Discover how to deploy open-source LLMs using LLM agent frameworks, orchestration frameworks, and LLMOps platforms. Learn about serving frameworks like vLLM and Ollama, and explore LLMOps tools that enhance language model performance in production environments.
Read moreDiscover top open source LLM frameworks and orchestration tools. Explore popular LLM projects, including LangChain and LlamaIndex, for seamless integration. Learn about Python LLM libraries, LLM agent frameworks, and the best tools for LLM development and orchestration.
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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.
Read moreWatch our webinar to explore Retrieval Augmented Generation (RAG) and its integration with Large Language Models (LLMs). Learn about RAG use cases, advanced LLM architectures, and techniques to enhance AI applications. Ideal for professionals utilizing or interested in RAG and LLM-powered systems.
Read moreWhy 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.
Read moreThis presentation investigates several common production-ready architectures for RAG and discusses the pros and cons of each. At the end of this talk you will be able to help design RAG augmented LLM architectures that best fit your use case.
Read moreLarge 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.
Read moreRetrieval-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.
Read moreThis article delves into the nuances of using large language models (LLMs) with large context windows, highlighting the benefits and challenges they present, from enhancing coherence and relevance to demanding more computational resources. Learn practical strategies for prompt design, maintaining narrative coherence, and utilizing attention mechanisms effectively.
Read moreThis article discusses the concept of token counts in large language models (LLMs) and their impact. Tokens are fragments of language used for text processing, representing words, parts of words, or punctuation marks. Code walkthroughs demonstrate how to calculate token counts and examples provide insight.
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