Add to ORKGMany NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download pretrained models of this scale. Still, using these models requires high-end hardware unavailable to many researchers. In some cases, LLMs can be used more affordably via RAM offloading or hosted APIs. However, these techniques have innate limitations: offloading is too slow for interactive inference, while APIs are not flexible enough for research. In this work, we propose Petals $-$ a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties trusted to process client's data. We demonstrate that this strateg...
Large language models (LLMs) are now available in various sizes and configurations from cloud API pr...
Language model fine-tuning is essential for modern natural language processing, but is computational...
Language models, given their black-box nature, often exhibit sensitivity to input perturbations, lea...
Scaling language models with more data, compute and parameters has driven significant progress in na...
The recent advance of self-supervised learning associated with the Transformer architecture enables ...
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resourc...
Inspired by Federated Learning, in this paper, we propose personal large models that are distilled f...
<p>When building large-scale machine learning (ML) programs, such as big topic models or deep neural...
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstr...
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). Ho...
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown e...
Deploying large language models (LLMs) is challenging because they are memory inefficient and comput...
Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (N...
The reusability of state-of-the-art Pre-trained Language Models (PLMs) is often limited by their gen...
Distilling state-of-the-art transformer models into lightweight student models is an effective way t...
Large language models (LLMs) are now available in various sizes and configurations from cloud API pr...
Language model fine-tuning is essential for modern natural language processing, but is computational...
Language models, given their black-box nature, often exhibit sensitivity to input perturbations, lea...
Scaling language models with more data, compute and parameters has driven significant progress in na...
The recent advance of self-supervised learning associated with the Transformer architecture enables ...
Parameter-shared pre-trained language models (PLMs) have emerged as a successful approach in resourc...
Inspired by Federated Learning, in this paper, we propose personal large models that are distilled f...
<p>When building large-scale machine learning (ML) programs, such as big topic models or deep neural...
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstr...
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). Ho...
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown e...
Deploying large language models (LLMs) is challenging because they are memory inefficient and comput...
Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (N...
The reusability of state-of-the-art Pre-trained Language Models (PLMs) is often limited by their gen...
Distilling state-of-the-art transformer models into lightweight student models is an effective way t...
Large language models (LLMs) are now available in various sizes and configurations from cloud API pr...
Language model fine-tuning is essential for modern natural language processing, but is computational...
Language models, given their black-box nature, often exhibit sensitivity to input perturbations, lea...