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Found 5 papers, 1 papers with code
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications
Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications, from search and recommendations to generative tasks.
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation
In this report, we present our research to address these challenges by utilizing a large foundation model with a textual interface for ranking and recommendation tasks.
Liger Kernel: Efficient Triton Kernels for LLM Training
Training Large Language Models (LLMs) efficiently at scale presents a formidable challenge, driven by their ever-increasing computational demands and the need for enhanced performance.
Enhancing Stability for Large Language Models Training in Constrained Bandwidth Networks
Training extremely large language models (LLMs) with billions of parameters is a computationally intensive task that pushes the limits of current data parallel training systems.
LiRank: Industrial Large Scale Ranking Models at LinkedIn
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.
