AI and Machine Learning

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🔎 Xiaomi has released an open-source model for document parsing with a size of just 0.8 billion parameters. Xiaomi-OCR-0 integrates text recognition, document structure parsing, key information extraction, and question-answering based on document content. In other words, the model’s scope goes beyond simple "transcribing text from an image"—it helps transform documents into data that can be used for further processing. According to the developers: - It scores 95.24 points on Real5-OmniDocBench. - It achieves an average of 83.2 points across five benchmarks focused on text-recognition-based question-answering. - It ranks first among models with fewer than 1 billion parameters for document parsing quality on OmniDocBench v1.6. The model weights, inference code, an interactive demo, an MCP (Model Context Protocol) service, and an "Agent Skill" have all been made publicly available. The latter two components allow document processing capabilities to be integrated into AI agent workflows. Its compact size makes the model an attractive option for deployment within your own infrastructure. However, performance on specific types of scans, languages, or complex tables should be verified ind

📕 Deep Learning with TensorFlow

Google has introduced a "sleep" phase for language models—a distinct stage during which the AI ​​consolidates new knowledge and trains itself using its own generated examples. A key challenge in continual learning is "catastrophic forgetting": as a model acquires new information, it may lose previously learned skills. Furthermore, information held within a model's context window does not automatically become part of its long-term, permanent memory. The SLEEP framework alternates between two modes: Wakefulness: The model interacts with incoming data and stores information in its short-term memory. Sleep: The intake of new data is paused. The model activates specific "Mixture-of-Experts" (MoE) memory blocks and integrates the new knowledge into their weights, minimizing interference with previously consolidated information. Once this process is complete, the short-term memory is cleared. There is also a "dreaming" phase, where random combinations of "experts" generate training examples. The system then selects the most useful of these examples to further refine the model's learning. Experimental results from the team include: - BABILong: The model can process information from sequenc

📚 An Illustrated Guide to AI Agents Artificial intelligence is entering a new phase. AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting... 🌐 Website

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⭐️ If you want to get dangerously good at generative AI, then save these 10 GitHub repos: 1️⃣ Generative AI for Beginners → https://github.com/microsoft/generative-ai-for-beginners 2️⃣ LLM Course → https://github.com/mlabonne/llm-course 3️⃣ Build a Large Language Model From Scratch → https://github.com/rasbt/LLMs-from-scratch 4️⃣ Prompt Engineering Guide → https://github.com/dair-ai/Prompt-Engineering-Guide 5️⃣ Awesome LLM Apps → https://github.com/Shubhamsaboo/awesome-llm-apps 6️⃣ OpenAI Cookbook → https://github.com/openai/openai-cookbook 7️⃣ Hands-On Large Language Models → https://github.com/HandsOnLLM/Hands-On-Large-Language-Models 8️⃣ GenAI Agents → https://github.com/NirDiamant/GenAI_Agents 9️⃣ Awesome Generative AI Guide → https://github.com/aishwaryanr/awesome-generative-ai-guide 🔟 Anthropic Courses → https://github.com/anthropics/courses

🔢 Part 7 - Deep Learning

🎬 Kandinsky 6.0 Video — create videos with sound in Full HD! An open-source lineup of models — the flagship Pro (29B) and the lightweight Lite (3B) — generates clips with synchronized speech, music, and ambient sounds. No special skills needed. 🔥 Key features: • Videos up to 5 seconds with audio • Lip-sync: character's lips move with speech • Sound quality: 44 kHz (streaming standard) • Realistic physics of movement • Resolutions: SD, HD, Full HD 📊 Performance: • Pro: clearly outperforms Kandinsky 5.0 across all criteria • Pro beats Veo 3.1 Fast in image animation • Pro beats LTX 2.5 in visual quality and speech • Lite runs on consumer GPUs (RTX 5060 Ti–5090, from 16 GB VRAM) 🛠️ For developers: Open-source under MIT license. Available on Diffusers, FastVideo, ComfyUI, SGLang, vLLM-omni. Anton Frolov, Sber: "Anyone can build their own services on top of it — free of charge and without restrictions." 🔗Hugging Face

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Pre-Chunking vs. Post-Chunking (On-Demand Chunking) This visual breaks down two common ways to chunk documents in Retrieval-Augmented Generation (RAG) systems,and when each makes sense. Pre-Chunking Documents are cleaned, split into chunks, embedded, and stored ahead of time. • Pros: Fast retrieval at query time, simpler runtime pipeline. • Cons: Rigid,changing chunk size or strategy means reprocessing the entire dataset. • Best for: Stable datasets, high-throughput apps, predictable queries. Post-Chunking / On-Demand Chunking Documents are stored whole; chunking happens after retrieval based on the user’s query. • Pros: More flexible and query-aware, often more relevant context. • Cons: Higher latency and infrastructure complexity. • Best for: Evolving content, exploratory queries, precision-focused use cases. 🔑 Takeaway: There’s no one-size-fits-all. If speed and scale matter most, pre-chunk. If adaptability and relevance are key, post-chunk. Many production systems even combine both.

🧠 Memlayer: A Smart Memory Layer for LLM Memlayer adds intelligent memory to any LLM, enabling agents to remember context and extract structured knowledge. With minimal configuration, it enables fast searching and filtering of important information. 🚀 Key Features: - Support for universal LLMs (OpenAI, Claude, etc.) - Smart memory filtering with three modes - Hybrid search using vector and graph approaches - High performance (<100 ms) and local data storage 🌐 GitHub: https://github.com/divagr18/memlayer

Do you want to understand the methods used to train LLMs? The training of large language models (LLMs) is based on various approaches that help models understand and generate text. Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities. Here are 4 common methods of training LLMs in simple language 👇 1. Causal Language Modeling Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences. Analogy: how to finish a sentence for another person by guessing the next word. 2. Masked Language Modeling Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language. Analogy: how to solve tasks with missing words. 3. Text Classification Modeling Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels. Analogy: how to sort letters into folders "Work", "Personal", or "Promotions". 4. Token Classification Modeling Assigns labels to each word or subword - for example, highlights names, places, or dates

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