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Using Large Language Models on Amazon Bedrock for multi-step task execution

AWS Machine Learning Blog

The goal of this blog post is to show you how a large language model (LLM) can be used to perform tasks that require multi-step dynamic reasoning and execution. These function signatures act as tools that the LLM can use to formulate a plan to answer a users query.

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MLPerf Inference v3.1 introduces new LLM and recommendation benchmarks

AI News

The latest release of MLPerf Inference introduces new LLM and recommendation benchmarks, marking a leap forward in the realm of AI testing. An updated recommender benchmark – refined to align more closely with industry practices – employs the DLRM-DCNv2 reference model and larger datasets, attracting nine submissions.

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Robot Photographer Takes the Perfect Picture

Flipboard

He enjoyed working at the intersection of several fields; human robot interaction, large language models, and classical computer vision were all necessary to create the robot. The LLM can be programmed to return any number of reference photographs.

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Multimodal Large Language Models

The MLOps Blog

TL;DR Multimodal Large Language Models (MLLMs) process data from different modalities like text, audio, image, and video. Compared to text-only models, MLLMs achieve richer contextual understanding and can integrate information across modalities, unlocking new areas of application. How do multimodal LLMs work?

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Shanghai AI Lab Presents HuixiangDou: A Domain-Specific Knowledge Assistant Powered by Large Language Models (LLM)

Marktechpost

Researchers from Shanghai AI Laboratory introduced HuixiangDou, a technical assistant based on Large Language Models (LLM), to tackle these issues, marking a significant breakthrough. HuixiangDou is designed for group chat scenarios in technical domains like computer vision and deep learning.

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Exploring Parameter-Efficient Fine-Tuning Strategies for Large Language Models

Marktechpost

Large Language Models (LLMs) signify a revolutionary leap in numerous application domains, facilitating impressive accomplishments in diverse tasks. Yet, their immense size incurs substantial computational expenses. With billions of parameters, these models demand extensive computational resources for operation.

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Microsoft Research Introduces GraphRAG: A Unique Machine Learning Approach that Improves Retrieval-Augmented Generation (RAG) Performance Using Large Language Model (LLM) Generated Knowledge Graphs

Marktechpost

Large Language Models (LLMs) have extended their capabilities to different areas, including healthcare, finance, education, entertainment, etc. These models have utilized the power of Natural Language Processing (NLP), Natural Language Generation (NLG), and Computer Vision to dive into almost every industry.