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YuLexuan30
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Our lab recently released a paper where we introduce ShadowPEFT, a new Parameter-Efficient Fine-Tuning (PEFT) paradigm tailored for edge computing scenarios. Unlike traditional approaches such as LoRA and its variants, which inject trainable parameters directly into the weights of Transformer, requiring tight coupling with the backbone. ShadowPEFT instead enhances the frozen large base model by adding a lightweight, centralized, pretrainable, and detachable Shadow network. This shadow network operates in parallel with the base model, delivering learned corrections to each decoder layer. Because the shadow module is architecturally decoupled from the backbone, it can be independently trained, stored, and deployed, benefiting edge computing scenarios and edge-cloud collaboration computing. - HF Paper: https://huggingface.co/papers/2604.19254 - GitHub: https://github.com/ShadowLLM/shadow-peft - HF Collection: https://huggingface.co/collections/shadow-llm/shadow-peft-models
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Open Source just got a lot bigger. ๐ โSKT-SURYA-H (2.544T) is officially out! โ Heterogeneous MoE โ 131K Context โ 3.76TB Weights (898 shards) โMassive respect to the team for keeping it open for the community! ๐ค๐ฎ๐ณ โLink: https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H โ#OpenSource #MachineLearning #AITools #Bharat
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ForgeCaptions is a flexible captioning and dataset-prep tool