LongCat-Flash-Lite-FP8

モデル紹介

We introduce LongCat-Flash-Lite, a non-thinking 68.5B parameter Mixture-of-Experts (MoE) model with approximately 3B activated parameters, supporting a 256k context length through the YaRN method. Building upon the LongCat-Flash architecture, LongCat-Flash-Lite distinguishes itself through the integration of an **N-gram embedding** table designed to enhance both model performance and inference speed. Despite allocating over 30B parameters to embeddings, LongCat-Flash-Lite not only outperforms parameter-equivalent MoE baselines but also demonstrates exceptional competitiveness against existing models of comparable scale, particularly in the agentic and coding domains. Through comprehensive scaling experiments across diverse scenarios, we identify specific regimes where embedding scaling achieves a superior Pareto frontier compared to increasing the number of experts, thereby offering a highly efficient alternative for model scaling. We further delineate a comprehensive set of architectural factors that determine embedding scaling efficacy, encompassing integration timing, parameter budgeting, hash collision mitigation, hyperparameter configuration, and embedding initialization, alongside the impacts of model width and depth. In contrast to FFN-based experts, the N-gram embedding table inherently mitigates I/O bottlenecks within MoE layers, yielding substantial improvements in inference latency. Furthermore, we introduce a specialized N-gram Cache and develop synchronized kernels, which collectively and significantly boost inference efficiency.

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

よくある質問

出典と追跡記録

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。