Language Models

Industrial insight and articles from Winder.AI, focusing on the topic Language Models

Testing and Evaluating Large Language Models in AI Applications

Published
Author
Dr. Phil Winder
CEO

With the rapidly expanding use of large language models (LLMs) in downstream products, the need to ensure performance and reliability is crucial. But with random outputs and non-deterministic behaviour how do you know if you application performs, or works at all? This webinar offers a comprehensive, vendor-agnostic exploration of techniques and best practices for testing and evaluating LLMs, ensuring they meet the desired success criteria and perform effectively across varied scenarios.

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Generating Keywords Automatically With llama-cli and Phi-3

Published
Author
Dr. Phil Winder
CEO

Automate keyword generation for your Hugo blog with llama-cli and Phi-3. Learn how Dr. Phil Winder used local language models to generate SEO-friendly keywords for Winder.AI, enhancing related content linking and site performance. Get the complete script and code explanation for effortless keyword management.

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Build a Voice-Based Chatbot with OpenAI, Vocode, and ElevenLabs

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

Why might we want to make an LLM talk? The concept of having a human-like conversation with an advanced AI model is an interesting idea that has many practical applications. Voice-based models are transforming how we interact with technology, making interactions more natural and intuitive. By enabling AI to talk, we open the door to numerous practical applications, from accessibility to enhanced human-machine interactions. This guide explores how to create a voice-based chatbot using OpenAI, Vocode and ElevenLabs.

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Exploring Small Language Models

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

Large language models (LLMs) are powerful but demand significant resources, making them less ideal for smaller setups. Small language models (SLMs) are a practical, resource-efficient alternative, offering quicker deployment and easier maintenance. This article discusses the benefits and applications of SLMs, focusing on their efficiency, speed, robustness, and security in contexts where LLMs are not feasible.

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Big Data in LLMs with Retrieval-Augmented Generation (RAG)

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

Retrieval-Augmented Generation (RAG) improves Language Large Models (LLMs) by integrating external data through indexing, retrieval, and generation steps. This method allows LLMs to access up-to-date information and specific details, enhancing their applicability across various domains by providing more accurate, relevant responses and enabling real-time updates and domain-specific customization.

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