阿里通義千問團隊近日開源全新多模態MoE模型Qwen3.8-Flash-Next，作爲Qwen4架構早期預覽版。該模型採用高度稀疏的混合專家設計，總參數1250億，另搭配510億參數N-gram嵌入表和40億參數多token預測模塊，以極低計算開銷實現越級性能表現。
阿里千問發佈多模態MoE模型Qwen3.8-Flash，並開源下一代架構Qwen3.8-Flash-Next（Qwen4雛形）。模型總參數125B，搭載51B N-gram Embedding，每token僅激活6B；原生支持26萬token，可擴展至100萬。訓練成本爲前代1/9，API定價輸入每百萬Tokens 1元、輸出3元。
DeepSeek團隊推出Engram模塊，爲稀疏大語言模型引入“條件記憶軸”，旨在解決傳統Transformer處理重複知識時計算資源浪費的問題。該模塊作爲混合專家模型的補充，將N-gram嵌入技術融入模型，提升處理重複模式的效率。
nghuyong
ERNIE-Gram是一個通過顯式N-Gram掩碼語言建模進行預訓練的自然語言理解模型
mfleck
基於facebook/wav2vec2-xls-r-300m在Common Voice德語數據集上微調的語音識別模型，集成n-gram語言模型，詞錯誤率8.8%
imvladikon
基於facebook/wav2vec2-xls-r-300m微調的希伯來語語音識別模型，添加了n-gram語言模型增強
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