Accelerate Mixtral 8x7B pre-training with expert parallelism on Amazon SageMaker
Mixture of Experts (MoE) architectures for large language models (LLMs) have become increasingly popular due to their ability to enhance ...
Tag: PreTraining
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7× Faster Pre-training on Web-scale Image-Text Data
Contrastive learning is now recognized as a revolutionary approach to developing high-quality visual representations by aligning image and text embeddings. ...
Scalable Pre-training of Large Autoregressive Image Models
This paper introduces AIM, a collection of vision models pre-trained with an autoregressive objective. These models are inspired by their ...
Google DeepMind Researchers Propose a Novel AI Method Called Sparse Fine-grained Contrastive Alignment (SPARC) for Fine-Grained Vision-Language Pretraining
Contrastive pre-training using large, noisy image-text datasets has become popular for building general vision representations. These models align global image ...
Meet MosaicBERT: A BERT-Style Encoder Architecture and Training Recipe that is Empirically Optimized for Fast Pretraining
BERT is a language model which was released by Google in 2018. It is based on the transformer architecture and ...
Unlocking the Secrets of CLIP’s Data Success: Introducing MetaCLIP for Optimized Language-Image Pre-training
In recent years, there have been exceptional advancements in Artificial Intelligence, with many new advanced models being introduced, especially in ...
Researchers from the University of Washington and Princeton Present a Pre-Training Data Detection Dataset WIKIMIA and a New Machine Learning Approach MIN-K% PROB
Large Language Models (LLMs) are powerful models capable of processing large volumes of textual data. They are trained on a ...
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