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AI and innovation are at the forefront of so many industries, yet we still find ourselves split on how best to adapt and leverage this technology. The latest article from Replicate touches on some cutting-edge ideas regarding autoaggression in AI. It's a thought-provoking angle that deserves some discussion. Is it better for AI to be designed with a level of aggression to drive efficiency, or should we prefer a more conservative approach focused on safety and ethics? Let's share our perspectives and engage in this crucial conversation—respectfully, of course! What are your thoughts? 🤔💬 #AI #Innovation #Debate #NaitiveAI #TechTalk #FutureOfWork https://lnkd.in/gj-ksGzr

🗞️ DevTips Weekly - Issue #2 Programmable CI/CD, Text Classification with Spring AI, 7 Programmers Habits, and Distributed Transaction Management #DevTips #Newsletter

This is another framework I have used a lot #llm #ai.

𝐃𝐞𝐦𝐲𝐬𝐭𝐢𝐟𝐲𝐢𝐧𝐠 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬: 𝐀 𝐂𝐥𝐨𝐬𝐞𝐫 𝐋𝐨𝐨𝐤 𝐚𝐭 𝐆𝐨𝐨𝐠𝐥𝐞'𝐬 𝐆𝐞𝐦𝐦𝐚! Ever wondered how transformers in language models work but found the technical details daunting? Well, you're not alone, and today's highlight is a brilliant breakdown that makes these complex systems accessible to everyone. In a recent blog post, an expert delved deep into the workings of Google’s Gemma, a state-of-the-art transformer-based large language model (LLM). What’s exciting is that the post is crafted with both casual ML enthusiasts and seasoned programmers in mind. 𝘛𝘩𝘦 𝘊𝘰𝘳𝘦 𝘊𝘰𝘯𝘤𝘦𝘱𝘵: The post explores the concept of single-step prediction—taking a prompt like “I want to move” and predicting what words might logically follow. This fundamental mechanism powers everything from sophisticated chatbots to advanced coding assistants. 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘛𝘩𝘳𝘰𝘶𝘨𝘩 𝘌𝘹𝘢𝘮𝘱𝘭𝘦: Using Gemma 2B, the author doesn't just tell, they show! Accompanied by a straightforward PyTorch implementation available through notebooks on GitHub and Colab, readers can see the unembellished code that brings the theoretical into the practical. 𝘛𝘸𝘰 𝘞𝘢𝘺𝘴 𝘵𝘰 𝘌𝘯𝘨𝘢𝘨𝘦: Implementation Focus - The main text walks readers through the necessary steps to get Gemma up and running. Expandable Insights - For those curious about the "why" behind the code, expandable sections delve into the machine learning intuitions underpinning each step. Whether you’re a developer looking to understand LLMs better or simply an AI enthusiast curious about how language prediction works, this post is a gold mine of information. Check out below full article for a comprehensive yet understandable guide to one of the most fascinating areas in machine learning today! #AI #MachineLearning #LanguageModels #PyTorch #GoogleGemma #TechnologyExplained #Coding #Innovation #TechCommunity Source: https://lnkd.in/dU246cR6

Tools Every AI Engineer Should Know: A Practical Guide https://flip.it/7mxIZy

Excited to share a new tutorial from Red Hat InstructLab - Learn how to install and fine-tune your first AI model in this comprehensive guide! #AI #DataScience

Excited to share a new tutorial from #RedHat #InstructLab - Learn how to install and fine-tune your first AI model in this comprehensive guide! #AI #DataScience

Wolff emphasizes that careful prompt engineering is worth the effort, and argues that: "as AI models get more sophisticated, precise guidance becomes MORE important, NOT LESS" I wonder if this observation is true, I haven't seen this yet but it would be great to get some insights on this. https://lnkd.in/gdQ7VirT #GenAI #PromptEngineering

Finally, a way to understand how AI language models think! OpenAI released Transformer Debugger, a tool that lets you peek inside those complex language models (like the ones that power chatbots and translators). What you can do with it: 1. See what the model focuses on: Figure out which words or parts of a sentence the AI pays the most attention to. 2. Understand how the model works: Watch how it changes its understanding of the text as it processes it. 3. Mess around and see what happens: Change things inside the model and see how it affects the results. Why this is cool: 1. Find problems: See if your AI is making unfair decisions and understand why. 2. Make your model better: Figure out why it's not learning properly and how to improve it. 3. Trust your AI: Knowing how your model makes decisions helps you use it more responsibly. Try it yourself: https://lnkd.in/gDj4myyq #ai #machinelearning #interpretability #languagemodels #llms

Exciting news! Check out our latest blog post on installing and fine-tuning your first #AI model. Learn how to kickstart your AI journey with #InstructLab's tutorial. #AI #RedHat