Stephen Batifol

🧠 Reflecting on: Why Graph Technology for AI Reasoning 🎙️ Speaking at DSC DACH was an incredible experience. It was a unique opportunity to dive deep into one of my favorite topics — how Graph Technology enhances AI, particularly through RAG — and engage in discussions with brilliant minds. We tackled key challenges like explainability, privacy, ethical responsibility, and knowledge management automation. 💡 Key takeaways: - Enhanced contextual understanding: Graphs create richer AI models through interconnected relationships. - Improved retrieval: Graph RAG allows AI systems to access diverse, private data sources, reducing biases and improving decision-making. - Privacy protection: By structuring and retrieving data securely, Graphs ensure sensitive information isn’t exposed. - Increased explainability: Graphs shift AI away from fully "black-box" models, offering clearer and interpretable insights. - Automated knowledge management: Graphs help integrate human domain-expertise with processes data, preserving institutional knowledge, enhancing decision-making, and driving innovation. 📖 For a deeper dive into these concepts, check out my article: Why Graph Technology for AI Reasoning (https://lnkd.in/dHHbq3NN) Looking forward to bringing more data stories to life! 🚀 #DSCDACH2024 #AI #Graphs #GenerativeAI #RAG #EthicalAI #Explainability #KnowledgeManagement

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New Blogpost: Fine-tuning LLMs for Multi-turn Conversations In this deep dive we cover: 🎛️ How to fine-tune with conversation data. 🎭 Implement loss masking to improve model performance. 🐍 Full code example fine-tuning Llama 3.1 8B on CoQA dataset. Read here: https://lnkd.in/gFxbpia6

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Deep Dive into xLSTM with Max Beck at ML Week Europe Join us at Machine Learning Week Europe for a keynote by Maximilian Beck from Johannes Kepler Universität Linz and our NXAI team. Max will talk about “xLSTM: New Architectures for Large Language Models”, sharing his latest breakthroughs in efficient and scalable AI models. 👉 Ready to explore the future of AI? Join Max and the NXAI team in Munich at Machine Learning Week! #MachineLearning #xLSTM #AI #MLWeekEurope #NXAI Peter Seeberg, Martin Szugat

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📅 On 07.11.2024, we invite you to the ScaDS.AI Dresden/Leipzig training "Big Data Processing on HPC" with Apurv Kulkarni and Wenyu Zhang. Apache Spark and Apache Flink are two typical #BigData analytics frameworks. Their APIs allow the development and testing of an application on a local workstation and later, without changing the source code of the application, distribute work to many computers when the local workstation is not sufficient anymore due to limited resources. The course focuses on the step from a local workstation to an #HPC environment and presents how the typical Big Data analysis workflow can be organized in an HPC environment. In this course participants will be introduced to running a data pipeline and data processing along with managing the configurations on the HPC environment, using Apache Flink and Apache Spark. Register here: 👉 https://lnkd.in/eYfmiMT8

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💬  Learn how to build a custom chatbot with LLMs + RAG and scalable workflows 🤖 RSVP now to join our Chief ML Engineer, Niels Bantilan, for a live workshop online covering: - Using LLMs - Building RAG system for information retrieval - Prompt engineering - Creating scalable workflows using Union.ai (powered by Flyte) Who should attend: Anyone interested in building custom chatbots with LLMs and best MLOps practices should attend. This workshop is designed to be approachable for most skill levels. Familiarity with machine learning and Python is strongly encouraged but not required. By the end of this workshop, you'll be able to build a custom chatbot using LLMs, RAG, and scalable AI workflows for large language models (LLMs). 🔗 https://lnkd.in/gnTajH3Z #llm #llms #rag

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🔔 We are excited to introduce our first series of open-source #Small Language Foundation Models, Prem 1B and Prem 1B chat. These models are available on Hugging Face under APACHE LICENSE 2.0. 📍 Our goal is to create models that excel at #RAG. Since RAG works by processing information at runtime, the main constraint is LLM size. For RAG, models don't need to be huge; they just need strong text comprehension to give accurate answers when provided with the right context. 🥊 Prem 1B was pre-trained on SlimPajama dataset. We used Lightning AI and Ray by Anyscale to orchestrate our data processing and training pipeline. Training was done on 16 H100 GPUs distributed across 2 nodes. We trained our model for 2 epochs on 1.2 T tokens. 🌐 Prem 1B is a transformer-based SLM, similar to TinyLlama, and uses Flash Attention. We explored architecture styles such as State Space models like Mamba and MoEs, but learned that data quality eventually determines everything. Prem 1B supports context length up to 8192 tokens. 💭 For Chat-Finetuning (SFT), we trained the models with a dataset supporting multi-turn conversation. We used chat template and tokenizer from #Llama3. For our alignment step, we used DPO and popular datasets like UltraFeedback, Orca DPO Pairs, OASST2 DPO Pairs. Read our release blog here: https://lnkd.in/d4ZFB2Xi

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Join us as we unpack the future of AI with Carsten Kraus in this follow-up article from Launch's #NavigatingAbroad series. Learn about the impactful role of small language models in making AI more accessible and the intricate dynamics of reinforcement learning. What implications will these technological advances have on the future landscape of AI? #AIUnpacked #FutureOfAI #LanguageModels #ReinforcementLearning #TechInnovation

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Learn how you can convert a PyTorch model, trained on the web, to a mobile-optimized format. Madhur Zanwar describes the process using a few lines of code. He also discusses his journey and how one approach didn't yield satisfactory results while trying to run it in a React Native app. #machinelearning #pytorch #computervision #edgeml #reactnative #objectdetection #yolov5

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[New on our blog] MLOps Journey: Building a Mature ML Development Process by Albin Sundqvist TL;DR → Building a great AI system takes more than creating one good model. Instead, you have to implement a workflow that enables you to iterate and continuously improve. → Data scientists often lack focus, time, or knowledge about software engineering principles. As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. → Using the following 3 principles helps you build a mature ML development process: 1. Establish a standard repository structure you can use as a scaffold for your projects. 2. Design your scripts, jobs, and pipelines to be idempotent. 3. Treat your pipelines as artifacts, not only the model or data itself. — (link to the full article in the comments) #ml #machinelearning #mlops

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🚀 Looking to streamline fine-tuning for models like Llama 3, Mistral, and Mixtral? Our latest blog shows how Anyscale simplifies LLMs fine-tuning with Ray: 🔑 Key takeaways: • Scalable fine-tuning with LLM-Forge • Model Deployment with RayLLM Read more: https://lnkd.in/gQwD4A2U

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The NeurIPS Navigator: A Behind the Scenes Look, Powered by Cerebras Inference ⚡ How do you create RAG app that turns 4,500+ research papers from the 2024 NeurIPS Conference? Dive into how we processed PDFs, created embedding chunks and stored it in on Postgres in a performant way. Created by our Fellow Isaac Tai Read more here: https://bit.ly/4fUOf8m

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How to fine-tune #LLMs on custom #datasets at Scale using Qwak and #CometML Alexandru Razvant https://lnkd.in/gP4aWM69

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The Rails 8 feature set perfectly complements SQLite's power in creating resilient, high-performance production apps, but still the question lingers: Can I really go all-in on #SQLite? Join Stephen Margheim at #RailsWorld as he illustrates how to leverage Rails and SQLite's full potential in your next venture, and when SQLite does and doesn't make sense for your application. https://lnkd.in/eaBstScy

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Generative AI isn't just about models - it's about providing these models with the freshest and most relevant context using real-time data streaming. As generative AI moves from experimental to essential, businesses are racing to solve the critical challenge of contextual intelligence. From airline customer interactions to real-time fraud detection, the ability to instantly integrate operational and analytical systems is becoming a make-or-break capability for enterprises looking to stay competitive. Discover how enterprises are building AI's real-time foundation in CIOL: https://cnfl.io/3CFgOsc

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