InTowards Data SciencebyDr. Leon EversbergImproved RAG Document Processing With MarkdownHow to read and convert PDFs to Markdown for better RAG results with LLMsNov 197Nov 197
InTowards Data SciencebyIda SilfverskiöldEconomics of Hosting Open Source LLMsLeveraging various deployment optionsNov 1213Nov 1213
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 47Sep 47
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 2132Oct 2132
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 224Aug 224
InTowards Data SciencebyHeiko HotzAutomated Prompt Engineering: The Definitive Hands-On GuideLearn how to automate prompt engineering and unlock significant performance improvements in your LLM workloadSep 411Sep 411
DatadriftersLlama 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 146Aug 146
InTowards Data SciencebyShaw TalebiCompressing Large Language Models (LLMs)Make LLMs 10X smaller without sacrificing performanceAug 305Aug 305
Aniket HinganeAdvanced Multi-Stage, Multi-Vector Querying Using the ColBERT Approach in QdrantSmart Retrieval → Brilliant Answering → Elevating AI PerformanceJul 301Jul 301
Manan SuriA Dummy’s Guide to Word2VecEssentials of Word2Vec + Implementing Word2Vec using gensimJan 21, 20223Jan 21, 20223
Skillcate AIBERT for Dummies: State-of-the-art Model from GoogleExceeds human performance on language understanding benchmarkOct 1, 2022Oct 1, 2022
InTowards Data SciencebyHan HELOIR, Ph.D. ☕️The Art of Chunking: Boosting AI Performance in RAG ArchitecturesThe Key to Effective AI-Driven RetrievalAug 1816Aug 1816
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 11Aug 11
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 12Jul 12
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, 202333Dec 7, 202333
InLevel Up CodingbyFareed KhanBuilding LLaMA 3 From Scratch with PythonCode Your Own Billion Parameter LLMMay 2817May 2817
InAIGuysbyVishal RajputPrompt Engineering Is Dead: DSPy Is New Paradigm For PromptingDSPy Paradigm: Let’s program — not prompt — LLMsJun 1977Jun 1977
DatadriftersLlama 3 Powered Voice Assistant: Integrating Local RAG with Qdrant, Whisper, and LangChainVoice-enabled AI applications will forever change how we interact with technology.May 174May 174
InLevel Up CodingbyPavan BelagattiEvaluating Large Language Models: A Developer’s Guide!Large Language Models (LLMs) like GPT-4, Claude, LLama and Gemini have contributed a lot to the AI community, helping organizations build…May 14May 14
