Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables

Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.

Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing

Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.

WoS题录Keywords字段关键词频度统计程序设计与实现

使用Visual Basic编程,采用正则表达式批量提取由Web of Science导出的Bib Tex题录中所有Keywords字段关键词,按需合并所得关键词的同义词、近义词及词形变化词,然后将出现频度的统计数据写入Excel表,并编制Excel宏自动生成折线图,实现关键词分布的简单可视化。情报工作者后续可借助Excel功能对该程序生成的Excel表执行复杂的数据组合分析,以提高工作效率。

How to Write English Titles, Abstract and Key Words for Folklore Papers： Practice and Analysis

Folklore research entails field trips, serve as secondary role. Writing of title, abstract and while textual study and circumstantial investigation merely keywords for folklore papers differs from that of other types of articles. Proceeding from writing strategies and linguistic features, the authors intend to share their experience with fellow researchers.

An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

Eight Key Words about Internet in the Government Work Report

In the government work report for Two Sessions 2019, key words about E-commerce and internet were mentioned lots of times. This included terms such as Internet+, cross-border E-commerce,industrial internet, digital economy,sharing economy, online and offline consumption, Internet+education and soon.Keywords No.1:Internet+Work Report:1. Internet+was advanced across the board and new technologies and models were used to transform traditional industries. 2. We will speed up efforts to pursue Internet+in all industries and sectors.

Key words For China

expressions like ＂socialism with Chinese character- istics＂ and ＂comprehensively deepening reform？＂ Then help is at hand with a program launched in December to enable foreigners understand politi- cal and cultural phrases,

Practical Encrypted Key Agreement Using Passwords

We presented a simple and efficient password-based encrypted key exchange protocol that allows a user to establish secure session keys with remote servers from client terminals in low resource environments. He does not need to carry smart card storing his private information but just needs to know his identity and password. For this purpose, the scheme was implemented over elliptic curves because of their well-known advantages with regard to processing and size constraints. Furthermore, the scheme is provably secure under the assumptions that the hash function closely behaves like a random oracle and that the elliptic curve computational Diffie-Hellman problem is difficult.