Andrea Belli

🚀 How to Evaluate LLM Applications Large Language Models (LLMs) are powerful, but their real impact depends on how we evaluate them. The right benchmarks and evaluation methods ensure accuracy, fairness, and real-world applicability. 🔍 Key Takeaways from the Framework: ✅ Benchmarks Language Understanding & QA: TruthfulQA, MMLU, DROP Common-sense & Reasoning: ARC, HellaSwag, BIG-Bench Hard Coding: HumanEval, CodeXGLUE Conversation & Chatbots: Chatbot Arena, MT Bench, Stanford HELM ✅ Evaluation Methods Character Level: Levenshtein Distance Word Based: BLEU, ROUGE, METEOR, Perplexity Embedding Based: BERTScore, MoverScore, QAG Score Language Model Based: GPTScore, SelfCheckGPT, G-Eval, Prometheus NLP Models: NLI, BLEURT Frameworks: DeepEval, RAGAS 💡 Whether you’re building chatbots, coding assistants, or reasoning systems, choosing the right benchmark + evaluation method is crucial to measure performance and reliability. 👉 Question for you: Which evaluation method do you think gives the most real-world reflection of LLM performance traditional metrics like BLEU/ROUGE or LLM-based evaluations like GPTScore & SelfCheckGPT? #LLM #AI #MachineLearning #NLP #Evaluation #GenAI #ArtificialIntelligence

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🚀 Introducing PINNeAPPle: The Revolution in Scientific Machine Learning We are proud to develop our innovative platform that democratizes access to advanced ML techniques for scientific and engineering problems! 🎯 Working hard to launch the platform and API in early October! ✨ What makes PINNeAPPle special: 🔬 Physics-Informed Neural Networks (PINNs) – Solve complex differential equations with scientific precision 🧠 60+ Advanced Models organized into 13 main categories: 📊 AUTOENCODERS • 2D/3D Autoencoders • Dense Autoencoders • Variational Autoencoders • Koopman Autoencoders • Physics-Informed Koopman 🔬 PHYSICS-INFORMED NEURAL NETWORKS • Vanilla PINNs • PINN-LSTM • PINNsformer • Variational PINNs • Extended PINNs • Extreme Theory of Functional Connections • PIELM 🌊 NEURAL OPERATORS • DeepONet • Fourier Neural Operators • Galerkin Neural Operators • Physics-Informed Neural Operators • Universal Operator Networks 🧮 BAYESIAN MODELS • Neural ODEs • Neural SDEs • Latent ODEs • Hamiltonian Neural Networks • Neural Controlled Differential Equations • Bayesian RNNs • Deep State Space Models 🔁 RECURRENT NETWORKS • LSTM • GRU • Bidirectional LSTM/GRU • Seq2Seq RNNs • ODE-RNNs • Symplectic RNNs 🎯 TRANSFORMERS • Autoformer • Informer • FEDformer • Temporal Fusion Transformer • TimesNet • Custom Transformers 📈 TIME SERIES • ARIMA • VAR • Kalman Filters • Temporal Convolutional Networks 🕸️ GRAPH NEURAL NETWORKS • Equivariant GNNs • Spatiotemporal GNNs • Graph Neural ODEs 🌀 REDUCED ORDER MODELING • POD • Dynamic Mode Decomposition • Operator Inference • Hankel-Koopman • ROM Hybrid Models 💧 RESERVOIR COMPUTING • Echo State Networks • Extreme Learning Machines • Koopman Operators 🔲 CONVOLUTIONS • Specialized Conv1D/2D/3D for scientific data ⚡ Intuitive Interface – From data upload to final results, all in a few clicks 🎯 Use Cases: • Physical and engineering simulations • Predictive analytics in energy and utilities • Pattern detection in complex data • Industrial process optimization 📹 Check out the attached video to see how easy it is to train a PINN model for Australian electricity demand analysis – from data upload to final insights! 🚀 WANT TO BE ONE OF THE FIRST TO TEST IT? If you’re interested in accessing our pre-launch beta version and helping shape the platform’s future, leave a comment below sharing your use case or area of interest! We are selecting an exclusive group of beta testers for October. Don’t miss this opportunity! 👇 #MachineLearning #AI #PINNs #DataScience #Innovation #DeepLearning #PhysicsInformed #TechStartup #ArtificialIntelligence #ScientificComputing #LaunchingSoon #BetaTesting

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𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: 𝗧𝗵𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗹𝗶𝗻𝗸 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝘀𝗲𝗮𝗿𝗰𝗵 Large language models are powerful, but without relevant context they often produce inaccurate results. The real breakthrough comes when we combine LLMs with vector databases, which are specialized systems designed to store, index, and search vector embeddings. These embeddings capture the semantic meaning of unstructured content such as documents, images, and audio, allowing AI to retrieve information based on meaning rather than keywords. Traditional databases are designed for structured data and exact matches. Vector databases enable similarity-based search, helping AI systems understand context and return results that are relevant even when wording differs. 𝗛𝗼𝘄 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗪𝗼𝗿𝗸 Unstructured data is converted into vector embeddings using models like OpenAI, Hugging Face, or Instructor models. These vectors are stored in specialized databases and indexed using advanced algorithms such as:   • 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗡𝗮𝘃𝗶𝗴𝗮𝗯𝗹𝗲 𝗦𝗺𝗮𝗹𝗹 𝗪𝗼𝗿𝗹𝗱 (𝗛𝗡𝗦𝗪): Builds multi-layer graphs for highly efficient navigation   • 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗤𝘂𝗮𝗻𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Compresses embeddings for faster retrieval while conserving memory   • 𝗜𝗻𝘃𝗲𝗿𝘁𝗲𝗱 𝗙𝗶𝗹𝗲 𝗜𝗻𝗱𝗲𝘅 (𝗜𝗩𝗙): Clusters similar vectors to accelerate searches When a query arrives, the database locates the closest embeddings using similarity metrics like cosine similarity, Euclidean distance, or dot product and returns the most relevant results. 𝗞𝗲𝘆 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀   • Retrieval Augmented Generation to improve LLM accuracy and reduce hallucinations   • Semantic search to retrieve documents or products based on meaning instead of keywords   • Recommendations for products, videos, or personalized content   • Multimodal search for finding similar images, videos, or audio files   • Fraud detection by identifying patterns that match suspicious behaviors 𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀   • 𝗖𝗹𝗼𝘂𝗱 𝗵𝗼𝘀𝘁𝗲𝗱 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Pinecone, Weaviate, Qdrant, Milvus, Redis Vector   • 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗮𝗻𝗱 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝗹𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀: FAISS, ScaNN, Annoy   • 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: LangChain, LlamaIndex   • 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻𝘀: PostgreSQL pgvector, Elasticsearch, MongoDB 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Vector databases allow AI systems to reason with knowledge they were never trained on. They enable conversational agents to provide contextually accurate responses, enhance recommendation engines, and power intelligent multimodal search capabilities. LLMs provide reasoning. Vector databases connect knowledge. Together, they unlock the next generation of enterprise AI systems. Follow Umair Ahmad for more insights. #AI #VectorDatabases #SemanticSearch #RAG #MachineLearning #LLMOps 

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99% of people are just using LLMs. The top 1%? They’re building them. 💻🧠 Sebastian Raschka, PhD just launched something big: Master and Build Large Language Models → 👉 https://lnkd.in/dzKMZi_p I’ve read all his books. I’ve gone through every course. This one is next-level. No shortcuts. No copy-paste notebooks. You build the architecture yourself. From scratch. 💥 What you’ll learn: → Build a transformer, layer by layer → Train and fine-tune real LLMs → Deploy them into production → Understand tokenization, attention, gradients—the whole thing This is not for tourists. It’s for builders who want to understand how LLMs actually work. Line by line. Block by block. If you’re tired of surface-level AI noise, here’s the signal. This course gives you the technical foundation everyone else is skipping. I’m diving in. Let’s go deeper. 🔧📘

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We compared self-hosted and API-based LLM deployments across a range of models, including quantized variants. One of the key factors that emerged was the role of utilization in determining cost-efficiency. Even with FP8 quantization and tuned hardware, the self-hosted environments didn’t come close to matching API efficiency. The gap wasn’t due to performance—latency and throughput were strong when the systems were busy. The challenge was keeping the GPUs consistently utilized. When utilization dropped, unit costs increased significantly. The API-based models were generally stable over the 7-day window. However, we did observe some short periods of degraded performance—spikes in latency and changes in throughput. These weren’t persistent, but they highlight a potential challenge in scenarios where predictable response times are required. Overall, the testing reinforced that deployment tradeoffs depend heavily on workload characteristics. Sustained, high-throughput demand makes it easier to justify self-hosting. For more variable or bursty workloads, efficiency is harder to maintain without advanced orchestration. Full research note is available here: https://lnkd.in/eeCY2Z5R

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