矛盾。

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