Baidu releases ERNIE 5.5 Flash Base language model
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, the raw pre-training checkpoint of its new sparse Mixture-of-Experts language model. ERNIE 5.5 Flash Base is a 280B-parameter text model from Baidu that activates 14B parameters per token. The weights stop at the end of pre-training, with no mid- or post-training applied. This as a clean starting point for teams that want to compare fine-tuning, instruction tuning or RL methods from one checkpoint. TL;DR Size: 280B total, 14B active per token; 128
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A 280-billion parameter language model was released as a raw pre-training checkpoint with 14 billion active parameters per token, designed as a clean starting point for teams to test their own fine-tuning and training methods. The model uses a sparse architecture where most layers employ heavily compressed attention that caches only one entry per 128 tokens, while maintaining full-context access in a smaller number of layers. On standardized benchmarks evaluated through the same pipeline, it scored highest on 24 of 30 base-model tests, outperforming comparable models from competing developers on tasks like MMLU-Pro and HumanEval+. The weights are available in multiple precision formats and can be deployed using standard serving frameworks with up to 1 million token context lengths for inference.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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