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Martin WelkerLLMs sind datenschutztechnisch wie Datenbanken zu behandeln.Die „Hamburger Thesen zum Datenschutz bei LLMs“ sind sehr populär, aber leider im zentralen Argument falsch — und daher möglicherweise…Sep 4, 2024Sep 4, 2024
Bargougui HaikelUnlocking the Power of LLMs: An In-Depth Exploration of Prompt Engineering and Advanced Techn…Prompt engineering is communicating with AI so that one receives the most accurate or desired results with the least amount of time or…Aug 4, 2024Aug 4, 2024
InTDS ArchivebyDr. Leon EversbergImproved RAG Document Processing With MarkdownHow to read and convert PDFs to Markdown for better RAG results with LLMsNov 19, 202412Nov 19, 202412
InTDS ArchivebyIda SilfverskiöldEconomics of Hosting Open Source LLMsLeveraging various deployment optionsNov 12, 202414Nov 12, 202414
Sebastian PetrusTop 10 RAG Frameworks Github Repos 2024Retrieval-Augmented Generation (RAG) has emerged as a powerful technique for enhancing the capabilities of large language models.Sep 4, 202411Sep 4, 202411
InGenerative AIbyKenny VaneetveldeForget LangChain, CrewAI and AutoGen — Try This Framework and Never Look BackIn the rapidly evolving field of artificial intelligence, developers are inundated with frameworks and tools promising to simplify the…Oct 21, 202440Oct 21, 202440
InGoogle Cloud - Communitybyguillaume blaquiereCloud Run GPU: Make your LLMs serverlessCloud Run is a great serverless scale-to-0 service, but with limited use cases because of limited hardware. What about if GPUs are…Aug 22, 20244Aug 22, 20244
InTDS ArchivebyHeiko HotzAutomated Prompt Engineering: The Definitive Hands-On GuideLearn how to automate prompt engineering and unlock significant performance improvements in your LLM workloadSep 4, 202413Sep 4, 202413
Agent IssueLlama 3.1 INT4 Quantization: Cut Costs by 75% Without Sacrificing Performance!This is a very important news for LLM practitioners, who have been working with large language models across various business and product…Aug 14, 20246Aug 14, 20246
InTDS ArchivebyShaw TalebiCompressing Large Language Models (LLMs)Make LLMs 10X smaller without sacrificing performanceAug 30, 20246Aug 30, 20246
Aniket HinganeAdvanced Multi-Stage, Multi-Vector Querying Using the ColBERT Approach in QdrantSmart Retrieval → Brilliant Answering → Elevating AI PerformanceJul 30, 20241Jul 30, 20241
Manan SuriA Dummy’s Guide to Word2VecEssentials of Word2Vec + Implementing Word2Vec using gensimJan 21, 20224Jan 21, 20224
Skillcate AIBERT for Dummies: State-of-the-art Model from GoogleExceeds human performance on language understanding benchmarkOct 1, 2022Oct 1, 2022
InTDS ArchivebyHan HELOIR, Ph.D. ☕️The Art of Chunking: Boosting AI Performance in RAG ArchitecturesThe Key to Effective AI-Driven RetrievalAug 18, 202416Aug 18, 202416
Sacha StorzUsing a small local LLM (llama 3.1If you have data that includes sensitive information like names or other personal details, it’s probably best not to send it to a remote…Aug 11, 2024Aug 11, 2024
M K Pavan KumarBuilding Robust LLM Applications for Production grade scale using LiteLLM.LiteLLM is an innovative proxy that simplifies the integration of various large language models (LLMs) into applications by providing a…Jul 12, 2024Jul 12, 2024
InGenerative AIbyGabriel BotsieChain of Density Prompting: A New Way to Generate Better Summaries with Generative AISummarising content is a difficult task. The post explores the:Oct 1, 2023Oct 1, 2023
InLevel Up CodingbyFareed KhanBuilding a Million-Parameter LLM from Scratch Using PythonA Step-by-Step Guide to Replicating LLaMA ArchitectureDec 7, 202335Dec 7, 202335
InLevel Up CodingbyFareed KhanBuilding LLaMA 3 From Scratch with PythonCode Your Own Billion Parameter LLMMay 28, 202419May 28, 202419
