How LLMs process augmented prompts with transformers. Learn RAG systems with Zain Hasan.

A cool video on how LLMs process augmented prompts using token embeddings, positional vectors, and multi-head attention. Attention is all you need to watch it now and upskill !

🚀 Why LLMs Can Make Sense of Retrieved Context One of the most fascinating things about Large Language Models (LLMs) is how they can understand and use retrieved information effectively. This magic lies in the Transformer architecture — the foundation behind modern AI. 🔍 When we feed LLMs with retrieved context (from a vector DB, RAG pipeline, or other sources), the self-attention mechanism enables the model to: Weigh the relevance of each token in the input Relate new context to what it already knows Generate responses that are coherent and contextually accurate 💡 In short, Transformers don’t just read retrieved data — they connect it meaningfully to the prompt, ensuring the output is richer and more precise. I came across this excellent explainer by DeepLearning.AI 🎥 — it beautifully breaks down why and how this happens. Highly recommended for anyone curious about the "black box" of AI! #LLM #Transformers #DeepLearning #RAG #MachineLearning #AI ---

What are the subjects covered in the UP B.Ed Entrance Exam Syllabus? General Ability: Classification, Coding-Decoding, Puzzles, Direction, Ranking, Series, Relations, Percentage, Time & Distance, Average, Compound Interest, Profit & Loss, and Ratio & Proportion; Science: Chapters from Class 11th, 12th, and graduation syllabus. 📚🎓📝💡

The aim of these principles is to help people involved in the development and management of learning programs and environments apply complexity and systems thinking ideas to how they go about this. By building upon the learnings of complexity science over the past decades - to how complex systems work and their key characteristics - we hope these principles will aid educators in working more effectively with complexity to realize better outcomes for learners. You will find on the following pages four main principles, each with three short "cards" that highlight more specific concepts. These complexity principle cards can be printed off or added to a virtual whiteboard to construct games for participants to play within challenge scenarios. Find the complete set on our site here: https://lnkd.in/emGWybkH

🚀 New write-up: Implementing GRPO’s Efficient Sampling from Scratch for ReTool🚀 If you’ve ever implemented a research paper, you know the real challenge isn’t just understanding the equations—it’s wrangling the details that never make it into the paper. For my latest experiment, I decided to: - Skip the HuggingFace trainer magic - Build an explicit, debuggable ReTool + GRPO training loop - Handle expensive multi-completion rollouts *efficiently* The unsung hero? A custom **RepeatSampler** that reuses rollouts along both rows and columns—cutting generation costs dramatically. In my Medium post, I share: - Why GRPO + ReTool can get tricky - How to debug samplers like a HuggingFace engineer - The small “gotchas” that will waste your night if you miss them - The efficiency math behind generation reuse If you’ve ever been up at 2 AM staring at tensor shapes wondering what went wrong—this one’s for you. 📖 Read the full story (with code): https://lnkd.in/gDcEYhZy 🤗 Full implementation: https://lnkd.in/g9tFg_vv **Question for you**: How do you debug your samplers or data pipelines when things get weird? #MachineLearning #LLMTraining #ReinforcementLearning #PyTorch #DataPipelines #HuggingFace #AIResearch #GRPO #ToolUse

Understanding the three key approaches to assessment (OF learning, FOR learning, and AS learning ) can transform how we evaluate and support our students. This infographic breaks down their purposes, timing, and impact, helping educators make informed choices to foster both achievement and growth.

Want to learn how to align a Vision Language Model (VLM) for reasoning using GRPO and TRL? 🌋 🧑🍳 We've got you covered!! NEW multimodal post training recipe to align a VLM using TRL in Hugging Face's Cookbook. Go to the recipe 👉https://lnkd.in/d2Pc7r5r Powered by the latest TRL v0.20 release, this recipe shows how to teach Qwen2.5-VL-3B-Instruct to reason over images 🌋

📚 Dr. Ho Cheong Lam's latest research paper, "What examples to use in instruction? An intervention study to test variation theory," dives into how the use of different types of examples impact learning outcomes. Discover insights that can reshape how you teach! https://lnkd.in/gkWEP-JD #ECE #EdUHK

Year 13 starts now. The step up is real and here is how to handle it. The step from Year 12 to 13 is often harder than the jump from GCSE to A Level. Year 12 introduced core ideas like calculus and exponentials. Year 13 assumes fluency and adds trickier problem solving and more nuanced ideas. AS tends to be one topic per question. In A Level papers the later questions blend topics. A favourite example is Question 11 on OCR 2020 Paper 1, which links circles, the discriminant, and trigonometry. It is a ten marker that catches many students out. Lesson time is limited. Teachers cannot cover every twist and combination. For the highest grades you need to be independent, identify and address your gaps, and run self directed practice. What to do now. Start revising as soon as you can. Reinforce your Year 12 knowledge and learn a couple of Year 13 topics. Keep it simple. One topic at a time. Clear checklists. Revisit what you have already covered to strengthen your foundations. Use the resources I recommended in a previous post. Do this now and the September workload will feel lighter, not heavier.

🚀 Day 134 of 160 Days of DSA Problem: Activity Selection https://lnkd.in/d2V5neKB 🔥 Today’s Learning — - A classic greedy problem where sorting is the key. - Stored activities as {start, finish} pairs. - Sorted them by finish time to ensure we can select the maximum number of non-overlapping activities. - Always picked the next activity whose start time is strictly greater than the last selected activity’s finish time. - Time Complexity: O(N log N) (sorting dominates) - Space Complexity: O(N) (for storing pairs) 📌 This is a textbook example of how sorting + greedy choice gives the optimal solution in scheduling problems. #gfg #geekstreak2025 #GECB #CodingChallenges #TechSkills #Consistency #CodingEralnGECB #160DayCodingChallenge