Baidu open-sources the 280-billion-parameter ERNIE 5.5 Flash Base foundation model

1. 05:33
   MarkTechPost

   Baidu Releases ERNIE 5.5 Flash Base: A 280B MoE Base Model With 14B Active Parameters

   Baidu has released ERNIE 5.5 Flash Base, a 280B-parameter text-only sparse MoE pre-training checkpoint that activates 14B parameters per token, with 128K training context and up to 1M for fine-tuning and inference. It uses hybrid attention (37 HCA layers caching 1 entry per 128 tokens plus 6 MLA+DSA layers), 512 routed experts with top-10 plus one shared expert, and ECHO in place of MTP, and was pre-trained from scratch on 33.5T tokens with the Muon optimizer. Baidu reports best scores on 24 of 30 base-model benchmarks in its unified pipeline, including MMLU-Pro 78.4 versus 69.2 for DeepSeek-V4-Flash-Base, with weights on Hugging Face and ModelScope in FP8 and BF16 and serving documented for SGLang and vLLM.

2. 01:56
   The Decoder

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

   Baidu has released ERNIE-5.5-Flash-Base, an open foundation model shipped without fine-tuning or post-training, with a post-trained version said to follow. It uses a mixture-of-experts architecture with 280 billion parameters, 14 billion active per input, and was pre-trained on 33.5 trillion tokens with a training context of up to 128,000 tokens, designed to support up to one million tokens during fine-tuning and inference. Baidu says it competes with similarly sized open foundation models like DeepSeek V4 Flash and MiMo V2.5 on reasoning, code, and long-context benchmarks, and the weights are available on Hugging Face in FP8 and BF16.

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