12 citations · 12 across the 4 of their papers we have counts for
4 papers
AceSpec: An Asymmetric Edge-Cloud Collaborative Framework for Communication-Efficient LLM Inference
Yida Zhang, Zhiyong Gao, Shuaibing Yue +2
Deploying Large Language Models (LLMs) on edge devices typically relies on model compression or split inference. However, compression degrades reasoning capabilities, while split i…
A Pipelined Collaborative Speculative Decoding Framework for Efficient Edge-Cloud LLM Inference
Yida Zhang, Zhiyong Gao, Shuaibing Yue +2
Recent advancements and widespread adoption of Large Language Models (LLMs) in both industry and academia have catalyzed significant demand for LLM serving. However, traditional cl…
F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World
Ziyin Zhang, Zihan Liao, Hang Yu +2
We present F2LLM-v2, a new family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a newly curated composite of 60 million…
FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models
Xinglin Pan, Wenxiang Lin, Lin Zhang +5
Recent large language models (LLMs) have tended to leverage sparsity to reduce computations, employing the sparsely activated mixture-of-experts (MoE) technique. MoE introduces fou…