Remove Auto-complete Remove BERT Remove Large Language Models
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Best Large Language Models & Frameworks of 2023

AssemblyAI

However, among all the modern-day AI innovations, one breakthrough has the potential to make the most impact: large language models (LLMs). Large language models can be an intimidating topic to explore, especially if you don't have the right foundational understanding. What Is a Large Language Model?

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TensorRT-LLM: A Comprehensive Guide to Optimizing Large Language Model Inference for Maximum Performance

Unite.AI

As the demand for large language models (LLMs) continues to rise, ensuring fast, efficient, and scalable inference has become more crucial than ever. Kernel Auto-tuning : TensorRT automatically selects the best kernel for each operation, optimizing inference for a given GPU. build/tensorrt_llm*.whl

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Introduction to Large Language Models (LLMs): An Overview of BERT, GPT, and Other Popular Models

John Snow Labs

Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. In this comprehensive overview, we will explore the definition, significance, and real-world applications of these game-changing models. What are Large Language Models (LLMs)?

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This AI Paper by Microsoft and Tsinghua University Introduces YOCO: A Decoder-Decoder Architectures for Language Models

Marktechpost

This field primarily enhances machine understanding and generation of human language, serving as a backbone for various applications such as text summarization, translation, and auto-completion systems. Efficient language modeling faces significant hurdles, particularly with large models.

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Beyond ChatGPT; AI Agent: A New World of Workers

Unite.AI

Transformers and Advanced NLP Models : The introduction of transformer architectures revolutionized the NLP landscape. Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication. AI Agents vs. ChatGPT Many advanced AI agents, such as Auto-GPT and BabyAGI, utilize the GPT architecture.

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FastGen: Cutting GPU Memory Costs Without Compromising on LLM Quality

Marktechpost

However, these models pose challenges, including computational complexity and GPU memory usage. Despite great success in various applications, there is an urgent need to find a cost-effective way to serve these models. Still, an increase in model size and generation length leads to an increase in memory usage of the KV cache.

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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

Unlocking Unstructured Data with LLMs Leveraging large language models (LLMs) for unstructured data extraction is a compelling solution with distinct advantages that address critical challenges. Context-Aware Data Extraction LLMs possess strong contextual understanding, honed through extensive training on large datasets.