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Accelerating Large Language Model Inference: Techniques for Efficient Deployment

Unite.AI

Large language models (LLMs) like GPT-4, LLaMA , and PaLM are pushing the boundaries of what's possible with natural language processing. While still computationally intensive, these models could be deployed on modest hardware and followed relatively straightforward inference processes.

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How to Use Hugging Face Pipelines?

Towards AI

A practical guide on how to perform NLP tasks with Hugging Face Pipelines Image by Canva With the libraries developed recently, it has become easier to perform deep learning analysis. Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more.

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Sentiment Analysis with Python and Streamlit

Heartbeat

Build and deploy your own sentiment classification app using Python and Streamlit Source:Author Nowadays, working on tabular data is not the only thing in Machine Learning (ML). are getting famous with use cases like image classification, object detection, chat-bots, text generation, and more.

Python 52
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Amazon EC2 DL2q instance for cost-efficient, high-performance AI inference is now generally available

AWS Machine Learning Blog

Amazon Elastic Compute Cloud (Amazon EC2) DL2q instances, powered by Qualcomm AI 100 Standard accelerators, can be used to cost-efficiently deploy deep learning (DL) workloads in the cloud. Set up the environment and install required packages Install Python 3.8. Set up the Python 3.8 This is a guest post by A.K

BERT 95
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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts.

ML 72
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Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

AWS Machine Learning Blog

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. One of the primary reasons that customers are choosing a PyTorch framework is its simplicity and the fact that it’s designed and assembled to work with Python.

ML 78
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Fine-tune GPT-J using an Amazon SageMaker Hugging Face estimator and the model parallel library

AWS Machine Learning Blog

The model is trained on the Pile and can perform various tasks in language processing. It can support a wide variety of use cases, including text classification, token classification, text generation, question and answering, entity extraction, summarization, sentiment analysis, and many more.