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EasyQuant: Transforming Big Language Model Quantization through Tencent’s Algorithm that doesn’t require Data

The constant progression of natural language processing (NLP) has brought about an era of advanced, large language models (LLMs) that can accomplish complex tasks with a considerably high level of accuracy. However, these models are costly in terms of computational requirements and memory, limiting their application in environments with finite resources. Model quantization is a…

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Establishing Connections with VisionLLaMA: A Comprehensive Framework for Visual Tasks

In recent years, large language models such as LLaMA, largely based on transformer architectures, have significantly influenced the field of natural language processing. This raises the question of whether the transformer architecture can be applied effectively to process 2D images. In response, a paper introduces VisionLLaMA, a vision transformer that seeks to bridge language and…

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Scientists from the University of California, San Diego and the University of Southern California have unveiled a revolutionary AI construct, dubbed CyberDemo. This groundbreaking structure is programmed for robotics to learn imitation from visual perceptions.

Automation and AI researchers have long grappled with dexterity in robotic manipulation, particularly in tasks requiring a high degree of skill. Traditional imitation learning methods have been hindered by the need for extensive human demonstration data, especially in tasks that require dexterous manipulation. The paper referenced in this article presents a novel framework, CyberDemo, which relies…

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Transforming AI Conversation: A Look at How FUSECHAT Combines Several Language Models to Create a Superior, More Memory-Efficient LLM.

The development of Large Language Models (LLMs) such as GPT and LLaMA has significantly revolutionized natural language processing (NLP). They have found use in a broad range of functions, causing a growing demand for custom LLMs amongst individuals and corporations. However, the development of these LLMs is resource-intensive, posing a significant challenge for potential users. To…

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The AI Research Team at Salesforce introduces the SFR-Embedding Model, which improves Text Retrieval using Transfer Learning Techniques.

Salesforce AI Researchers have developed a new solution to enhancing text-embedding models for use in a variety of natural language processing (NLP) tasks. While current models have set extremely high standards, it is believed there is room for progression, particularly in tasks related to retrieval, clustering, classification, and semantic textual similarity. The new model, named…

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This Chinese AI Research Document presents ChatMusician: A publicly available Language Model that incorporates innate musical capabilities.

The intersection of artificial intelligence (AI) and music has become an essential field of study, with Large Language Models (LLMs) playing a significant role in generating sequences. Skywork AI PTE. LTD. and Hong Kong University of Science and Technology have developed ChatMusician, a text-based LLM, to tackle the issue of understanding and generating music. ChatMusician shows…

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The Future of Code Generation Championed by StarCoder2 and The Stack v2: Implementing Large Language Models in a Revolutionary Way

The BigCode project has successfully developed StarCoder2, the second iteration of an advanced large language model designed to revolutionise the field of software development. A collaboration between over 30 top universities and institutions, StarCoder2 uses machine learning to optimise code generation, making it easier to fix bugs and automate routine coding tasks. Training StarCoder2 on…

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Scientists from the University of Oxford have unveiled Craftax: A Benchmark for Machine Learning in Open-Ended Reinforcement Learning.

Researchers from the University of Oxford and University College London have developed Craftax, a reinforcement learning (RL) benchmark that unifies effective parallelization, compilation, and the removal of CPU to GPU transfer in RL experiments. This research seeks to address the limitations educators face in using tools such as MiniHack and Crafter due to their prolonged…

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Optimizing Language Models for Efficiency and Recall: Presenting BASED for Fast, High-Quality Text Production

Language models' performance pertains to their efficiency and ability to recall information, with demand for these capabilities high as artificial intelligence continues to tackle the intricacies of human language. Researchers from Stanford University, Purdue University, and the University at Buffalo have developed an architecture, called Based, differing significantly from traditional methodologies. Its aim is to…

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IBM Research Introduces SimPlan: Narrowing the Divide in AI Planning using Advanced Hybrid Broad Language Model Technology

IBM Research has unveiled "SimPlan", an innovative method designed to enhance the planning capabilities of large language models (LLMs), which traditionally struggle with mapping out action sequences toward achieving an optimal outcome. The SimPlan method, developed by researchers from IBM, combines the linguistic skills of LLMs with the structured approach of classical planning algorithms, addressing…

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Introducing Sailor: A Collection of Advanced Language Models Aimed at Overcoming Language Challenges in Southeast Asia

A group of researchers from the Sea AI Lab and Singapore University of Technology and Design have developed Sailor, a sophisticated collection of language models designed to ease the process of language translation in linguistically-diverse regions such as Southeast Asia. This solution distinguishes itself by accurately addressing the nuances of languages such as Indonesian, Thai,…

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This article presents DiLightNet: A unique artificial intelligence technique that provides meticulous control over lighting in text-driven diffusion-based image creation.

In a collaborative effort, researchers from Microsoft Research Asia, Zhejiang University, College of William & Mary, and Tsinghua University introduced a novel artificial intelligence method called DiLightNet. This method aims to solve the fine-grained lighting control issue present in text-driven diffusion-based image generation. While current text-driven generative models can produce images from text prompts, they…

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