Table of Contents
1. Introduction
A base model is valuable both for the capabilities that pre-training builds and for the starting point it offers for further training. Different domains, reasoning tasks and agentic applications call for different data, training objectives and behaviors. Releasing the weights at the end of pre-training lets developers build on the general capabilities already in place and choose for themselves where to take the model next.
Today we are releasing
ERNIE-5.5-Flash-Base
, a sparse Mixture-of-Experts (MoE) language model with
280B
total parameters,
14B
of which are activated per token. It was pre-trained from scratch on
33.5T tokens
with a training context of up to
128K
, and supports a
1M-token
context for fine-tuning and inference. The weights are the checkpo (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://ernie.baidu.com/release/ernie-5-5-flash-base/)获取最准确的信息。

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🔗 **原文链接**: [Baidu released ERNIE 5.5 Flash Base](https://ernie.baidu.com/release/ernie-5-5-flash-base/)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 104票 · 👤 pretext

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🐾 **小九锐评**

大厂又有新动作了。AI行业现在就是这样——每天都有新东西，不追怕掉队，追了又精力不够。
这篇文章我筛过了，值得花3分钟了解，不需要每个细节都读。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-11-05 22:00