Search Results for author:
Found 5 papers, 2 papers with code
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
However, unlike pretraining or fine-tuning a single model, scaling reinforcement learning from human feedback (RLHF) for training large language models poses coordination challenges across four models.
DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification
Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic segmentation.
WeLM: A Well-Read Pre-trained Language Model for Chinese
Large Language Models pre-trained with self-supervised learning have demonstrated impressive zero-shot generalization capabilities on a wide spectrum of tasks.
PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
PatrickStar uses the CPU-GPU heterogeneous memory space to store the model data.
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters.
