Christian Adib’s Post

Excited to share insights on Parameter Efficient Fine-Tuning (PEFT) in my latest article! 🚀 Discover how PEFT optimizes model training for specific tasks while conserving computational resources. Dive into techniques like Low-Rank Adoption and Quantization for streamlined fine-tuning. Read more for a peek into the future of machine learning! #PEFT #MachineLearning #Efficiency #AI 🧠💡 https://lnkd.in/gTZEBaNk

As organizations think about Generative AI at scale, the biggest challenge I see teams facing is optimizing these three aspects in tandem: - Cost - Performance - Quality The problem is that it’s impossible to optimize for each variable independently. If you want higher quality of responses, then you may need multi-turn LLM loops, which cost more and make inference slower. On the other hand, if you want cheaper inference, you may have to go with a smaller model with lower response quality, or you may have to live with “noisy neighbours” (https://lnkd.in/gTkbyNv9) which affects performance. So - how do we optimize for the three nodes of this inference triangle? You can’t optimize what you can’t measure - so as you think about moving GenAI workloads to production, the first step is to capture metrics: - request and token volumes for cost - Time-to-first-token and Tokens Per Second for performance - Evaluation metrics such as accuracy, groundedness, relevance, F1 scores etc. for quality control Typically, when there is a breakthrough, it comes in at least two of these three dimensions. For example: GPT-4-Turbo is both better quality and cheaper. The Groq LPU architecture is both cheaper and faster. It is important to have good measurements for each dimension so you can fully take advantage of GenAI breakthroughs as they come.

AI model success = 80% data preparation, 20% algorithm tweaking. This principle holds true, where the focus on data quality and preprocessing led to a 15% improvement in model performance. Data Cleaning: Reduce noise by implementing automated outlier detection, handling missing values, and normalizing data distributions. Feature Engineering: Introduce domain-specific feature generation using techniques like one-hot encoding and PCA, which led to a 10% boost in F1-score. Dataset Balancing: By employing SMOTE (Synthetic Minority Oversampling Technique), address class imbalances, resulting in more stable precision-recall curves. Without robust data preparation: Accuracy = 82%, Precision = 78%, Recall = 74% After enhanced data processing: Accuracy = 92%, Precision = 91%, Recall = 89% These steps proved that clean data, balanced datasets, and carefully engineered features are the real backbone of a high-performing model. Algorithm selection alone couldn’t bridge this gap! #AI #DataPreparation #MachineLearning #FeatureEngineering #ModelTraining #DataScience

As we push the boundaries in AI and engineering, keeping up with the latest techniques is crucial. Last week, we explored Chain of Thought (CoT) prompting. This week, let's dive into a more advanced strategy: Tree of Thought (ToT) prompting. 🔍 What is ToT Prompting? ToT prompting boosts reasoning skills by allowing models to correct errors independently while continuously accumulating knowledge. This autonomous technique explores various reasoning paths, self-assesses, and evolves, making it perfect for complex problem-solving. 🔍 Example prompt: Imagine three different experts are answering this question. All experts will write down 1 step of their thinking, then share it with the group. Then all experts will go on to the next step, etc. If any expert realizes they're wrong at any point then they leave. The question is... This iterative method ensures a well-reasoned and precise final solution.

🚀 A New Era in AI: 1-bit LLMs - Efficiency Meets Excellence 🚀 - Today, I want to share an exciting leap in AI technology that I recently explored in a groundbreaking research paper: The Era of 1-bit LLMs featuring BitNet b1.58. This work represents a paradigm shift in the way we approach large language models (LLMs). 🌟 BitNet b1.58 is a 1.58-bit Large Language Model where every parameter is ternary {-1, 0, 1}, dramatically reducing computational costs while matching the performance of full-precision (FP16/BF16) models. It opens the door to scalable, cost-effective, and energy-efficient LLMs that redefine performance standards. 💡 Key Highlights: Cost-Effectiveness: BitNet b1.58 reduces memory usage by up to 3.55x and increases inference speed by 2.71x compared to traditional LLMs. Energy Efficiency: By leveraging integer operations instead of floating-point arithmetic, it saves 71.4x energy consumption for matrix multiplications, a game-changer for sustainable AI. Performance Parity: Despite being a low-bit model, it matches or outperforms FP16 models in perplexity and zero-shot accuracy on various benchmarks. Scalability: Handles larger batch sizes and sequences with reduced latency, making it a perfect candidate for future AI hardware and edge deployments. Hardware Optimization: Introduces a new computation paradigm that calls for hardware designs tailored to 1-bit LLMs. 📊 Results Speak Louder Than Words: For models with billions of parameters, BitNet b1.58 achieves better efficiency while maintaining the same or better accuracy. For example, the 3.9B BitNet b1.58 outperforms the 3B FP16 model with 3.32x less memory and 2.4x faster inference. 🌍 Implications for the Future: Sustainability: A significant step toward greener AI by reducing the carbon footprint of large-scale models. Accessibility: Enables deployment on resource-constrained devices like mobile phones and edge devices, unlocking new possibilities for ubiquitous AI applications. 📣 In a world where AI adoption is accelerating, the ability to deliver high performance at a fraction of the cost and energy is a breakthrough. This research not only sets new benchmarks but also provides a scaling law for future LLM generations.

I really love this. Honestly the more I hear from Armand Ruiz the more I respect and admire his point of view for AI. Massive models require comparatively massive amounts of compute to train and run. Smaller models should be able to run on more hardware, allowing organizations to run on premises easier, or reap the benefits of cutting-edge technology earlier. (#quantumcomputing anyone?) Although large models are needed for research and the breakthroughs we have seen, I believe that the next steps are getting models performant enough to run effectively on a desktop.

🔍 3 Key AI/ML Mistakes to Avoid for Better Results! 🔍 In the world of machine learning, it’s easy to stumble into pitfalls that cost time and resources. Here are three key mistakes to avoid: -Ignoring Data Issues 🛠️: Investing in advanced AI/ML without fixing data quality and infrastructure problems is a waste. Start with solid data to maximize your ML efforts. -Overcomplicating Models 🤔: When tackling new use cases, a simple heuristic can often deliver 80% of the results. Keep your approach straightforward and effective. -Complex Architectures 🧩: Fancy, complex ML setups can lead to increased maintenance in the long term. Often, a simpler model can perform just as well with less hassle. Focus on smart and simple solutions to drive real impact! 💡✨ #MachineLearning #DataScience #AI #MLTips #TechSimplicity #DataQuality

“Specialized mini-minds” is what it will take for us to see real value in AI use cases across sectors and geographies.

In the rapidly evolving landscape of artificial intelligence and machine learning, ensuring consistency and reliability remains a formidable challenge. The introduction of structured randomization into decisions based on machine-learning model predictions offers a promising solution to address inherent uncertainties while maintaining efficiency. ✨What Makes Structured Randomization Revolutionary? - **Enhanced Decision-Making**: By integrating a systematic approach to randomness, organizations can mitigate the risk of overfitting and improve the adaptability of models to new data sets. - **Boost in Robustness**: Structured randomization fortifies the decision-making process against the variance brought about by unforeseen data, thereby ensuring more consistent outcomes. 🌟 Key Advantages: 1. **Improved Reliability**: This technique allows for a more reliable interpretation of predictions, providing a buffer against misinterpretations caused by minor data anomalies. 2. **Scalability**: As businesses scale their operations, the randomness integrated into structural frameworks ensures that model predictions remain robust and applicable across various scenarios. 3. **Resource Efficiency**: Maintaining high levels of efficiency helps organizations deploy computational resources smartly, reducing costs and enhancing overall productivity. 🚀 What's Next? - **Widespread Adoption**: As industries begin to embrace this innovation, we can expect a paradigm shift in the standards for model prediction accuracy. - **Ongoing Research**: Continuous advancements and research in this field will likely yield even more sophisticated methods to harness randomness strategically. As we look ahead, the convergence of structured randomization with machine-learning predictions signals a transformative era in AI where uncertainty does not hinder progress but instead, guides us towards more robust and efficient outcomes. 💡 Grateful for the bright minds illuminating these pathways! #ArtificialIntelligence #MachineLearning #Innovation #TechTrends #AI #ML

I recently worked with a team on a project to develop standards for data interoperability within Industrial Edge AI systems utilizing MQTT. We identified three common patterns and crafted guidelines for uniformly structuring message topics and payloads to help integrate predictions and insights from Edge AI systems into plain MQTT and Sparkplug networks. 1. 𝐓𝐡𝐞 𝐅𝐮𝐥𝐥𝐲-𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 - describes an AI/ML system where the data inputs and the resulting predictions are transmitted via MQTT. 2. 𝐓𝐡𝐞 𝐔𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 - refers to an AI/ML system designed to work with data that is not initially obtained through MQTT. 3. 𝐓𝐡𝐞 𝐀𝐦𝐛𝐚𝐬𝐬𝐚𝐝𝐨𝐫 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 - describes an AI/ML system that takes in data through MQTT, processes it with one or more AI/ML models, and then forwards the processed data to another system using a different protocol. Check out the whitepaper below to learn more about the project. Big shout-out to the contributors: Kudzai Manditereza - HiveMQ Seth Clark - Modzy Nathan Mellis - Modzy Bradley Munday - Modzy Brent Wassell - Oshkosh Josh Coenen - Oshkosh