Petuum’s Post

🌟 Excited to share a new milestone! 🌟 I’m thrilled to announce that I’ve earned my certification in Developing Applications with Llama 3, a cutting-edge course exploring Meta’s open-source large language models (LLMs). 🚀 During this journey, I gained hands-on experience and valuable insights, including: ✅ Understanding the Llama 3 architecture and its potential. ✅ Running Llama 3 models (7B, 13B, and 70B) on a local setup. ✅ Exploring the benefits, risks, and relevance of open-source LLMs. ✅ Building simple text-based applications powered by Llama 3. ✅ Developing solutions for private querying of personal documents. ✅ Learning the fundamentals of fine-tuning Llama 3 models. ✅ Building AI Agents. Let’s connect and explore how open-source AI can drive innovation! #AI #OpenSource #Llama3 #LLM #Agents #Innovation

Explore the exciting world of LLMs in this informative video designed for developers, to learn about different AI model options, analyze pricing structures, and delve into essential features, enabling developers to make informed decisions when integrating LLMs into applications. https://lnkd.in/d-TSKA4J

We noticed that folk were building synthetic datasets in a common way. Basically going from prompt to dataset, and iterating. So we implemented a friendly abstraction to help you learn and improve this flow. Here it is: 1️⃣ Define a system prompt for you use case 2️⃣ Generate an SFT dataset 3️⃣ Refine the prompt, model choice, and parameters 4️⃣ Review and Repeat This lets you get a solid grip on your dataset, so you can go on to training a model or building a custom distilabel pipeline.

Great 9 min. video that demystifies getting started with LLMs for developers, breaking down different AI model options, analyze pricing structures and a some provides some use cases where using LLMs can help. https://lnkd.in/gv6xZ7pv

New Post: New technique makes RAG systems much better at retrieving the right documents - https://lnkd.in/gFgbnJqD - By adding knowledge of surrounding documents to document embeddings, you can make embedding models aware of the context of their applications.Read More - #news #business #world #jobs #school #passion

🎄 🌟 Day 13 of the #PowerPlatformAdventCalendar 🌟 🎄 🎥 #RetrievalAugmentedGeneration (#RAG) is a technique that combines retrieval-based and generative models to enhance the quality of generated content. In RAG, a retrieval model first fetches relevant documents or information from a large dataset based on a query. This retrieved information is then used by a generative model to produce more accurate and contextually relevant responses. This approach leverages the strengths of both models, ensuring that the generated content is informed by real-world data, leading to more reliable and informative outputs. RAG is particularly useful in applications requiring detailed and factual responses. 🖇️ https://lnkd.in/e25aQ85y ☝️Subscribe to our channel for more tutorials and tips. 👍 Like and 🤝 share this post with your friends and colleagues. CC: Serkan Aytekin, Marius Wodtke #OpenAI #TDSendpoints #Dataverse #CopilotAgents #PowerBI #CanvasApps #AzureAI #PowerPlatform #MicrosoftDynamics #CRM #PowerApps #PowerPages #Copilot #CopilotStudio #PowerBI #PowerAutomate #Azure #Microsoft #AI #AdventCalendar #GenerativeAI #Security #Audits

Retrieval-augmented generation (RAG) is one prominent technique employed to integrate LLM into business use cases, allowing proprietary knowledge to be infused into LLM. This post assumes you already possess knowledge about RAG and you are here to improve your RAG accuracy. #llms #genai #chatgpt #aiml #llmops #rag https://lnkd.in/dP3aeQFT

Practical Course! I can felicitate the skills to build an LLM chatbot over my private knowledge base.

SMASH, a framework for evaluating output from LLM systems How do you evaluate the response from an LLM-based system, such as a chatbot? What is the criteria you use? In our work on Beagle+ (see https://lnkd.in/gfCCUmg7), we built custom tools to measure speed and cost. We built user feedback mechanisms to measure satisfaction. And a team of lawyers manually review the conversations to ensure safety and accuracy. Over the past few months we’ve been moving towards automating the entire evaluation process. We’re using some of the emerging tools in this space, such as Langsmith, Langfuse, and Langtrace. To support us in this shift, we needed a framework to help us think holistically about LLM output. An easy-to-remember mnemonic would be nice too. This is what we came up with: SMASH: Speed, Money, Accuracy, Safety, and Happiness (i.e. user satisfaction). SPEED. How long does it take to respond? If streaming, to finish the response? MONEY. How cost-efficient is the response? ACCURACY. Is the response correct? Were all facts provided in the prompt preserved? SAFETY. If someone acts on the information provided in the response, could it materially harm them? HAPPINESS. Is the user pleased with the response? The SMASH framework helps us think about and explain the quality of an LLM response. I hope it helps you too.

💥 💥 💥 LLM agents have demonstrated promise in their ability to automate computer tasks, but face challenges with multi-step reasoning and planning. Towards addressing this, researchers propose an inference-time tree search algorithm for LLM agents to explicitly perform exploration and multi-step planning in interactive web environments. It is the first tree search algorithm for LLM agents that shows effectiveness on realistic and complex web environments: on the challenging VisualWebArena benchmark, applying the search algorithm on top of a GPT-4o agent yields a 39.7% relative increase in success rate compared to the same baseline without search, setting a state-of-the-art success rate of 26.4%. On WebArena, search also yields a 28.0% relative improvement over a baseline agent, setting a competitive success rate of 19.2%. Project page 👉 https://lnkd.in/gqH-U-5Y #machinelearning #llmagents #aiagents