Payam Siyari

Building a Production-Ready RAG System for Persian Language Processing I’m excited to share insights from developing an advanced Retrieval-Augmented Generation system specifically engineered for Persian/Farsi document processing, leveraging cutting-edge ML infrastructure and architectural patterns. 🔬 Technical Architecture Highlights: Advanced RAG Implementation: ∙ Self-Corrective Agentic RAG with adaptive query rewriting and iterative refinement ∙ Hybrid search architecture combining dense embeddings with SPLADE sparse representations ∙ Multi-agent orchestration enabling context-aware document interaction and general conversation modes Production Infrastructure: ∙ NVIDIA Triton Inference Server deployment on H100 GPUs with vLLM backend ∙ Milvus vector database for high-performance similarity search ∙ PostgreSQL for structured data with MinIO object storage for scalable file management ∙ Model ensemble: Qwen2.5-72B-Instruct-AWQ, DeepSeek-OCR, and sentence-transformers ML Pipeline Innovations: ∙ Automated synthetic QA pair generation using LangGraph workflows ∙ Cross-encoder reranking (ms-marco-MiniLM-L6-v2) for precision optimization ∙ Dynamic prompt management system with A/B testing capabilities and versioning ∙ Fine-tuning pipeline with quality-scored synthetic datasets Engineering Principles: ∙ Clean Architecture with dependency injection and separation of concerns ∙ Docker containerization for reproducible deployments ∙ Comprehensive observability with metrics tracking and performance monitoring This project demonstrates how to architect enterprise-grade RAG systems that combine theoretical ML advancements with practical production considerations, particularly for low-resource languages. Open to discussing technical implementation details, architectural decisions, or challenges in deploying LLMs at scale. #MLEngineering #RAG #NLP #LLMs #VectorSearch #ProductionML #AI #DeepLearning #Persian #TritonInference #CleanArchitecture

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100 million Egyptians don't speak the Arabic that AI understands. Most Arabic NLP models train on Modern Standard Arabic—a formal language nobody speaks in daily conversation. So I built something to help fix this: 4,322 Egyptian Arabic dialogue pairs, fully open-source. - Natural conversational language - 18 domain categories - Ready to use for translation, chatbots, research Dataset: https://lnkd.in/g6HE3kvN Thoughts: https://lnkd.in/g_McQdib #NLP #MachineLearning #Arabic #OpenSource

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TIL that Iran launched Ballistic missiles at The Weizmann Institute of Science in June 2025, destroying labs and years of work. I don't know how you justify targeting Research Campuses. I was looking for the best places to conduct SOTA research in computer science. I'm waiting on my Master's until these wars subside, but this is so not cool. No student or researcher should have to factor "will my university get bombed?" into their career planning. That should be a basic global consensus. For those that don't know, The Weizmann Institute of Science offers top notch MSc. Programs free of cost so that students can completely devote their time to Scientific Research and Study. I was getting excited right before this news infuriated me. To the students and researchers at Weizmann: I’m deeply sorry for your loss. Years of work should not be erased by a missile salvo. The least the rest of us can do is not look away, and keep insisting that universities must be off‑limits everywhere.

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Google Research Director Denny Zhou, who founded the LLM Reasoning Team at DeepMind, recently gave a great talk at Stanford’s CS25 class: Large Language Model Reasoning. This was one of the most intuitive talks about reasoning model training and application. He outlined new directions for training models to handle questions where answers aren’t easily verifiable. My team is currently working on an enterprise LLM project focused on using reasoning to extract relevant context and generate responses for unverifiable cases. It’s a tough but promising area, and it’s an interesting time to be building. [Link to Denny Zhou's talk: https://lnkd.in/gdJ6Mzi8]

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Reinforcement Pre-Training (Microsoft, June 2025) Paper: https://lnkd.in/eY4PCpUi Abstract: "In this work, we introduce Reinforcement Pre-Training (RPT) as a new scaling paradigm for large language models and reinforcement learning (RL). Specifically, we reframe next-token prediction as a reasoning task trained using RL, where it receives verifiable rewards for correctly predicting the next token for a given context. RPT offers a scalable method to leverage vast amounts of text data for general-purpose RL, rather than relying on domain-specific annotated answers. By incentivizing the capability of next-token reasoning, RPT significantly improves the language modeling accuracy of predicting the next tokens. Moreover, RPT provides a strong pre-trained foundation for further reinforcement fine-tuning. The scaling curves show that increased training compute consistently improves the next-token prediction accuracy. The results position RPT as an effective and promising scaling paradigm to advance language model pre-training."

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Love this direction. Designing rewards that are dense while keeping benchmarks hard to hack is the right way to push LLM evaluation forward. I also appreciate how the paper introduces example-specific rubrics — making LLM judging aligned with actual user expectations instead of vague, artificial criteria. Clear, grounded rubrics are exactly what the evaluation ecosystem needs. Highly recommend reading. 👇

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Mapping the "Identity" of Quantum States ⚛️ As a life long learner and Qiskit Advocate from a remote area of Pakistan, I’ve often found that the biggest fun in quantum is to see math LIVE! We need practical, reality-based tools to see the math in action. Today, I’m sharing my latest project: an interactive Eigen-Hunter built with Python and Streamlit. This tool bridges the gap between Linear Algebra and Quantum Logic. It’s one thing to solve an equation on paper; it’s another to see the "Click" when an abstract Matrix aligns perfectly with a physical state vector. What this research tool achieves: 🔹 Geometric Intuition: Visualizing an Eigenvector as a "Straight Path" that preserves its identity through a Quantum Gate. 🔹 Arithmetic Verification: A live(Row X Column) breakdown to demystify the transformation. 🔹 Eigenvalue (lambda) Discovery: Seeing how this "Factor" relates directly to quantum measurement results. 🔹 Research Documentation: A built-in log to track stable basis states for global collaborations. By "hunting" for these stable directions, we aren't just looking at numbers; we are finding the basis states that define how quantum computers actually process information. Check out the code and try the Pauli-X Gate challenge: Can you find the state that refuses to flip? 📂 GitHub Repository: https://lnkd.in/d8_kMM_h #QuantumComputing #Qiskit #Python #LinearAlgebra #OpenSource #QuantumResearch #WomenInStem #EigenFunctions IBM Quantum OQI – Open Quantum Institute QuLearnLabs Miguel Alfonso Martinez Qiskit Olivia Lanes Nathaniel Walker Lee Chang Xin

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