Large models represent a groundbreaking advancement in multiple application fields,
enabling remarkable achievements across various tasks. However, their unprecedented …

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA
into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter …

Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Large Language Models (LLMs) trained on extensive textual corpora have emerged as
leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite …

Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …

Low-rank adapation (LoRA) is a popular method that reduces the number of trainable
parameters when finetuning large language models, but still faces acute storage challenges …

We introduce Chronos, a simple yet effective framework for pretrained probabilistic time
series models. Chronos tokenizes time series values using scaling and quantization into a …

Quantization is an indispensable technique for serving Large Language Models (LLMs) and
has recently found its way into LoRA fine-tuning. In this work we focus on the scenario where …