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A Guide to 400+ Categorized Large Language Model(LLM) Datasets

Analytics Vidhya

But what if I tell you there’s a goldmine: a repository packed with over 400+ datasets, meticulously categorised across five essential dimensions—Pre-training Corpora, Fine-tuning Instruction Datasets, Preference Datasets, Evaluation Datasets, and Traditional NLP Datasets and more?

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LangChain: Automating Large Language Model (LLM) Evaluation

Analytics Vidhya

Introduction Large Language Models (LLMs) have captivated the world with their ability to generate human-quality text, translate languages, summarize content, and answer complex questions. As LLMs become more powerful and sophisticated, so does the importance of measuring the performance of LLM-based applications.

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10 Exciting Projects on Large Language Models(LLM)

Analytics Vidhya

With advanced large […] The post 10 Exciting Projects on Large Language Models(LLM) appeared first on Analytics Vidhya. A portfolio of your projects, blog posts, and open-source contributions can set you apart from other candidates.

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Evaluating Large Language Models: A Technical Guide

Unite.AI

Large language models (LLMs) like GPT-4, Claude, and LLaMA have exploded in popularity. But how do we know if these models are actually any good? With new LLMs being announced constantly, all claiming to be bigger and better, how do we evaluate and compare their performance?

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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. NVIDIA's TensorRT-LLM steps in to address this challenge by providing a set of powerful tools and optimizations specifically designed for LLM inference.

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Deploying Large Language Models in Production: LLMOps with MLflow

Analytics Vidhya

Introduction Large Language Models (LLMs) are now widely used in a variety of applications, like machine translation, chat bots, text summarization , sentiment analysis , making advancements in the field of natural language processing (NLP).

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How to Build Reliable LLM Applications with Phidata?

Analytics Vidhya

Introduction With the intro of Large Language Models, the usage of these LLMs in different applications has greatly increased. In most of the recent applications developed across many problem statements, LLMs are part of it. appeared first on Analytics Vidhya.

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